problem solving skills model

What is Problem Solving? (Steps, Techniques, Examples)

By Status.net Editorial Team on May 7, 2023 — 5 minutes to read

What Is Problem Solving?

Definition and importance.

Problem solving is the process of finding solutions to obstacles or challenges you encounter in your life or work. It is a crucial skill that allows you to tackle complex situations, adapt to changes, and overcome difficulties with ease. Mastering this ability will contribute to both your personal and professional growth, leading to more successful outcomes and better decision-making.

Problem-Solving Steps

The problem-solving process typically includes the following steps:

• Identify the issue : Recognize the problem that needs to be solved.
• Analyze the situation : Examine the issue in depth, gather all relevant information, and consider any limitations or constraints that may be present.
• Generate potential solutions : Brainstorm a list of possible solutions to the issue, without immediately judging or evaluating them.
• Evaluate options : Weigh the pros and cons of each potential solution, considering factors such as feasibility, effectiveness, and potential risks.
• Select the best solution : Choose the option that best addresses the problem and aligns with your objectives.
• Implement the solution : Put the selected solution into action and monitor the results to ensure it resolves the issue.
• Review and learn : Reflect on the problem-solving process, identify any improvements or adjustments that can be made, and apply these learnings to future situations.

Defining the Problem

To start tackling a problem, first, identify and understand it. Analyzing the issue thoroughly helps to clarify its scope and nature. Ask questions to gather information and consider the problem from various angles. Some strategies to define the problem include:

• Brainstorming with others
• Asking the 5 Ws and 1 H (Who, What, When, Where, Why, and How)
• Analyzing cause and effect
• Creating a problem statement

Generating Solutions

Once the problem is clearly understood, brainstorm possible solutions. Think creatively and keep an open mind, as well as considering lessons from past experiences. Consider:

• Creating a list of potential ideas to solve the problem
• Grouping and categorizing similar solutions
• Prioritizing potential solutions based on feasibility, cost, and resources required
• Involving others to share diverse opinions and inputs

Evaluating and Selecting Solutions

Evaluate each potential solution, weighing its pros and cons. To facilitate decision-making, use techniques such as:

• SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)
• Decision-making matrices
• Pros and cons lists
• Risk assessments

After evaluating, choose the most suitable solution based on effectiveness, cost, and time constraints.

Implementing and Monitoring the Solution

Implement the chosen solution and monitor its progress. Key actions include:

• Communicating the solution to relevant parties
• Setting timelines and milestones
• Assigning tasks and responsibilities
• Monitoring the solution and making adjustments as necessary
• Evaluating the effectiveness of the solution after implementation

Utilize feedback from stakeholders and consider potential improvements. Remember that problem-solving is an ongoing process that can always be refined and enhanced.

Problem-Solving Techniques

During each step, you may find it helpful to utilize various problem-solving techniques, such as:

• Brainstorming : A free-flowing, open-minded session where ideas are generated and listed without judgment, to encourage creativity and innovative thinking.
• Root cause analysis : A method that explores the underlying causes of a problem to find the most effective solution rather than addressing superficial symptoms.
• SWOT analysis : A tool used to evaluate the strengths, weaknesses, opportunities, and threats related to a problem or decision, providing a comprehensive view of the situation.
• Mind mapping : A visual technique that uses diagrams to organize and connect ideas, helping to identify patterns, relationships, and possible solutions.

Brainstorming

When facing a problem, start by conducting a brainstorming session. Gather your team and encourage an open discussion where everyone contributes ideas, no matter how outlandish they may seem. This helps you:

• Generate a diverse range of solutions
• Encourage all team members to participate
• Foster creative thinking

When brainstorming, remember to:

• Reserve judgment until the session is over
• Encourage wild ideas
• Combine and improve upon ideas

Root Cause Analysis

For effective problem-solving, identifying the root cause of the issue at hand is crucial. Try these methods:

• 5 Whys : Ask “why” five times to get to the underlying cause.
• Fishbone Diagram : Create a diagram representing the problem and break it down into categories of potential causes.
• Pareto Analysis : Determine the few most significant causes underlying the majority of problems.

SWOT Analysis

SWOT analysis helps you examine the Strengths, Weaknesses, Opportunities, and Threats related to your problem. To perform a SWOT analysis:

• List your problem’s strengths, such as relevant resources or strong partnerships.
• Identify its weaknesses, such as knowledge gaps or limited resources.
• Explore opportunities, like trends or new technologies, that could help solve the problem.
• Recognize potential threats, like competition or regulatory barriers.

SWOT analysis aids in understanding the internal and external factors affecting the problem, which can help guide your solution.

Mind Mapping

A mind map is a visual representation of your problem and potential solutions. It enables you to organize information in a structured and intuitive manner. To create a mind map:

• Write the problem in the center of a blank page.
• Draw branches from the central problem to related sub-problems or contributing factors.
• Add more branches to represent potential solutions or further ideas.

Mind mapping allows you to visually see connections between ideas and promotes creativity in problem-solving.

Examples of Problem Solving in Various Contexts

In the business world, you might encounter problems related to finances, operations, or communication. Applying problem-solving skills in these situations could look like:

• Identifying areas of improvement in your company’s financial performance and implementing cost-saving measures
• Resolving internal conflicts among team members by listening and understanding different perspectives, then proposing and negotiating solutions
• Streamlining a process for better productivity by removing redundancies, automating tasks, or re-allocating resources

In educational contexts, problem-solving can be seen in various aspects, such as:

• Addressing a gap in students’ understanding by employing diverse teaching methods to cater to different learning styles
• Developing a strategy for successful time management to balance academic responsibilities and extracurricular activities
• Seeking resources and support to provide equal opportunities for learners with special needs or disabilities

Everyday life is full of challenges that require problem-solving skills. Some examples include:

• Overcoming a personal obstacle, such as improving your fitness level, by establishing achievable goals, measuring progress, and adjusting your approach accordingly
• Navigating a new environment or city by researching your surroundings, asking for directions, or using technology like GPS to guide you
• Dealing with a sudden change, like a change in your work schedule, by assessing the situation, identifying potential impacts, and adapting your plans to accommodate the change.
• How to Resolve Employee Conflict at Work [Steps, Tips, Examples]
• How to Write Inspiring Core Values? 5 Steps with Examples
• 30 Employee Feedback Examples (Positive & Negative)

Critical & Creative Thinking - OER & More Resources: IDEAL problem solving

• Self evaluation
• Creating goals
• Creating personal mission statement
• Creative Thinking
• Problem Solving
• IDEAL problem solving
• CRITICAL THINKING
• Critical Thinking Tips
• Logic Terms
• Logic Traps
• Free OER Textbooks
• More Thinking: OER
• Ethics - OER Textbooks
• Evidence-Based Critical Thinking
• BELIEFS & BIAS
• Limits of Perception
• Reality & Assumptions
• Stereotypes & Race
• MAKING YOUR CASE
• Argument (OER)
• Inductive Arguments
• Information Literacy: Be Savvy about your Sources
• Persuasive Speaking (OER)
• Philosophy & Thinking
• WiPhi Philosophy Project
• Browse All Guides

VM: I had to inter-library loan this item to read the original content.  This is highly cited throughout literature, so I wanted to have a good grasp on what it covered.  Here are my notes and commentary:

•  Full text From TNtech.edu: "Ideal Problem Solver, 2 ed." (c) 1984, 1993 more... less... Thanks to Center for Assessment & Improvement of Learning - Reports & Publications"
• Full text from ERIC: The IDEAL Workplace: Strategies for Improving Learning, Problem Solving, and Creativity
• Show your support: The Ideal Problem Solver: A Guide to Improving Thinking, Learning, and Creativity Second Edition

The reason you should learn the IDEAL method is so you don't need to avoid problems.  The more know about and practice problem solving, the easier it gets.  It is learnable skill. It also prompts you to look for problems and solutions instead of just doing things the same old way.

Improvement of problem solving skills.  

Model for analyzing the processes that underlie effective problem solving.

IDEAL Model for improving problem solving (Verbatim copy of Fig 2.1; p.12)

I = Identifying the problem.

D = Define and represent the problem.

E = Explore possible strategies.

A = Act on the strategies.

L = Look back and evaluate the effects of your activities.

ELABORATION:

I = Identifying that there is a problem that, once described as a problem, may be solved or improved.

D = Define and represent the problem.  Draw it instead of trying to imagine it.

E = Explore possible strategies & alternative approaches or viewpoints. 

General strategies: Break problem down into small simple problems. Working a problem backwards. Build scale model Try simulation experiment, with smaller or simpler sets.

A = Act on the strategies. Try, then reflect or recall. Actively try learning strategy.

L = Look back and evaluate the effects of your activities. Look at results of learning strategy used: Does it work to allow full recall?

"Many students make the mistake of assuming that they have "learned" adequately if the information seems to make sense as they read it in a textbook or hear it in a lecture."    (p. 23" Must  use or practice, recall, or paraphrase - in order to evaluate effectiveness of learning.  

Math: Do example problems before looking at solution to practice concepts.  Look at solution to see where you went wrong (or not). 

Don't let the test be the first time you evaluate your understanding of material

Problem identification and definition.

Proof of concept - act/look/evaluate.

To find an answer to a problem, you can dig deeper, or dig somewhere else.  

Question assumptions about limits  The old - think outside the box- strategy.

When memorizing, know what you need to remember  Definitions?  Concepts? Graphs?  Dates?  each teacher has different priorities...ask them what to focus on

Ways to solve problem of learning new information.

Techniques for improving memory.

Short term meomory

Long term memory

Remembering people's names

Studying for an essay test.

Using cues to retrieve information.  For example, you can remember IDEAL first and that will help you reconstruct the idea of how to solve problems.

Some strategies for remembering information:

Make a story full of memorable images.  

Funny obnoxious "vivid images" or "mental pictures" are more memorabl e. (Ex: random words in a list, passwords, people's names. Banana vomit haunts me.)

Rehearse over and over - over learn.   (Ex: Memorizing a phone number 867-5309 )

Rehearse words in groups - chunking. (Ex: Memorizing a part in a play, poems, pledges, short stories.)

Organize words into conceptual categories - Look for unifying relationships. (Recall, order not important. Ex: Shopping list, points in an essay.)

Look for similarities and coincidences in the words themselves. (Ex: How many words have e's, or 2 syllables, or have pun-ishing homonyms)

The feet that use the manual transmission car pedals are, from left to right: ​ C ( L eft-foot) utch , the  B( R ight-foot) ake , and the  A ccelerato ( R ight-foot)

Does order mimic alphabetical order? The manual transmission car pedals are, from left to right, the C lutch, the B rake, and the A ccelerator )   

Use Acronyms I dentify D efine ​E xplore A ct ​L ook

Acronym- easily remembered word: FACE

Acrostic- easily remembered phrase:    E very G ood B oy D eserves F udge

• Modified image source: Commons.wikimedia.org

Don't waste time studying what you already know

Image - Name Strategy:

What is unique about the person?  What is unique about their name?

Find a relationship between the two.

Other Pairing Strategies:

method of loci: arranging words to be remembered in association with familiar location or path .

Peg-word method: arranging words to be remembered in association with number order or alphabet letter order .

Strategies to comprehend new information.

more difficult than

Strategies to memorize new information.

Learning with understanding - comprehending new information.

Knowledge of CORE CONCEPTS in a field SIMPLIFIES problem solving. 

Ways to approach a problem of learning information that seems to be arbitrary:

Over-learn:  rehearse the facts until they are mastered.  2+2=4

Find relationships between images or words that are memorable: story telling, silmilarieties, vivid images, pegging, etc.

When a concept seems unclear, learn more about it.

Memory- can be of seemingly arbitrary words or numbers: ROTE (Ex. Facts and relationships) appearance

Comprehension - is understanding significance or relationships or function

Novices often forced to memorize information until they learn enough (related concepts and context) to understand it.

The mere memorization of information rarely provides useful conceptual tools that enable one to solve new problems later on. (p. 61,69)

Taking notes will not necessarily lead to effective recall prompts. How do you know when you understand material? Self-test by trying to explain material to another person.That will expose gaps in understanding.

Recall answers or solve problems out of order to be sure you know which concepts to apply and why.

Look at mistakes made as soon as possible, and learn where you went wrong.

Uses of information require more or less precision in understanding, depending on context. (A pilot must know more about an airplane than a passenger.)

Evaluation basics: evaluate factual claims look for flaws in logic question assumptions that form the basis of the argument

Correlation does not necessarily prove cause and effect.

Importance of being able to criticize ideas and generate alternatives.

Strategies for effective criticism.

Strategies for formulating creative solutions.

Finding/understanding implicit assumptions that hamper brainstorming.

Strategies for making implicit assumptions explicit.

"The uncreative mind can spot wrong answers, but it takes a creative mnd to spot wrong questions ." Emphasis added. - Anthony Jay, (p.93)

Making implicit assumptions explicit: look for inconsistencies question assumptions make predictions analyze worst case get feedback & criticism from others

Increase generation of novel ideas: break down problem into smaller parts analyze properties on a simpler level use analogies use brainstorming give it a rest, sleep on it don't be in a hurry, let ideas incubate: ​talk to others, read, keep the problem in the back of your mind try to communicate your ideas as clearly as possible, preferably in writing. attempting to write or teach an idea can function as a discovery technique

Strategies for Effective Communication

What we are trying to accomplish (goal)

Evaluating communication fro effectiveness:

Identify and Define: Have you given audience basis to understand different points of view about a topic? Different problem definitions can lead to different solutions. Did you Explore pros and cons of different strategies? Did you take Action and then Look at consequences? Did you organize your content into main points that are easy to identify and remeber?

Did you use analogies and background information to put facts into context?

Did you make sure your facts were accurate and did you avoid making assumptions?Always check for logical fallacies and inconsistencies.  Did you include information that is novel and useful, instead of just regurgitating what everyone already knows?

After you communicate, get feedback and evaluate your strategies.  Look for effects, and learn from your mistakes.  (p. 117)

Identify and Define what (problem) you want to communicate, with respect to your audience and your goals. Explore strategies for communicating your ideas.Act - based on your strategies. Look at effects.

Summaries of Useful  Attitudes and Strategies: Anybody can use the IDEAL system to improve their problem solving skills.

Related Resources:

• Teaching The IDEAL Problem-Solving Method To Diverse Learners Written by: Amy Sippl
• << Previous: Problem Solving
• Next: CRITICAL THINKING >>
• Last Updated: Jun 26, 2024 4:12 PM
• URL: https://library.fvtc.edu/Thinking

About Us • Contact Us • FVTC Terms of Service • Sitemap FVTC Mission, Vision, Values & Purposes • FVTC Privacy Statement • FVTC Library Services Accessibility Statement DISCLAIMER: Any commercial mentions on our website are for instructional purposes only. Our guides are not a substitute for professional legal or medical advice. Fox Valley Technical College • Library Services • 1825 N. Bluemound Drive • Room G113 Appleton, WI 54912-2277 • United States • (920) 735-5653 © 2024 • Fox Valley Technical College • All Rights Reserved.

The https://library.fvtc.edu/ pages are hosted by SpringShare. Springshare Privacy Policy.

McKinsey Problem Solving: Six steps to solve any problem and tell a persuasive story

The McKinsey problem solving process is a series of mindset shifts and structured approaches to thinking about and solving challenging problems. It is a useful approach for anyone working in the knowledge and information economy and needs to communicate ideas to other people.

Over the past several years of creating StrategyU, advising an undergraduates consulting group and running workshops for clients, I have found over and over again that the principles taught on this site and in this guide are a powerful way to improve the type of work and communication you do in a business setting.

When I first set out to teach these skills to the undergraduate consulting group at my alma mater, I was still working at BCG. I was spending my day building compelling presentations, yet was at a loss for how to teach these principles to the students I would talk with at night.

Through many rounds of iteration, I was able to land on a structured process and way of framing some of these principles such that people could immediately apply them to their work.

While the “official” McKinsey problem solving process is seven steps, I have outline my own spin on things – from experience at McKinsey and Boston Consulting Group. Here are six steps that will help you solve problems like a McKinsey Consultant:

Step #1: School is over, stop worrying about “what” to make and worry about the process, or the “how”

When I reflect back on my first role at McKinsey, I realize that my biggest challenge was unlearning everything I had learned over the previous 23 years. Throughout school you are asked to do specific things. For example, you are asked to write a 5 page paper on Benjamin Franklin — double spaced, 12 font and answering two or three specific questions.

In school, to be successful you follow these rules as close as you can. However, in consulting there are no rules on the “what.” Typically the problem you are asked to solve is ambiguous and complex — exactly why they hire you. In consulting, you are taught the rules around the “how” and have to then fill in the what.

The “how” can be taught and this entire site is founded on that belief. Here are some principles to get started:

Step #2: Thinking like a consultant requires a mindset shift

There are two pre-requisites to thinking like a consultant. Without these two traits you will struggle:

• A healthy obsession looking for a “better way” to do things
• Being open minded to shifting ideas and other approaches

In business school, I was sitting in one class when I noticed that all my classmates were doing the same thing — everyone was coming up with reasons why something should should not be done.

As I’ve spent more time working, I’ve realized this is a common phenomenon. The more you learn, the easier it becomes to come up with reasons to support the current state of affairs — likely driven by the status quo bias — an emotional state that favors not changing things. Even the best consultants will experience this emotion, but they are good at identifying it and pushing forward.

Key point : Creating an effective and persuasive consulting like presentation requires a comfort with uncertainty combined with a slightly delusional belief that you can figure anything out.

Step #3: Define the problem and make sure you are not solving a symptom

Before doing the work, time should be spent on defining the actual problem. Too often, people are solutions focused when they think about fixing something. Let’s say a company is struggling with profitability. Someone might define the problem as “we do not have enough growth.” This is jumping ahead to solutions — the goal may be to drive more growth, but this is not the actual issue. It is a symptom of a deeper problem.

Consider the following information:

• Costs have remained relatively constant and are actually below industry average so revenue must be the issue
• Revenue has been increasing, but at a slowing rate
• This company sells widgets and have had no slowdown on the number of units it has sold over the last five years
• However, the price per widget is actually below where it was five years ago
• There have been new entrants in the market in the last three years that have been backed by Venture Capital money and are aggressively pricing their products below costs

In a real-life project there will definitely be much more information and a team may take a full week coming up with a problem statement . Given the information above, we may come up with the following problem statement:

Problem Statement : The company is struggling to increase profitability due to decreasing prices driven by new entrants in the market. The company does not have a clear strategy to respond to the price pressure from competitors and lacks an overall product strategy to compete in this market.

Step 4: Dive in, make hypotheses and try to figure out how to “solve” the problem

Now the fun starts!

There are generally two approaches to thinking about information in a structured way and going back and forth between the two modes is what the consulting process is founded on.

First is top-down . This is what you should start with, especially for a newer “consultant.” This involves taking the problem statement and structuring an approach. This means developing multiple hypotheses — key questions you can either prove or disprove.

Given our problem statement, you may develop the following three hypotheses:

• Company X has room to improve its pricing strategy to increase profitability
• Company X can explore new market opportunities unlocked by new entrants
• Company X can explore new business models or operating models due to advances in technology

As you can see, these three statements identify different areas you can research and either prove or disprove. In a consulting team, you may have a “workstream leader” for each statement.

Once you establish the structure you you may shift to the second type of analysis: a bottom-up approach . This involves doing deep research around your problem statement, testing your hypotheses, running different analysis and continuing to ask more questions. As you do the analysis, you will begin to see different patterns that may unlock new questions, change your thinking or even confirm your existing hypotheses. You may need to tweak your hypotheses and structure as you learn new information.

A project vacillates many times between these two approaches. Here is a hypothetical timeline of a project:

Step 5: Make a slides like a consultant

The next step is taking the structure and research and turning it into a slide. When people see slides from McKinsey and BCG, they see something that is compelling and unique, but don’t really understand all the work that goes into those slides. Both companies have a healthy obsession (maybe not to some people!) with how things look, how things are structured and how they are presented.

They also don’t understand how much work is spent on telling a compelling “story.” The biggest mistake people make in the business world is mistaking showing a lot of information versus telling a compelling story. This is an easy mistake to make — especially if you are the one that did hours of analysis. It may seem important, but when it comes down to making a slide and a presentation, you end up deleting more information rather than adding. You really need to remember the following:

Data matters, but stories change hearts and minds

Here are four quick ways to improve your presentations:

Tip #1 — Format, format, format

Both McKinsey and BCG had style templates that were obsessively followed. Some key rules I like to follow:

• Make sure all text within your slide body is the same font size (harder than you would think)
• Do not go outside of the margins into the white space on the side
• All titles throughout the presentation should be 2 lines or less and stay the same font size
• Each slide should typically only make one strong point

Tip #2 — Titles are the takeaway

The title of the slide should be the key insight or takeaway and the slide area should prove the point. The below slide is an oversimplification of this:

Even in consulting, I found that people struggled with simplifying a message to one key theme per slide. If something is going to be presented live, the simpler the better. In reality, you are often giving someone presentations that they will read in depth and more information may make sense.

To go deeper, check out these 20 presentation and powerpoint tips .

Tip #3 — Have “MECE” Ideas for max persuasion

“MECE” means mutually exclusive, collectively exhaustive — meaning all points listed cover the entire range of ideas while also being unique and differentiated from each other.

An extreme example would be this:

• Slide title: There are seven continents
• Slide content: The seven continents are North America, South America, Europe, Africa Asia, Antarctica, Australia

The list of continents provides seven distinct points that when taken together are mutually exclusive and collectively exhaustive . The MECE principle is not perfect — it is more of an ideal to push your logic in the right direction. Use it to continually improve and refine your story.

Applying this to a profitability problem at the highest level would look like this:

Goal: Increase profitability

2nd level: We can increase revenue or decrease costs

3rd level: We can increase revenue by selling more or increasing prices

Each level is MECE. It is almost impossible to argue against any of this (unless you are willing to commit accounting fraud!).

Tip #4 — Leveraging the Pyramid Principle

The pyramid principle is an approach popularized by Barbara Minto and essential to the structured problem solving approach I learned at McKinsey. Learning this approach has changed the way I look at any presentation since.

Here is a rough outline of how you can think about the pyramid principle as a way to structure a presentation:

As you build a presentation, you may have three sections for each hypothesis. As you think about the overall story, the three hypothesis (and the supporting evidence) will build on each other as a “story” to answer the defined problem. There are two ways to think about doing this — using inductive or deductive reasoning:

If we go back to our profitability example from above, you would say that increasing profitability was the core issue we developed. Lets assume that through research we found that our three hypotheses were true. Given this, you may start to build a high level presentation around the following three points:

These three ideas not only are distinct but they also build on each other. Combined, they tell a story of what the company should do and how they should react. Each of these three “points” may be a separate section in the presentation followed by several pages of detailed analysis. There may also be a shorter executive summary version of 5–10 pages that gives the high level story without as much data and analysis.

Step 6: The only way to improve is to get feedback and continue to practice

Ultimately, this process is not something you will master overnight. I’ve been consulting, either working for a firm or on my own for more than 10 years and am still looking for ways to make better presentations, become more persuasive and get feedback on individual slides.

The process never ends.

The best way to improve fast is to be working on a great team . Look for people around you that do this well and ask them for feedback. The more feedback, the more iterations and more presentations you make, the better you will become. Good luck!

If you enjoyed this post, you’ll get a kick out of all the free lessons I’ve shared that go a bit deeper. Check them out here .

Do you have a toolkit for business problem solving? I created Think Like a Strategy Consultant as an online course to make the tools of strategy consultants accessible to driven professionals, executives, and consultants. This course teaches you how to synthesize information into compelling insights, structure your information in ways that help you solve problems, and develop presentations that resonate at the C-Level. Click here to learn more or if you are interested in getting started now, enroll in the self-paced version ($497) or hands-on coaching version ($997). Both versions include lifetime access and all future updates.

Share this:

• Click to share on Facebook (Opens in new window)
• Click to share on LinkedIn (Opens in new window)
• Click to share on Twitter (Opens in new window)
• Click to share on Pocket (Opens in new window)
• Click to share on WhatsApp (Opens in new window)

• For Individuals
• For Businesses
• For Universities
• For Governments
• Online Degrees
• Find your New Career
• Join for Free

Effective Problem-Solving and Decision-Making

This course is part of multiple programs. Learn more

This course is part of multiple programs

Taught in English

Some content may not be translated

Instructor: Diane Spiegel

Financial aid available

254,323 already enrolled

(6,764 reviews)

What you'll learn

Explain both the affordances and limitations associated with problem-solving and decision-making

Reflect on how mindset and personal bias influence your ability to solve problems and make decisions

Explain and discuss how organizational decisions or non-decisions impact personal development, team dynamics, and company-wide performance

Articulate how both good and bad team decisions can benefit your professional growth

Skills you'll gain

• Critical Thinking
• Decision Theory
• Decision-Making
• Problem Solving

Details to know

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

Problem-solving and effective decision-making are essential skills in today’s fast-paced and ever-changing workplace. Both require a systematic yet creative approach to address today’s business concerns. This course will teach an overarching process of how to identify problems to generate potential solutions and how to apply decision-making styles in order to implement and assess those solutions. Through this process, you will gain confidence in assessing problems accurately, selecting the appropriate decision-making approaches for the situation at hand, making team decisions, and measuring the success of the solution’s implementation. Using case studies and situations encountered by class members, you will explore proven, successful problem-solving and decision-making models and methods that can be readily transferred to workplace projects.

Upon completing this course, you will be able to: 1. Identify key terms, styles, and approaches to effective problem-solving and decision-making 2. Explain both the affordances and limitations associated with problem-solving and decision-making 3. Reflect on how mindset and personal bias influence your ability to solve problems and make decisions 4. Explain and discuss how organizational decisions or non-decisions impact personal development, team dynamics, and company-wide performance 5. Articulate how both good and bad team decisions can benefit your professional growth

Identify the Problem

Problem-solving is an essential skill in today's fast-paced and ever-changing workplace. It requires a systematic approach that incorporates effective decision-making. Throughout this course, we will learn an overarching process of identifying problems to generate potential solutions, then apply decision-making styles in order to implement and assess those solutions. In this module, we will learn to identify problems by using a root cause approach as a foundational tool. Additionally, we will address problem parameters that often occur in business situations. Throughout this course, we will utilize a case scenario that will provide specific examples to illustrate the steps in the problem-solving and decision-making process.

What's included

1 video 7 readings 1 quiz 1 discussion prompt

1 video • Total 5 minutes

• Accurately Identify the Problem • 5 minutes • Preview module

7 readings • Total 55 minutes

• Problem Solving in Today’s Workplace • 5 minutes
• Introduction: Problem-Solving and Decision-Making Process • 10 minutes
• The Problem-Solving and Decision-Making Process • 5 minutes
• Course Example: Hybrid Work Environment • 5 minutes
• Parameters • 10 minutes
• Identify the Problem • 15 minutes
• Review: Identify the Problem • 5 minutes

1 quiz • Total 30 minutes

• Module 1 Quiz • 30 minutes

1 discussion prompt • Total 30 minutes

• Benefits and Drawbacks of Problem-Solving and Decision-Making Process • 30 minutes

Generate Solutions

In the previous module, we learned how to identify the root cause of a problem. Now we will discuss how mindset and personal bias can potentially limit creativity in solving workplace challenges. We’ll review problem-solving styles and creativity enhancement approaches to generate a variety of unique solutions while addressing constraints and limited resources.

1 video 6 readings 1 quiz 1 discussion prompt

1 video • Total 4 minutes

• Generate Multiple Solutions with Various Team Perspectives • 4 minutes • Preview module

6 readings • Total 80 minutes

• Introduction • 5 minutes
• Mindset & Personal Bias • 10 minutes
• Problem Solving Styles • 20 minutes
• Generate Solutions • 30 minutes
• Generate Solutions: Hybrid Work Environment Example • 10 minutes
• Review: Generate Solutions • 5 minutes
• Module 2 Quiz • 30 minutes
• Mindset & Personal Bias • 30 minutes

Make the Decision

In the previous module, we learned how to generate a variety of creative solutions. Now we need to decide which solution is the best option. We will explore which decision-making styles lend themselves to best solve the problem given its affordances and limitations. Tips for making better decisions are outlined as well as hazards to avoid.

1 video 5 readings 1 quiz 1 discussion prompt

1 video • Total 3 minutes

• Make the Decision • 3 minutes • Preview module

5 readings • Total 55 minutes

• Decisions Making Styles • 10 minutes
• Choose a Solution • 20 minutes
• Make the Decision: Hybrid Work Environment Example • 10 minutes
• Review: Make the Decision • 10 minutes
• Module 3 Quiz • 30 minutes
• The Impact of Decisions • 30 minutes

Implement and Assess the Solution

In the previous module, we learned how to make the decision given the best information at hand. Once the decision is made, it’s time to implement and assess the chosen solution. As we get ready to implement, we are well-served to review situational variables as elements in the environment may have shifted during the decision-making process. We will also need to define the solution’s performance metrics and Key Performance Indicators (KPIs) in order to later measure or assess the solution’s impact on the organization. Anecdotal data is equally valuable as it can share the emotional impact on employees.

• Measure Success Through Data • 3 minutes • Preview module
• Implement the Solution • 30 minutes
• Assess the Solution • 10 minutes
• Review: Implement and Assess the Solution • 5 minutes
• Final Message • 5 minutes
• Module 4 Quiz • 30 minutes
• Implement & Assess the Solution • 30 minutes

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.

Recommended if you're interested in Business Essentials

University of Colorado System

Agile Leadership: Introduction to Change

Coursera Project Network

Create an FPS Weapon in Unity (Part 4 - Ammunition)

Guided Project

University of Virginia

Design Thinking for Innovation

University of California, Irvine

Project Management & Other Tools for Career Development

Specialization

Why people choose Coursera for their career

Learner reviews

Showing 3 of 6764

6,764 reviews

Reviewed on Dec 24, 2022

thanks to this course i been more enhance my skill of problem solving in my profession and using different technique to solve the problems and making the best decission making

Reviewed on May 16, 2023

The course was like a refresher to me and it was fun learning it. It was easy to understand and the videos makes it more easy to summarize the content of the topic.

Reviewed on Jun 27, 2020

It's a very effective course for people who are in leadership positions, the materials were excellent and the professor explained the topics with clear examples.

New to Business Essentials? Start here.

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions

When will i have access to the lectures and assignments.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions

How to improve your problem solving skills and build effective problem solving strategies

Design your next session with SessionLab

Join the 150,000+ facilitators 
using SessionLab.

Recommended Articles

54 great online tools for workshops and meetings, how to create an unforgettable training session in 8 simple steps, a step-by-step guide to planning a workshop.

• 18 Free Facilitation Resources We Think You’ll Love

Effective problem solving is all about using the right process and following a plan tailored to the issue at hand. Recognizing your team or organization has an issue isn’t enough to come up with effective problem solving strategies. 

To truly understand a problem and develop appropriate solutions, you will want to follow a solid process, follow the necessary problem solving steps, and bring all of your problem solving skills to the table.   We’ll forst look at what problem solving strategies you can employ with your team when looking for a way to approach the process. We’ll then discuss the problem solving skills you need to be more effective at solving problems, complete with an activity from the SessionLab library you can use to develop that skill in your team.

Let’s get to it! 

Problem solving strategies

What skills do i need to be an effective problem solver, how can i improve my problem solving skills.

Problem solving strategies are methods of approaching and facilitating the process of problem-solving with a set of techniques , actions, and processes. Different strategies are more effective if you are trying to solve broad problems such as achieving higher growth versus more focused problems like, how do we improve our customer onboarding process?

Broadly, the problem solving steps outlined above should be included in any problem solving strategy though choosing where to focus your time and what approaches should be taken is where they begin to differ. You might find that some strategies ask for the problem identification to be done prior to the session or that everything happens in the course of a one day workshop.

The key similarity is that all good problem solving strategies are structured and designed. Four hours of open discussion is never going to be as productive as a four-hour workshop designed to lead a group through a problem solving process.

Good problem solving strategies are tailored to the team, organization and problem you will be attempting to solve. Here are some example problem solving strategies you can learn from or use to get started.

Use a workshop to lead a team through a group process

Often, the first step to solving problems or organizational challenges is bringing a group together effectively. Most teams have the tools, knowledge, and expertise necessary to solve their challenges – they just need some guidance in how to use leverage those skills and a structure and format that allows people to focus their energies.

Facilitated workshops are one of the most effective ways of solving problems of any scale. By designing and planning your workshop carefully, you can tailor the approach and scope to best fit the needs of your team and organization. 

Problem solving workshop

• Creating a bespoke, tailored process
• Tackling problems of any size
• Building in-house workshop ability and encouraging their use

Workshops are an effective strategy for solving problems. By using tried and test facilitation techniques and methods, you can design and deliver a workshop that is perfectly suited to the unique variables of your organization. You may only have the capacity for a half-day workshop and so need a problem solving process to match. 

By using our session planner tool and importing methods from our library of 700+ facilitation techniques, you can create the right problem solving workshop for your team. It might be that you want to encourage creative thinking or look at things from a new angle to unblock your groups approach to problem solving. By tailoring your workshop design to the purpose, you can help ensure great results.

One of the main benefits of a workshop is the structured approach to problem solving. Not only does this mean that the workshop itself will be successful, but many of the methods and techniques will help your team improve their working processes outside of the workshop. 

We believe that workshops are one of the best tools you can use to improve the way your team works together. Start with a problem solving workshop and then see what team building, culture or design workshops can do for your organization!

Run a design sprint

Great for: 

• aligning large, multi-discipline teams
• quickly designing and testing solutions
• tackling large, complex organizational challenges and breaking them down into smaller tasks

By using design thinking principles and methods, a design sprint is a great way of identifying, prioritizing and prototyping solutions to long term challenges that can help solve major organizational problems with quick action and measurable results.

Some familiarity with design thinking is useful, though not integral, and this strategy can really help a team align if there is some discussion around which problems should be approached first. 

The stage-based structure of the design sprint is also very useful for teams new to design thinking.  The inspiration phase, where you look to competitors that have solved your problem, and the rapid prototyping and testing phases are great for introducing new concepts that will benefit a team in all their future work. 

It can be common for teams to look inward for solutions and so looking to the market for solutions you can iterate on can be very productive. Instilling an agile prototyping and testing mindset can also be great when helping teams move forwards – generating and testing solutions quickly can help save time in the long run and is also pretty exciting!

Break problems down into smaller issues

Organizational challenges and problems are often complicated and large scale in nature. Sometimes, trying to resolve such an issue in one swoop is simply unachievable or overwhelming. Try breaking down such problems into smaller issues that you can work on step by step. You may not be able to solve the problem of churning customers off the bat, but you can work with your team to identify smaller effort but high impact elements and work on those first.

This problem solving strategy can help a team generate momentum, prioritize and get some easy wins. It’s also a great strategy to employ with teams who are just beginning to learn how to approach the problem solving process. If you want some insight into a way to employ this strategy, we recommend looking at our design sprint template below!

Use guiding frameworks or try new methodologies

Some problems are best solved by introducing a major shift in perspective or by using new methodologies that encourage your team to think differently.

Props and tools such as Methodkit , which uses a card-based toolkit for facilitation, or Lego Serious Play can be great ways to engage your team and find an inclusive, democratic problem solving strategy. Remember that play and creativity are great tools for achieving change and whatever the challenge, engaging your participants can be very effective where other strategies may have failed.

LEGO Serious Play

• Improving core problem solving skills
• Thinking outside of the box
• Encouraging creative solutions

LEGO Serious Play is a problem solving methodology designed to get participants thinking differently by using 3D models and kinesthetic learning styles. By physically building LEGO models based on questions and exercises, participants are encouraged to think outside of the box and create their own responses. 

Collaborate LEGO Serious Play exercises are also used to encourage communication and build problem solving skills in a group. By using this problem solving process, you can often help different kinds of learners and personality types contribute and unblock organizational problems with creative thinking. 

Problem solving strategies like LEGO Serious Play are super effective at helping a team solve more skills-based problems such as communication between teams or a lack of creative thinking. Some problems are not suited to LEGO Serious Play and require a different problem solving strategy.

Card Decks and Method Kits

• New facilitators or non-facilitators 
• Approaching difficult subjects with a simple, creative framework
• Engaging those with varied learning styles

Card decks and method kids are great tools for those new to facilitation or for whom facilitation is not the primary role. Card decks such as the emotional culture deck can be used for complete workshops and in many cases, can be used right out of the box. Methodkit has a variety of kits designed for scenarios ranging from personal development through to personas and global challenges so you can find the right deck for your particular needs.

Having an easy to use framework that encourages creativity or a new approach can take some of the friction or planning difficulties out of the workshop process and energize a team in any setting. Simplicity is the key with these methods. By ensuring everyone on your team can get involved and engage with the process as quickly as possible can really contribute to the success of your problem solving strategy.

Source external advice

Looking to peers, experts and external facilitators can be a great way of approaching the problem solving process. Your team may not have the necessary expertise, insights of experience to tackle some issues, or you might simply benefit from a fresh perspective. Some problems may require bringing together an entire team, and coaching managers or team members individually might be the right approach. Remember that not all problems are best resolved in the same manner.

If you’re a solo entrepreneur, peer groups, coaches and mentors can also be invaluable at not only solving specific business problems, but in providing a support network for resolving future challenges. One great approach is to join a Mastermind Group and link up with like-minded individuals and all grow together. Remember that however you approach the sourcing of external advice, do so thoughtfully, respectfully and honestly. Reciprocate where you can and prepare to be surprised by just how kind and helpful your peers can be!

Mastermind Group

• Solo entrepreneurs or small teams with low capacity
• Peer learning and gaining outside expertise
• Getting multiple external points of view quickly

Problem solving in large organizations with lots of skilled team members is one thing, but how about if you work for yourself or in a very small team without the capacity to get the most from a design sprint or LEGO Serious Play session? 

A mastermind group – sometimes known as a peer advisory board – is where a group of people come together to support one another in their own goals, challenges, and businesses. Each participant comes to the group with their own purpose and the other members of the group will help them create solutions, brainstorm ideas, and support one another. 

Mastermind groups are very effective in creating an energized, supportive atmosphere that can deliver meaningful results. Learning from peers from outside of your organization or industry can really help unlock new ways of thinking and drive growth. Access to the experience and skills of your peers can be invaluable in helping fill the gaps in your own ability, particularly in young companies.

A mastermind group is a great solution for solo entrepreneurs, small teams, or for organizations that feel that external expertise or fresh perspectives will be beneficial for them. It is worth noting that Mastermind groups are often only as good as the participants and what they can bring to the group. Participants need to be committed, engaged and understand how to work in this context. 

Coaching and mentoring

• Focused learning and development
• Filling skills gaps
• Working on a range of challenges over time

Receiving advice from a business coach or building a mentor/mentee relationship can be an effective way of resolving certain challenges. The one-to-one format of most coaching and mentor relationships can really help solve the challenges those individuals are having and benefit the organization as a result.

A great mentor can be invaluable when it comes to spotting potential problems before they arise and coming to understand a mentee very well has a host of other business benefits. You might run an internal mentorship program to help develop your team’s problem solving skills and strategies or as part of a large learning and development program. External coaches can also be an important part of your problem solving strategy, filling skills gaps for your management team or helping with specific business issues. 

Now we’ve explored the problem solving process and the steps you will want to go through in order to have an effective session, let’s look at the skills you and your team need to be more effective problem solvers.

Problem solving skills are highly sought after, whatever industry or team you work in. Organizations are keen to employ people who are able to approach problems thoughtfully and find strong, realistic solutions. Whether you are a facilitator , a team leader or a developer, being an effective problem solver is a skill you’ll want to develop.

Problem solving skills form a whole suite of techniques and approaches that an individual uses to not only identify problems but to discuss them productively before then developing appropriate solutions.

Here are some of the most important problem solving skills everyone from executives to junior staff members should learn. We’ve also included an activity or exercise from the SessionLab library that can help you and your team develop that skill. 

If you’re running a workshop or training session to try and improve problem solving skills in your team, try using these methods to supercharge your process!

Active listening

Active listening is one of the most important skills anyone who works with people can possess. In short, active listening is a technique used to not only better understand what is being said by an individual, but also to be more aware of the underlying message the speaker is trying to convey. When it comes to problem solving, active listening is integral for understanding the position of every participant and to clarify the challenges, ideas and solutions they bring to the table.

Some active listening skills include:

• Paying complete attention to the speaker.
• Removing distractions.
• Avoid interruption.
• Taking the time to fully understand before preparing a rebuttal.
• Responding respectfully and appropriately.
• Demonstrate attentiveness and positivity with an open posture, making eye contact with the speaker, smiling and nodding if appropriate. Show that you are listening and encourage them to continue.
• Be aware of and respectful of feelings. Judge the situation and respond appropriately. You can disagree without being disrespectful.   
• Observe body language. 
• Paraphrase what was said in your own words, either mentally or verbally.
• Remain neutral. 
• Reflect and take a moment before responding.
• Ask deeper questions based on what is said and clarify points where necessary.   

Active Listening   #hyperisland   #skills   #active listening   #remote-friendly   This activity supports participants to reflect on a question and generate their own solutions using simple principles of active listening and peer coaching. It’s an excellent introduction to active listening but can also be used with groups that are already familiar with it. Participants work in groups of three and take turns being: “the subject”, the listener, and the observer.

Analytical skills

All problem solving models require strong analytical skills, particularly during the beginning of the process and when it comes to analyzing how solutions have performed.

Analytical skills are primarily focused on performing an effective analysis by collecting, studying and parsing data related to a problem or opportunity. 

It often involves spotting patterns, being able to see things from different perspectives and using observable facts and data to make suggestions or produce insight. 

Analytical skills are also important at every stage of the problem solving process and by having these skills, you can ensure that any ideas or solutions you create or backed up analytically and have been sufficiently thought out.

Nine Whys   #innovation   #issue analysis   #liberating structures   With breathtaking simplicity, you can rapidly clarify for individuals and a group what is essentially important in their work. You can quickly reveal when a compelling purpose is missing in a gathering and avoid moving forward without clarity. When a group discovers an unambiguous shared purpose, more freedom and more responsibility are unleashed. You have laid the foundation for spreading and scaling innovations with fidelity.

Collaboration

Trying to solve problems on your own is difficult. Being able to collaborate effectively, with a free exchange of ideas, to delegate and be a productive member of a team is hugely important to all problem solving strategies.

Remember that whatever your role, collaboration is integral, and in a problem solving process, you are all working together to find the best solution for everyone. 

Marshmallow challenge with debriefing   #teamwork   #team   #leadership   #collaboration   In eighteen minutes, teams must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one marshmallow. The marshmallow needs to be on top. The Marshmallow Challenge was developed by Tom Wujec, who has done the activity with hundreds of groups around the world. Visit the Marshmallow Challenge website for more information. This version has an extra debriefing question added with sample questions focusing on roles within the team.

Communication  

Being an effective communicator means being empathetic, clear and succinct, asking the right questions, and demonstrating active listening skills throughout any discussion or meeting. 

In a problem solving setting, you need to communicate well in order to progress through each stage of the process effectively. As a team leader, it may also fall to you to facilitate communication between parties who may not see eye to eye. Effective communication also means helping others to express themselves and be heard in a group.

Bus Trip   #feedback   #communication   #appreciation   #closing   #thiagi   #team   This is one of my favourite feedback games. I use Bus Trip at the end of a training session or a meeting, and I use it all the time. The game creates a massive amount of energy with lots of smiles, laughs, and sometimes even a teardrop or two.

Creative problem solving skills can be some of the best tools in your arsenal. Thinking creatively, being able to generate lots of ideas and come up with out of the box solutions is useful at every step of the process. 

The kinds of problems you will likely discuss in a problem solving workshop are often difficult to solve, and by approaching things in a fresh, creative manner, you can often create more innovative solutions.

Having practical creative skills is also a boon when it comes to problem solving. If you can help create quality design sketches and prototypes in record time, it can help bring a team to alignment more quickly or provide a base for further iteration.

The paper clip method   #sharing   #creativity   #warm up   #idea generation   #brainstorming   The power of brainstorming. A training for project leaders, creativity training, and to catalyse getting new solutions.

Critical thinking

Critical thinking is one of the fundamental problem solving skills you’ll want to develop when working on developing solutions. Critical thinking is the ability to analyze, rationalize and evaluate while being aware of personal bias, outlying factors and remaining open-minded.

Defining and analyzing problems without deploying critical thinking skills can mean you and your team go down the wrong path. Developing solutions to complex issues requires critical thinking too – ensuring your team considers all possibilities and rationally evaluating them. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Data analysis 

Though it shares lots of space with general analytical skills, data analysis skills are something you want to cultivate in their own right in order to be an effective problem solver.

Being good at data analysis doesn’t just mean being able to find insights from data, but also selecting the appropriate data for a given issue, interpreting it effectively and knowing how to model and present that data. Depending on the problem at hand, it might also include a working knowledge of specific data analysis tools and procedures. 

Having a solid grasp of data analysis techniques is useful if you’re leading a problem solving workshop but if you’re not an expert, don’t worry. Bring people into the group who has this skill set and help your team be more effective as a result.

Decision making

All problems need a solution and all solutions require that someone make the decision to implement them. Without strong decision making skills, teams can become bogged down in discussion and less effective as a result. 

Making decisions is a key part of the problem solving process. It’s important to remember that decision making is not restricted to the leadership team. Every staff member makes decisions every day and developing these skills ensures that your team is able to solve problems at any scale. Remember that making decisions does not mean leaping to the first solution but weighing up the options and coming to an informed, well thought out solution to any given problem that works for the whole team.

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

Dependability

Most complex organizational problems require multiple people to be involved in delivering the solution. Ensuring that the team and organization can depend on you to take the necessary actions and communicate where necessary is key to ensuring problems are solved effectively.

Being dependable also means working to deadlines and to brief. It is often a matter of creating trust in a team so that everyone can depend on one another to complete the agreed actions in the agreed time frame so that the team can move forward together. Being undependable can create problems of friction and can limit the effectiveness of your solutions so be sure to bear this in mind throughout a project. 

Team Purpose & Culture   #team   #hyperisland   #culture   #remote-friendly   This is an essential process designed to help teams define their purpose (why they exist) and their culture (how they work together to achieve that purpose). Defining these two things will help any team to be more focused and aligned. With support of tangible examples from other companies, the team members work as individuals and a group to codify the way they work together. The goal is a visual manifestation of both the purpose and culture that can be put up in the team’s work space.

Emotional intelligence

Emotional intelligence is an important skill for any successful team member, whether communicating internally or with clients or users. In the problem solving process, emotional intelligence means being attuned to how people are feeling and thinking, communicating effectively and being self-aware of what you bring to a room. 

There are often differences of opinion when working through problem solving processes, and it can be easy to let things become impassioned or combative. Developing your emotional intelligence means being empathetic to your colleagues and managing your own emotions throughout the problem and solution process. Be kind, be thoughtful and put your points across care and attention. 

Being emotionally intelligent is a skill for life and by deploying it at work, you can not only work efficiently but empathetically. Check out the emotional culture workshop template for more!

Facilitation

As we’ve clarified in our facilitation skills post, facilitation is the art of leading people through processes towards agreed-upon objectives in a manner that encourages participation, ownership, and creativity by all those involved. While facilitation is a set of interrelated skills in itself, the broad definition of facilitation can be invaluable when it comes to problem solving. Leading a team through a problem solving process is made more effective if you improve and utilize facilitation skills – whether you’re a manager, team leader or external stakeholder.

The Six Thinking Hats   #creative thinking   #meeting facilitation   #problem solving   #issue resolution   #idea generation   #conflict resolution   The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.

Flexibility 

Being flexible is a vital skill when it comes to problem solving. This does not mean immediately bowing to pressure or changing your opinion quickly: instead, being flexible is all about seeing things from new perspectives, receiving new information and factoring it into your thought process.

Flexibility is also important when it comes to rolling out solutions. It might be that other organizational projects have greater priority or require the same resources as your chosen solution. Being flexible means understanding needs and challenges across the team and being open to shifting or arranging your own schedule as necessary. Again, this does not mean immediately making way for other projects. It’s about articulating your own needs, understanding the needs of others and being able to come to a meaningful compromise.

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

Working in any group can lead to unconscious elements of groupthink or situations in which you may not wish to be entirely honest. Disagreeing with the opinions of the executive team or wishing to save the feelings of a coworker can be tricky to navigate, but being honest is absolutely vital when to comes to developing effective solutions and ensuring your voice is heard. 

Remember that being honest does not mean being brutally candid. You can deliver your honest feedback and opinions thoughtfully and without creating friction by using other skills such as emotional intelligence. 

Explore your Values   #hyperisland   #skills   #values   #remote-friendly   Your Values is an exercise for participants to explore what their most important values are. It’s done in an intuitive and rapid way to encourage participants to follow their intuitive feeling rather than over-thinking and finding the “correct” values. It is a good exercise to use to initiate reflection and dialogue around personal values.

Initiative 

The problem solving process is multi-faceted and requires different approaches at certain points of the process. Taking initiative to bring problems to the attention of the team, collect data or lead the solution creating process is always valuable. You might even roadtest your own small scale solutions or brainstorm before a session. Taking initiative is particularly effective if you have good deal of knowledge in that area or have ownership of a particular project and want to get things kickstarted.

That said, be sure to remember to honor the process and work in service of the team. If you are asked to own one part of the problem solving process and you don’t complete that task because your initiative leads you to work on something else, that’s not an effective method of solving business challenges.

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

Impartiality

A particularly useful problem solving skill for product owners or managers is the ability to remain impartial throughout much of the process. In practice, this means treating all points of view and ideas brought forward in a meeting equally and ensuring that your own areas of interest or ownership are not favored over others. 

There may be a stage in the process where a decision maker has to weigh the cost and ROI of possible solutions against the company roadmap though even then, ensuring that the decision made is based on merit and not personal opinion. 

Empathy map   #frame insights   #create   #design   #issue analysis   An empathy map is a tool to help a design team to empathize with the people they are designing for. You can make an empathy map for a group of people or for a persona. To be used after doing personas when more insights are needed.

Being a good leader means getting a team aligned, energized and focused around a common goal. In the problem solving process, strong leadership helps ensure that the process is efficient, that any conflicts are resolved and that a team is managed in the direction of success.

It’s common for managers or executives to assume this role in a problem solving workshop, though it’s important that the leader maintains impartiality and does not bulldoze the group in a particular direction. Remember that good leadership means working in service of the purpose and team and ensuring the workshop is a safe space for employees of any level to contribute. Take a look at our leadership games and activities post for more exercises and methods to help improve leadership in your organization.

Leadership Pizza   #leadership   #team   #remote-friendly   This leadership development activity offers a self-assessment framework for people to first identify what skills, attributes and attitudes they find important for effective leadership, and then assess their own development and initiate goal setting.

In the context of problem solving, mediation is important in keeping a team engaged, happy and free of conflict. When leading or facilitating a problem solving workshop, you are likely to run into differences of opinion. Depending on the nature of the problem, certain issues may be brought up that are emotive in nature. 

Being an effective mediator means helping those people on either side of such a divide are heard, listen to one another and encouraged to find common ground and a resolution. Mediating skills are useful for leaders and managers in many situations and the problem solving process is no different.

Conflict Responses   #hyperisland   #team   #issue resolution   A workshop for a team to reflect on past conflicts, and use them to generate guidelines for effective conflict handling. The workshop uses the Thomas-Killman model of conflict responses to frame a reflective discussion. Use it to open up a discussion around conflict with a team.

Planning 

Solving organizational problems is much more effective when following a process or problem solving model. Planning skills are vital in order to structure, deliver and follow-through on a problem solving workshop and ensure your solutions are intelligently deployed.

Planning skills include the ability to organize tasks and a team, plan and design the process and take into account any potential challenges. Taking the time to plan carefully can save time and frustration later in the process and is valuable for ensuring a team is positioned for success.

3 Action Steps   #hyperisland   #action   #remote-friendly   This is a small-scale strategic planning session that helps groups and individuals to take action toward a desired change. It is often used at the end of a workshop or programme. The group discusses and agrees on a vision, then creates some action steps that will lead them towards that vision. The scope of the challenge is also defined, through discussion of the helpful and harmful factors influencing the group.

Prioritization

As organisations grow, the scale and variation of problems they face multiplies. Your team or is likely to face numerous challenges in different areas and so having the skills to analyze and prioritize becomes very important, particularly for those in leadership roles.

A thorough problem solving process is likely to deliver multiple solutions and you may have several different problems you wish to solve simultaneously. Prioritization is the ability to measure the importance, value, and effectiveness of those possible solutions and choose which to enact and in what order. The process of prioritization is integral in ensuring the biggest challenges are addressed with the most impactful solutions.

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

Project management

Some problem solving skills are utilized in a workshop or ideation phases, while others come in useful when it comes to decision making. Overseeing an entire problem solving process and ensuring its success requires strong project management skills. 

While project management incorporates many of the other skills listed here, it is important to note the distinction of considering all of the factors of a project and managing them successfully. Being able to negotiate with stakeholders, manage tasks, time and people, consider costs and ROI, and tie everything together is massively helpful when going through the problem solving process. 

Record keeping

Working out meaningful solutions to organizational challenges is only one part of the process.  Thoughtfully documenting and keeping records of each problem solving step for future consultation is important in ensuring efficiency and meaningful change. 

For example, some problems may be lower priority than others but can be revisited in the future. If the team has ideated on solutions and found some are not up to the task, record those so you can rule them out and avoiding repeating work. Keeping records of the process also helps you improve and refine your problem solving model next time around!

Personal Kanban   #gamestorming   #action   #agile   #project planning   Personal Kanban is a tool for organizing your work to be more efficient and productive. It is based on agile methods and principles.

Research skills

Conducting research to support both the identification of problems and the development of appropriate solutions is important for an effective process. Knowing where to go to collect research, how to conduct research efficiently, and identifying pieces of research are relevant are all things a good researcher can do well. 

In larger groups, not everyone has to demonstrate this ability in order for a problem solving workshop to be effective. That said, having people with research skills involved in the process, particularly if they have existing area knowledge, can help ensure the solutions that are developed with data that supports their intention. Remember that being able to deliver the results of research efficiently and in a way the team can easily understand is also important. The best data in the world is only as effective as how it is delivered and interpreted.

Customer experience map   #ideation   #concepts   #research   #design   #issue analysis   #remote-friendly   Customer experience mapping is a method of documenting and visualizing the experience a customer has as they use the product or service. It also maps out their responses to their experiences. To be used when there is a solution (even in a conceptual stage) that can be analyzed.

Risk management

Managing risk is an often overlooked part of the problem solving process. Solutions are often developed with the intention of reducing exposure to risk or solving issues that create risk but sometimes, great solutions are more experimental in nature and as such, deploying them needs to be carefully considered. 

Managing risk means acknowledging that there may be risks associated with more out of the box solutions or trying new things, but that this must be measured against the possible benefits and other organizational factors. 

Be informed, get the right data and stakeholders in the room and you can appropriately factor risk into your decision making process. 

Decisions, Decisions…   #communication   #decision making   #thiagi   #action   #issue analysis   When it comes to decision-making, why are some of us more prone to take risks while others are risk-averse? One explanation might be the way the decision and options were presented.  This exercise, based on Kahneman and Tversky’s classic study , illustrates how the framing effect influences our judgement and our ability to make decisions . The participants are divided into two groups. Both groups are presented with the same problem and two alternative programs for solving them. The two programs both have the same consequences but are presented differently. The debriefing discussion examines how the framing of the program impacted the participant’s decision.

Team-building 

No single person is as good at problem solving as a team. Building an effective team and helping them come together around a common purpose is one of the most important problem solving skills, doubly so for leaders. By bringing a team together and helping them work efficiently, you pave the way for team ownership of a problem and the development of effective solutions. 

In a problem solving workshop, it can be tempting to jump right into the deep end, though taking the time to break the ice, energize the team and align them with a game or exercise will pay off over the course of the day.

Remember that you will likely go through the problem solving process multiple times over an organization’s lifespan and building a strong team culture will make future problem solving more effective. It’s also great to work with people you know, trust and have fun with. Working on team building in and out of the problem solving process is a hallmark of successful teams that can work together to solve business problems.

9 Dimensions Team Building Activity   #ice breaker   #teambuilding   #team   #remote-friendly   9 Dimensions is a powerful activity designed to build relationships and trust among team members. There are 2 variations of this icebreaker. The first version is for teams who want to get to know each other better. The second version is for teams who want to explore how they are working together as a team.

Time management 

The problem solving process is designed to lead a team from identifying a problem through to delivering a solution and evaluating its effectiveness. Without effective time management skills or timeboxing of tasks, it can be easy for a team to get bogged down or be inefficient.

By using a problem solving model and carefully designing your workshop, you can allocate time efficiently and trust that the process will deliver the results you need in a good timeframe.

Time management also comes into play when it comes to rolling out solutions, particularly those that are experimental in nature. Having a clear timeframe for implementing and evaluating solutions is vital for ensuring their success and being able to pivot if necessary.

Improving your skills at problem solving is often a career-long pursuit though there are methods you can use to make the learning process more efficient and to supercharge your problem solving skillset.

Remember that the skills you need to be a great problem solver have a large overlap with those skills you need to be effective in any role. Investing time and effort to develop your active listening or critical thinking skills is valuable in any context. Here are 7 ways to improve your problem solving skills.

Share best practices

Remember that your team is an excellent source of skills, wisdom, and techniques and that you should all take advantage of one another where possible. Best practices that one team has for solving problems, conducting research or making decisions should be shared across the organization. If you have in-house staff that have done active listening training or are data analysis pros, have them lead a training session. 

Your team is one of your best resources. Create space and internal processes for the sharing of skills so that you can all grow together. 

Ask for help and attend training

Once you’ve figured out you have a skills gap, the next step is to take action to fill that skills gap. That might be by asking your superior for training or coaching, or liaising with team members with that skill set. You might even attend specialized training for certain skills – active listening or critical thinking, for example, are business-critical skills that are regularly offered as part of a training scheme.

Whatever method you choose, remember that taking action of some description is necessary for growth. Whether that means practicing, getting help, attending training or doing some background reading, taking active steps to improve your skills is the way to go.

Learn a process 

Problem solving can be complicated, particularly when attempting to solve large problems for the first time. Using a problem solving process helps give structure to your problem solving efforts and focus on creating outcomes, rather than worrying about the format. 

Tools such as the seven-step problem solving process above are effective because not only do they feature steps that will help a team solve problems, they also develop skills along the way. Each step asks for people to engage with the process using different skills and in doing so, helps the team learn and grow together. Group processes of varying complexity and purpose can also be found in the SessionLab library of facilitation techniques . Using a tried and tested process and really help ease the learning curve for both those leading such a process, as well as those undergoing the purpose.

Effective teams make decisions about where they should and shouldn’t expend additional effort. By using a problem solving process, you can focus on the things that matter, rather than stumbling towards a solution haphazardly. 

Create a feedback loop

Some skills gaps are more obvious than others. It’s possible that your perception of your active listening skills differs from those of your colleagues. 

It’s valuable to create a system where team members can provide feedback in an ordered and friendly manner so they can all learn from one another. Only by identifying areas of improvement can you then work to improve them. 

Remember that feedback systems require oversight and consideration so that they don’t turn into a place to complain about colleagues. Design the system intelligently so that you encourage the creation of learning opportunities, rather than encouraging people to list their pet peeves.

While practice might not make perfect, it does make the problem solving process easier. If you are having trouble with critical thinking, don’t shy away from doing it. Get involved where you can and stretch those muscles as regularly as possible. 

Problem solving skills come more naturally to some than to others and that’s okay. Take opportunities to get involved and see where you can practice your skills in situations outside of a workshop context. Try collaborating in other circumstances at work or conduct data analysis on your own projects. You can often develop those skills you need for problem solving simply by doing them. Get involved!

Use expert exercises and methods

Learn from the best. Our library of 700+ facilitation techniques is full of activities and methods that help develop the skills you need to be an effective problem solver. Check out our templates to see how to approach problem solving and other organizational challenges in a structured and intelligent manner.

There is no single approach to improving problem solving skills, but by using the techniques employed by others you can learn from their example and develop processes that have seen proven results. 

Try new ways of thinking and change your mindset

Using tried and tested exercises that you know well can help deliver results, but you do run the risk of missing out on the learning opportunities offered by new approaches. As with the problem solving process, changing your mindset can remove blockages and be used to develop your problem solving skills.

Most teams have members with mixed skill sets and specialties. Mix people from different teams and share skills and different points of view. Teach your customer support team how to use design thinking methods or help your developers with conflict resolution techniques. Try switching perspectives with facilitation techniques like Flip It! or by using new problem solving methodologies or models. Give design thinking, liberating structures or lego serious play a try if you want to try a new approach. You will find that framing problems in new ways and using existing skills in new contexts can be hugely useful for personal development and improving your skillset. It’s also a lot of fun to try new things. Give it a go!

Encountering business challenges and needing to find appropriate solutions is not unique to your organization. Lots of very smart people have developed methods, theories and approaches to help develop problem solving skills and create effective solutions. Learn from them!

Books like The Art of Thinking Clearly , Think Smarter, or Thinking Fast, Thinking Slow are great places to start, though it’s also worth looking at blogs related to organizations facing similar problems to yours, or browsing for success stories. Seeing how Dropbox massively increased growth and working backward can help you see the skills or approach you might be lacking to solve that same problem. Learning from others by reading their stories or approaches can be time-consuming but ultimately rewarding.

A tired, distracted mind is not in the best position to learn new skills. It can be tempted to burn the candle at both ends and develop problem solving skills outside of work. Absolutely use your time effectively and take opportunities for self-improvement, though remember that rest is hugely important and that without letting your brain rest, you cannot be at your most effective. 

Creating distance between yourself and the problem you might be facing can also be useful. By letting an idea sit, you can find that a better one presents itself or you can develop it further. Take regular breaks when working and create a space for downtime. Remember that working smarter is preferable to working harder and that self-care is important for any effective learning or improvement process.

Want to design better group processes?

Over to you

Now we’ve explored some of the key problem solving skills and the problem solving steps necessary for an effective process, you’re ready to begin developing more effective solutions and leading problem solving workshops.

Need more inspiration? Check out our post on problem solving activities you can use when guiding a group towards a great solution in your next workshop or meeting. Have questions? Did you have a great problem solving technique you use with your team? Get in touch in the comments below. We’d love to chat!

Leave a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Effective online tools are a necessity for smooth and engaging virtual workshops and meetings. But how do you choose the right ones? Do you sometimes feel that the good old pen and paper or MS Office toolkit and email leaves you struggling to stay on top of managing and delivering your workshop? Fortunately, there are plenty of great workshop tools to make your life easier when you need to facilitate a meeting and lead workshops. In this post, we’ll share our favorite online tools you can use to make your life easier and run better workshops and meetings. In fact, there are plenty of free online workshop tools and meeting…

How does learning work? A clever 9-year-old once told me: “I know I am learning something new when I am surprised.” The science of adult learning tells us that, in order to learn new skills (which, unsurprisingly, is harder for adults to do than kids) grown-ups need to first get into a specific headspace.  In a business, this approach is often employed in a training session where employees learn new skills or work on professional development. But how do you ensure your training is effective? In this guide, we'll explore how to create an effective training session plan and run engaging training sessions. As team leader, project manager, or consultant,…

Going from a mere idea to a workshop that delivers results for your clients can feel like a daunting task. In this piece, we will shine a light on all the work behind the scenes and help you learn how to plan a workshop from start to finish. On a good day, facilitation can feel like effortless magic, but that is mostly the result of backstage work, foresight, and a lot of careful planning. Read on to learn a step-by-step approach to breaking the process of planning a workshop into small, manageable chunks.  The flow starts with the first meeting with a client to define the purposes of a workshop.…

Design your next workshop with SessionLab

Join the 150,000 facilitators using SessionLab

Sign up for free

10 Best Problem-Solving Therapy Worksheets & Activities

Cognitive science tells us that we regularly face not only well-defined problems but, importantly, many that are ill defined (Eysenck & Keane, 2015).

Sometimes, we find ourselves unable to overcome our daily problems or the inevitable (though hopefully infrequent) life traumas we face.

Problem-Solving Therapy aims to reduce the incidence and impact of mental health disorders and improve wellbeing by helping clients face life’s difficulties (Dobson, 2011).

This article introduces Problem-Solving Therapy and offers techniques, activities, and worksheets that mental health professionals can use with clients.

Before you continue, we thought you might like to download our three Positive Psychology Exercises for free . These science-based exercises explore fundamental aspects of positive psychology, including strengths, values, and self-compassion, and will give you the tools to enhance the wellbeing of your clients, students, or employees.

This Article Contains:

What is problem-solving therapy, 14 steps for problem-solving therapy, 3 best interventions and techniques, 7 activities and worksheets for your session, fascinating books on the topic, resources from positivepsychology.com, a take-home message.

Problem-Solving Therapy assumes that mental disorders arise in response to ineffective or maladaptive coping. By adopting a more realistic and optimistic view of coping, individuals can understand the role of emotions and develop actions to reduce distress and maintain mental wellbeing (Nezu & Nezu, 2009).

“Problem-solving therapy (PST) is a psychosocial intervention, generally considered to be under a cognitive-behavioral umbrella” (Nezu, Nezu, & D’Zurilla, 2013, p. ix). It aims to encourage the client to cope better with day-to-day problems and traumatic events and reduce their impact on mental and physical wellbeing.

Clinical research, counseling, and health psychology have shown PST to be highly effective in clients of all ages, ranging from children to the elderly, across multiple clinical settings, including schizophrenia, stress, and anxiety disorders (Dobson, 2011).

Can it help with depression?

PST appears particularly helpful in treating clients with depression. A recent analysis of 30 studies found that PST was an effective treatment with a similar degree of success as other successful therapies targeting depression (Cuijpers, Wit, Kleiboer, Karyotaki, & Ebert, 2020).

Other studies confirm the value of PST and its effectiveness at treating depression in multiple age groups and its capacity to combine with other therapies, including drug treatments (Dobson, 2011).

The major concepts

Effective coping varies depending on the situation, and treatment typically focuses on improving the environment and reducing emotional distress (Dobson, 2011).

PST is based on two overlapping models:

Social problem-solving model

This model focuses on solving the problem “as it occurs in the natural social environment,” combined with a general coping strategy and a method of self-control (Dobson, 2011, p. 198).

The model includes three central concepts:

• Social problem-solving
• The problem
• The solution

The model is a “self-directed cognitive-behavioral process by which an individual, couple, or group attempts to identify or discover effective solutions for specific problems encountered in everyday living” (Dobson, 2011, p. 199).

Relational problem-solving model

The theory of PST is underpinned by a relational problem-solving model, whereby stress is viewed in terms of the relationships between three factors:

• Stressful life events
• Emotional distress and wellbeing
• Problem-solving coping

Therefore, when a significant adverse life event occurs, it may require “sweeping readjustments in a person’s life” (Dobson, 2011, p. 202).

• Enhance positive problem orientation
• Decrease negative orientation
• Foster ability to apply rational problem-solving skills
• Reduce the tendency to avoid problem-solving
• Minimize the tendency to be careless and impulsive

D’Zurilla’s and Nezu’s model includes (modified from Dobson, 2011):

• Initial structuring Establish a positive therapeutic relationship that encourages optimism and explains the PST approach.
• Assessment Formally and informally assess areas of stress in the client’s life and their problem-solving strengths and weaknesses.
• Obstacles to effective problem-solving Explore typically human challenges to problem-solving, such as multitasking and the negative impact of stress. Introduce tools that can help, such as making lists, visualization, and breaking complex problems down.
• Problem orientation – fostering self-efficacy Introduce the importance of a positive problem orientation, adopting tools, such as visualization, to promote self-efficacy.
• Problem orientation – recognizing problems Help clients recognize issues as they occur and use problem checklists to ‘normalize’ the experience.
• Problem orientation – seeing problems as challenges Encourage clients to break free of harmful and restricted ways of thinking while learning how to argue from another point of view.
• Problem orientation – use and control emotions Help clients understand the role of emotions in problem-solving, including using feelings to inform the process and managing disruptive emotions (such as cognitive reframing and relaxation exercises).
• Problem orientation – stop and think Teach clients how to reduce impulsive and avoidance tendencies (visualizing a stop sign or traffic light).
• Problem definition and formulation Encourage an understanding of the nature of problems and set realistic goals and objectives.
• Generation of alternatives Work with clients to help them recognize the wide range of potential solutions to each problem (for example, brainstorming).
• Decision-making Encourage better decision-making through an improved understanding of the consequences of decisions and the value and likelihood of different outcomes.
• Solution implementation and verification Foster the client’s ability to carry out a solution plan, monitor its outcome, evaluate its effectiveness, and use self-reinforcement to increase the chance of success.
• Guided practice Encourage the application of problem-solving skills across multiple domains and future stressful problems.
• Rapid problem-solving Teach clients how to apply problem-solving questions and guidelines quickly in any given situation.

Success in PST depends on the effectiveness of its implementation; using the right approach is crucial (Dobson, 2011).

Problem-solving therapy – Baycrest

The following interventions and techniques are helpful when implementing more effective problem-solving approaches in client’s lives.

First, it is essential to consider if PST is the best approach for the client, based on the problems they present.

Is PPT appropriate?

It is vital to consider whether PST is appropriate for the client’s situation. Therapists new to the approach may require additional guidance (Nezu et al., 2013).

Therapists should consider the following questions before beginning PST with a client (modified from Nezu et al., 2013):

• Has PST proven effective in the past for the problem? For example, research has shown success with depression, generalized anxiety, back pain, Alzheimer’s disease, cancer, and supporting caregivers (Nezu et al., 2013).
• Is PST acceptable to the client?
• Is the individual experiencing a significant mental or physical health problem?

All affirmative answers suggest that PST would be a helpful technique to apply in this instance.

Five problem-solving steps

The following five steps are valuable when working with clients to help them cope with and manage their environment (modified from Dobson, 2011).

Ask the client to consider the following points (forming the acronym ADAPT) when confronted by a problem:

• Attitude Aim to adopt a positive, optimistic attitude to the problem and problem-solving process.
• Define Obtain all required facts and details of potential obstacles to define the problem.
• Alternatives Identify various alternative solutions and actions to overcome the obstacle and achieve the problem-solving goal.
• Predict Predict each alternative’s positive and negative outcomes and choose the one most likely to achieve the goal and maximize the benefits.
• Try out Once selected, try out the solution and monitor its effectiveness while engaging in self-reinforcement.

If the client is not satisfied with their solution, they can return to step ‘A’ and find a more appropriate solution.

Download 3 Free Positive Psychology Exercises (PDF)

Enhance wellbeing with these free, science-based exercises that draw on the latest insights from positive psychology.

Download 3 Free Positive Psychology Tools Pack (PDF)

By filling out your name and email address below.

Positive self-statements

When dealing with clients facing negative self-beliefs, it can be helpful for them to use positive self-statements.

Use the following (or add new) self-statements to replace harmful, negative thinking (modified from Dobson, 2011):

• I can solve this problem; I’ve tackled similar ones before.
• I can cope with this.
• I just need to take a breath and relax.
• Once I start, it will be easier.
• It’s okay to look out for myself.
• I can get help if needed.
• Other people feel the same way I do.
• I’ll take one piece of the problem at a time.
• I can keep my fears in check.
• I don’t need to please everyone.

5 Worksheets and workbooks

Problem-solving self-monitoring form.

Answering the questions in the Problem-Solving Self-Monitoring Form provides the therapist with necessary information regarding the client’s overall and specific problem-solving approaches and reactions (Dobson, 2011).

Ask the client to complete the following:

• Describe the problem you are facing.
• What is your goal?
• What have you tried so far to solve the problem?
• What was the outcome?

Reactions to Stress

It can be helpful for the client to recognize their own experiences of stress. Do they react angrily, withdraw, or give up (Dobson, 2011)?

The Reactions to Stress worksheet can be given to the client as homework to capture stressful events and their reactions. By recording how they felt, behaved, and thought, they can recognize repeating patterns.

What Are Your Unique Triggers?

Helping clients capture triggers for their stressful reactions can encourage emotional regulation.

When clients can identify triggers that may lead to a negative response, they can stop the experience or slow down their emotional reaction (Dobson, 2011).

The What Are Your Unique Triggers ? worksheet helps the client identify their triggers (e.g., conflict, relationships, physical environment, etc.).

Problem-Solving worksheet

Imagining an existing or potential problem and working through how to resolve it can be a powerful exercise for the client.

Use the Problem-Solving worksheet to state a problem and goal and consider the obstacles in the way. Then explore options for achieving the goal, along with their pros and cons, to assess the best action plan.

Getting the Facts

Clients can become better equipped to tackle problems and choose the right course of action by recognizing facts versus assumptions and gathering all the necessary information (Dobson, 2011).

Use the Getting the Facts worksheet to answer the following questions clearly and unambiguously:

• Who is involved?
• What did or did not happen, and how did it bother you?
• Where did it happen?
• When did it happen?
• Why did it happen?
• How did you respond?

2 Helpful Group Activities

While therapists can use the worksheets above in group situations, the following two interventions work particularly well with more than one person.

Generating Alternative Solutions and Better Decision-Making

A group setting can provide an ideal opportunity to share a problem and identify potential solutions arising from multiple perspectives.

Use the Generating Alternative Solutions and Better Decision-Making worksheet and ask the client to explain the situation or problem to the group and the obstacles in the way.

Once the approaches are captured and reviewed, the individual can share their decision-making process with the group if they want further feedback.

Visualization

Visualization can be performed with individuals or in a group setting to help clients solve problems in multiple ways, including (Dobson, 2011):

• Clarifying the problem by looking at it from multiple perspectives
• Rehearsing a solution in the mind to improve and get more practice
• Visualizing a ‘safe place’ for relaxation, slowing down, and stress management

Guided imagery is particularly valuable for encouraging the group to take a ‘mental vacation’ and let go of stress.

Ask the group to begin with slow, deep breathing that fills the entire diaphragm. Then ask them to visualize a favorite scene (real or imagined) that makes them feel relaxed, perhaps beside a gently flowing river, a summer meadow, or at the beach.

The more the senses are engaged, the more real the experience. Ask the group to think about what they can hear, see, touch, smell, and even taste.

Encourage them to experience the situation as fully as possible, immersing themselves and enjoying their place of safety.

Such feelings of relaxation may be able to help clients fall asleep, relieve stress, and become more ready to solve problems.

We have included three of our favorite books on the subject of Problem-Solving Therapy below.

1. Problem-Solving Therapy: A Treatment Manual – Arthur Nezu, Christine Maguth Nezu, and Thomas D’Zurilla

This is an incredibly valuable book for anyone wishing to understand the principles and practice behind PST.

Written by the co-developers of PST, the manual provides powerful toolkits to overcome cognitive overload, emotional dysregulation, and the barriers to practical problem-solving.

Find the book on Amazon .

2. Emotion-Centered Problem-Solving Therapy: Treatment Guidelines – Arthur Nezu and Christine Maguth Nezu

Another, more recent, book from the creators of PST, this text includes important advances in neuroscience underpinning the role of emotion in behavioral treatment.

Along with clinical examples, the book also includes crucial toolkits that form part of a stepped model for the application of PST.

3. Handbook of Cognitive-Behavioral Therapies – Keith Dobson and David Dozois

This is the fourth edition of a hugely popular guide to Cognitive-Behavioral Therapies and includes a valuable and insightful section on Problem-Solving Therapy.

This is an important book for students and more experienced therapists wishing to form a high-level and in-depth understanding of the tools and techniques available to Cognitive-Behavioral Therapists.

For even more tools to help strengthen your clients’ problem-solving skills, check out the following free worksheets from our blog.

• Case Formulation Worksheet This worksheet presents a four-step framework to help therapists and their clients come to a shared understanding of the client’s presenting problem.
• Understanding Your Default Problem-Solving Approach This worksheet poses a series of questions helping clients reflect on their typical cognitive, emotional, and behavioral responses to problems.
• Social Problem Solving: Step by Step This worksheet presents a streamlined template to help clients define a problem, generate possible courses of action, and evaluate the effectiveness of an implemented solution.

If you’re looking for more science-based ways to help others enhance their wellbeing, check out this signature collection of 17 validated positive psychology tools for practitioners. Use them to help others flourish and thrive.

17 Top-Rated Positive Psychology Exercises for Practitioners

Expand your arsenal and impact with these 17 Positive Psychology Exercises [PDF] , scientifically designed to promote human flourishing, meaning, and wellbeing.

Created by Experts. 100% Science-based.

While we are born problem-solvers, facing an incredibly diverse set of challenges daily, we sometimes need support.

Problem-Solving Therapy aims to reduce stress and associated mental health disorders and improve wellbeing by improving our ability to cope. PST is valuable in diverse clinical settings, ranging from depression to schizophrenia, with research suggesting it as a highly effective treatment for teaching coping strategies and reducing emotional distress.

Many PST techniques are available to help improve clients’ positive outlook on obstacles while reducing avoidance of problem situations and the tendency to be careless and impulsive.

The PST model typically assesses the client’s strengths, weaknesses, and coping strategies when facing problems before encouraging a healthy experience of and relationship with problem-solving.

Why not use this article to explore the theory behind PST and try out some of our powerful tools and interventions with your clients to help them with their decision-making, coping, and problem-solving?

We hope you enjoyed reading this article. Don’t forget to download our three Positive Psychology Exercises for free .

• Cuijpers, P., Wit, L., Kleiboer, A., Karyotaki, E., & Ebert, D. (2020). Problem-solving therapy for adult depression: An updated meta-analysis. European P sychiatry ,  48 (1), 27–37.
• Dobson, K. S. (2011). Handbook of cognitive-behavioral therapies (3rd ed.). Guilford Press.
• Dobson, K. S., & Dozois, D. J. A. (2021). Handbook of cognitive-behavioral therapies  (4th ed.). Guilford Press.
• Eysenck, M. W., & Keane, M. T. (2015). Cognitive psychology: A student’s handbook . Psychology Press.
• Nezu, A. M., & Nezu, C. M. (2009). Problem-solving therapy DVD . Retrieved September 13, 2021, from https://www.apa.org/pubs/videos/4310852
• Nezu, A. M., & Nezu, C. M. (2018). Emotion-centered problem-solving therapy: Treatment guidelines. Springer.
• Nezu, A. M., Nezu, C. M., & D’Zurilla, T. J. (2013). Problem-solving therapy: A treatment manual . Springer.

Share this article:

Article feedback

What our readers think.

Thanks for your information given, it was helpful for me something new I learned

Let us know your thoughts Cancel reply

Your email address will not be published.

Save my name, email, and website in this browser for the next time I comment.

Related articles

The Empty Chair Technique: How It Can Help Your Clients

Resolving ‘unfinished business’ is often an essential part of counseling. If left unresolved, it can contribute to depression, anxiety, and mental ill-health while damaging existing [...]

29 Best Group Therapy Activities for Supporting Adults

As humans, we are social creatures with personal histories based on the various groups that make up our lives. Childhood begins with a family of [...]

47 Free Therapy Resources to Help Kick-Start Your New Practice

Setting up a private practice in psychotherapy brings several challenges, including a considerable investment of time and money. You can reduce risks early on by [...]

Read other articles by their category

• Body & Brain (54)
• Coaching & Application (58)
• Compassion (26)
• Counseling (51)
• Emotional Intelligence (23)
• Gratitude (18)
• Grief & Bereavement (21)
• Happiness & SWB (40)
• Meaning & Values (27)
• Meditation (20)
• Mindfulness (44)
• Motivation & Goals (46)
• Optimism & Mindset (34)
• Positive CBT (30)
• Positive Communication (22)
• Positive Education (48)
• Positive Emotions (32)
• Positive Leadership (19)
• Positive Parenting (16)
• Positive Psychology (34)
• Positive Workplace (37)
• Productivity (18)
• Relationships (45)
• Resilience & Coping (39)
• Self Awareness (21)
• Self Esteem (38)
• Strengths & Virtues (32)
• Stress & Burnout Prevention (34)
• Theory & Books (46)
• Therapy Exercises (37)
• Types of Therapy (64)

Common Problem-Solving Models & How to Use Them

Problem – solving models are step-by-step processes that provide a framework for addressing challenges. Problems arise in every facet of life. From work. to home. to friends and family, problems and conflicts can make life difficult and interfere with our physical and mental well-being. Understanding how to approach problems when they arise and implementing problem-solving techniques can make the journey through a problem less onerous on ourselves and those around us.

By building a structured problem-solving process, you can begin to build muscle memory by repeatedly practicing the same approach, and eventually, you may even begin to find yourself solving complex problems . Building a problem-solving model for each of the situations where you may encounter a problem can give you a path forward, even when the most difficult of problems arise.

This article will explore the concept of problem-solving models and dive into examples of such models and how to use them. It will also outline the benefits of implementing a problem-solving model in each area of life and why these problem-solving methods can have a large impact on your overall well-being. The goal of this article is to help you identify effective problem-solving strategies and develop critical thinking to generate solutions for any problem that comes your way.

Problem-Solving Model Defined

The first step in creating a problem-solving plan is to understand what we mean when we say problem-solving models. A problem-solving model is a step-by-step process that helps a team identify and effectively solve problems that they may encounter. This problem-solving approach gives the team the muscle memory and guide to address a conflict and resolve disputes quickly and effectively.

There are common problem-solving models that many teams have implemented, but there is also the freedom to shape a method to fit the needs of a specific situation. These models often rely on various problem-solving techniques to identify the root cause of the issue and find the best solution. This article will explore some common problem-solving models as well as general problem-solving techniques to help a team engage with and solve problems effectively.

Benefits of Implementing Problem-Solving Models

Before we discuss the exact models for problem-solving, it can be helpful to discuss why problem-solving models are beneficial in the first place. There are a variety of benefits to having a plan in place when a problem arises, but a few important benefits are listed below.

Guide Posts

When a team encounters a problem and has a guide for how to approach and solve the problem, it can be a relief to know that they have a process to fall back on when the issue cannot be resolved quickly from the beginning. A problem-solving strategy will serve as a guide for the parties to know which steps to take next and how to identify the appropriate solution.

It can also clarify when the issue needs to stay within the team, and when the issue needs to be escalated to someone in a position with more authority. It can also help the entire team solve complex problems without creating an issue out of the way the team solves the problem. It gives the team a blueprint to work from and encourages them to find a good solution.

Creative Solutions That Last

When the team or family has a way to fall back on to solve a problem, it takes some of the pressure off of coming up with the process and allows the parties to focus on identifying the relevant information and coming up with various potential solutions to the issue. By using a problem-solving method, the parties can come up with different solutions and find common ground with the best solution. This can be stifled if the team is too focused on figuring out how to solve the problem.

Additionally, the solutions that the parties come up with through problem-solving tools will often address the root cause of the issue and stop the team from having to revisit the same problem over and over again. This can lead to overall productivity and well-being and help the team continue to output quality work. By encouraging collaboration and creativity, a problem-solving technique will often keep solving problems between the parties moving forward and possibly even address them before they show up.

Common Models to Use in the Problem-Solving Process

Several models can be applied to a complex problem and create possible solutions. These range from common and straightforward to creative and in-depth to identify the most effective ways to solve a problem. This section will discuss and break down the problem-solving models that are most frequently used.

Standard Problem-Solving Process

When you search for a problem-solving technique, chances are you will find the standard model for saving problems. This model identifies and uses several important steps that will often be used in other models as well, so it can be helpful to begin the model-building process with an understanding of this model as a base. Other models often draw from this process and adapt one or more of the steps to help create additional options. Each of these steps works to accomplish a specific goal in furtherance of a solution.

Define the Problem

The first step in addressing a problem is to create a clear definition of the issue at hand. This will often require the team to communicate openly and honestly to place parameters around the issue. As the team defines the problem, it will be clear what needs to be solved and what pieces of the conflict are ancillary to the major issue. It helps to find the root causes of the issue and begin a process to address that rather than the symptoms of the problem. The team can also create a problem statement, which outlines the parameters of the problem and what needs to be fixed.

In addition to open and honest communication, other techniques can help to identify the root cause and define the problem. This includes a thorough review of the processes and steps that are currently used in the task and whether any of those steps are directly or indirectly causing the problem.

This includes reviewing how tasks are done, how communication is shared, and the current partners and team members that work together to identify if any of those are part of the issue. It is also the time to identify if some of the easy fixes or new tools would solve the problem and what the impact would be.

It is also important to gain a wide understanding of the problem from all of the people involved. Many people will have opinions on what is going on, but it is also important to understand the facts over the opinions that are affecting the problem. This can also help you identify if the problem is arising from a boundary or standard that is not being met or honored. By gathering data and understanding the source of the problem, the process of solving it can begin.

Generate Solutions

The next step in the basic process is to generate possible solutions to the problem. At this step, it is less important to evaluate how each of the options will play out and how they may change the process and more important to identify solutions that could address the issue. This includes solutions that support the goals of the team and the task, and the team can also identify short and long-term solutions.

The team should work to brainstorm as many viable solutions as possible to give them the best options to consider moving forward. They cannot pick the first solution that is proposed and consider it a successful problem-solving process.

Evaluate and Select

After a few good options have been identified, the next step is to evaluate the options and pick the most viable option that also supports the goals of the team or organization. This includes looking at each of the possible solutions and determining how they would either encourage or hinder the goals and standards of the team. These should evaluated without bias toward the solution proposed or the person putting forward the solution. Additionally, the team should consider both actual outcomes that have happened in the past and predicted instances that may occur if the solution is chosen.

Each solution should be evaluated by considering if the solution would solve the current problem without causing additional issues, the willingness of the team to buy in and implement the solution, and the actual ability of the team to implement the solution.

Participation and honesty from all team members will make the process go more smoothly and ensure that the best option for everyone involved is selected. Once the team picks the option they would like to use for the specific problem, they should clearly define what the solution is and how it should be implemented. There should also be a strategy for how to evaluate the effectiveness of the solution.

Implement the Solution and Follow Up

Once a solution is chosen, a team will often assume that the work of solving problems is complete. However, the final step in the basic model is an important step to determine if the matter is resolved or if additional options are needed. After the solution has been implemented by the team, the members of the team must provide feedback and identify any potential obstacles that may have been missed in the decision-making process.

This encourages long-term solutions for the problem and helps the team to continue to move forward with their work. It also gives the team a sense of ownership and an example of how to evaluate an idea in the future.

If the solution is not working the way that it should, the team will often need to adapt the option, or they may get to the point where they scrap the option and attempt another. Solving a problem is not always a linear process, and encouraging reform and change within the process will help the team find the answer to the issues that they face.

GROW Method

Another method that is similar to the standard method is the G.R.O.W. method. This method has very similar steps to the standard method, but the catchiness of the acronym helps a team approach the problem from the same angle each time and work through the method quickly.

The first step in the method is to identify a goal, which is what the “g” stands for in “grow.” To establish a goal, the team will need to look at the issues that they are facing and identify what they would like to accomplish and solve through the problem-solving process. The team will likely participate in conversations that identify the issues that they are facing and what they need to resolve.

The next step is to establish the current reality that the group is facing. This helps them to determine where they currently are and what needs to be done to move them forward. This can help the group establish a baseline for where they started and what they would like to change.

The next step is to find any obstacles that may be blocking the group from achieving their goal. This is where the main crux of the issues that the group is facing will come out. This is also helpful in giving the group a chance to find ways around these obstacles and toward a solution.

Way Forward

After identifying the obstacles and potential ways to avoid them, the group will then need to pick the best way to move forward and approach their goal together. Here, they will need to create steps to move forward with that goal.

Divide and Conquer

Another common problem-solving method is the divide-and-conquer method. Here, instead of the entire team working through each step of the process as a large group, they split up the issue into smaller problems that can be solved and have individual members or small groups work through the smaller problems. Once each group is satisfied with the solution to the problem, they present it to the larger group to consider along with the other options.

This process can be helpful if there is a large team attempting to solve a large and complex problem. It is also beneficial because it can be used in teams with smaller, specialized teams within it because it allows each smaller group to focus on what they know best.

However, it does encourage the parties to shy away from collaboration on the overall issue, and the different solutions that each proposes may not be possible when combined and implemented.

For this reason, it is best to use this solution when approaching complex problems with large teams and the ability to combine several problem-solving methods into one.

Six Thinking Hats

The Six Thinking Hats theory is a concept designed for a team with a lot of differing conflict styles and problem-solving techniques. This method was developed to help sort through the various techniques that people may use and help a team find a solution that works for everyone involved. It helps to organize thinking and lead the conversation to the best possible solution.

Within this system, there are six different “hats” that identify with the various aspects of the decision-making process: the overall process, idea generation, intuition and emotions, values, information gathering, and caution or critical thinking. The group agrees to participate in the process by agreeing on which of the hats the group is wearing at a given moment. This helps set parameters and expectations around what the group is attempting to achieve at any moment.

This system is particularly good in a group with different conflict styles or where people have a hard time collecting and organizing their thoughts. It can be incredibly beneficial for complex problems with many moving parts. It can also help groups identify how each of the smaller sections relates to the big picture and help create new ideas to answer the overall problem.

However, it can derail if the group focuses too heavily or for too long on one of the “hats.” The group should ensure that they have a facilitator to guide them through the process and ensure that each idea and section is considered adequately.

Trial and Error

The trial and error process takes over the evaluation and selection process and instead chooses to try out each of the alternatives to determine what the best option would be. It allows the team to gather data on each of the options and how they apply practically. It also provides the ability for the team to have an example of each possible answer to help a decision-maker determine what the best option is.

Problem-solving methods that focus on trial and error can be helpful when a team has a simple problem or a lot of time to test potential solutions, gather data, and determine an answer to the issue.

It can also be helpful when the team has a sense of the best guess for a solution but wants to test it out to determine if the data supports that option, or if they have several viable options and would like to identify the best one. However, it can be incredibly time-consuming to test each of the options and evaluate how they went. Time can often be saved by evaluating each option and selecting the best to test.

Other Problem-Solving Skills

In addition to the methods outlined above, other problem-solving skills can be used regardless of the model that is used. These techniques can round out the problem-solving process and help address either specific steps in the overall method or alter the step in some way to help it fit a specific situation.

Ask Good Questions

One of the best ways to work through any of the problem-solving models is to ask good questions. This will help the group find the issue at the heart of the problem and address that issue rather than the symptoms. The best questions will also help the group find viable solutions and pick the solution that the group can use to move forward. The more creative the questions , the more likely that they will produce innovative solutions.

Take a Step Back

Occasionally, paying attention to a problem too much can give the group tunnel vision and harm the overall processes that the group is using. Other times, the focus can lead to escalations in conflict. When this happens, it can be helpful to set aside the problem and give the group time to calm down. Once they have a chance to reconsider the options and how they apply, they can approach the issue with a new sense of purpose and determination. This can lead to additional creative solutions that may help the group find a new way forward.

Final Thoughts

Problem-solving can be a daunting part of life. However, with a good problem-solving method and the right techniques, problems can be addressed well and quickly. Applying some of these options outlined in this article can give you a head start in solving your next problem and any others that arise.

To learn more about problem-solving models, problem-solving activities, and more, contact ADR Times !

Must-read Articles:

• Recent Posts

• Dispute Resolved Customer Disagrees: Explained - June 24, 2024
• Coercive Power: How it Impacts Your Employees - June 13, 2024
• When is Divorce Mediation Not Recommended? - June 12, 2024

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

Problem-Solving Strategies and Obstacles

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

JGI / Jamie Grill / Getty Images

• Application
• Improvement

From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. doi:10.3389/fnhum.2018.00261

Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

National Alliance on Mental Illness. Warning signs and symptoms .

Mayer RE. Thinking, problem solving, cognition, 2nd ed .

Schooler JW, Ohlsson S, Brooks K. Thoughts beyond words: When language overshadows insight. J Experiment Psychol: General . 1993;122:166-183. doi:10.1037/0096-3445.2.166

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

Want to subscribe to The McKinsey Podcast ?

Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

Would you like to learn more about our Strategy & Corporate Finance Practice ?

Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

Want better strategies? Become a bulletproof problem solver

Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

Explore a career with us

Related articles.

Strategy to beat the odds

Five routes to more innovative problem solving

How to improve your problem-solving at work: skills, models and examples

Highly valued by employers, problem-solving is needed in just about any line of work. We’ll show you how to step up your ability to take on workplace challenges below…

Whether you’re a seasoned manager or in a junior role, you’re bound to encounter challenges that need tackling head on during your week. And when it comes to overcoming them, fine-tuned, well-honed problem-solving skills are the way to do it.

If your problem-solving has been off in the past, then it can be difficult to take a step back before you act. Luckily, problem-solving is a skill to be learned like any other.

To help you get to grips with this valued skill, we’ll define problem-solving in detail, show you why it matters, and offer some pointers for improving your problem-solving skills.

What are problem-solving skills?

Problem-solving skills let us take on issues without resorting to hasty decisions and snap judgements. They’re what allow us to better understand the challenges before us so we can come up with solutions for dealing with them.

Depending on what the problem is, such skills may call on things like active listening , teamwork, creative thinking or mathematical analysis. Whatever you use to reach a solution, problem-solving is a valuable soft skill that most employers will look for in potential employees.

Why are problem-solving skills important?

Problem-solvers are equipped to take on what comes their way. When they have the right tools at their disposal, they’re in a better position to observe the issue, judge it accordingly, and act in the most effective way. And through experience, these skills become more refined and precise, allowing them to take on tougher problems.

So, why else are they important? Let’s look at what else problem-solving can add to an employee’s skillset…

Greater time management skills

When you know how to approach a problem, greater time management skills tend to come naturally. Because you can balance your time more efficiently, your ability to weigh up your options becomes more precise and considered, allowing you to make less hasty decisions that could make a problem worse.

More creative thinking

Those with strong problem-solving skills can always see the opportunity in a challenge. By tackling problems with innovative solutions, you might find that the result is stronger than you expect.

Improved performance under pressure

When deadlines loom or change is on the horizon, a lack of problem-solving skills could be what leads to poor or half-baked solutions. Because they’re naturally geared towards dealing with the unknown and the unexpected, problem-solvers are less inclined to feel pressure when it arises.

Greater addressing of risk

As well as the ability to deal with the issue itself, problem-solvers are well-equipped to address problems that could spring up later down the line based on trends, patterns and current events . This allows them to possess a degree of control over the future.

How to improve your problem-solving skills

So, how can you improve your ability to solve problems in the workplace? The following tips can help give you an edge whatever your position in a company may be.

Look for opportunities to solve problems

If you’re not used to taking them on, it can be easy to sit back and let someone else deal with problems. Instead of shying away from them, put yourself in situations where problems can arise.

We don’t mean deliberately making mistakes here, but taking on more duties in your current role, with another team or outside your organisation can help familiarise you with the kind of problems that can occur and ways to deal with them.

Observe how others solve problems

By shadowing your colleagues, you can learn problem-solving techniques and put them into practice yourself. Ask a colleague if you can observe their strategy, or schedule in a one-to-one to ask about how they take on problems.

Familiarise yourself with practice problems

There’s a wealth of resources in print and online that you can use to improve your problem-solving skills. These materials offer all sorts of scenarios to put your abilities to the test, unearthing skills you didn’t know you had.

An example problem-solving model

There are several problem-solving models out there, but typically, they follow the broad steps below.

1. Define the problem

Take a step back and analyse the situation. Are there multiple problems? What is causing them? How do these problems affect you and others involved?

Then, drill into the problem by doing the following:

• Separate facts from opinion

• Identify what has caused the problem

• Discuss with team members to gather more information

• Gather relevant data

At this stage, don’t be tempted to come up with a solution. You’re simply trying to find out what the problem is.

2. Identify potential solutions

While you may have only come up with one solution to a problem in the past, brainstorming several alternatives is a better approach. Ask colleagues for their input and get some insights from those with experience of similar problems.

In coming up with alternatives, consider the following:

• Weigh up what might slow down solving the problem

• Ensure your ideas align with goals and objectives

• Identify long and short-term solutions

• Write down the solutions you come up with

3. Evaluate your solutions

Once you have a list of solutions, you need to evaluate them further before acting. What are the positive and negative consequences of each? What resources will you need to carry them out? How much time and, if necessary, who else will you need to put the solution in place?

4. Choose a solution

Your evaluation should clarify which solution best suits the problem. Now it’s time to put that solution into practice.

Before you do, consider:

• Does it solve the problem without creating another?

• Have you reached a group consensus over the solution?

• Is implementing it practical and straightforward?

• Does it fit within company policies and procedures?

5. Put the solution into action

Once you’ve decided on the right solution, it needs to be implemented. Your action plan should include measurable objectives that allow you to monitor its success, as well as timelines and feedback channels your team can use during implementation.

Making sure this plan is communicated to everyone involved will also be key to its success.

6. Assess how effective the solution is

Your work isn’t done just yet! You’ll need to measure how things are progressing to ensure the solution is working as intended. Doing so means you can course-correct should further surprises arise, or else go back to alternative solutions.

How to show problem-solving skills on your CV and at interviews

As we said up top, problem-solving is highly valued by employers, so you’ll want to highlight such abilities on your CV, cover letter and in interviews.

Think back to previous roles for examples of when you used problem-solving skills. It’s not enough to say you’re good at problem-solving; employers will be looking for concrete examples, so be sure to mention them in your cover letter and use bullet points on your CV with specifics.

In interviews, you might be called on to describe times when you encountered problems in previous roles. Here, you should mention the processes you followed to address these issues, the skills you used, and the outcomes achieved.

Likewise, you may be asked hypothetical questions to show how you would solve problems. Base your answers on the steps above, and use the STARR method in conjunction with previous instances of problem-solving to give a detailed yet concise response.

Click here   for the latest news and features from SEFE Marketing & Trading or   visit our homepage   to find out about our latest career opportunities.

The views, opinions and positions expressed within this article are those of our third-party content providers alone and do not represent those of SEFE Marketing & Trading. The accuracy, completeness and validity of any statements made within this article are not guaranteed. SEFE Marketing & Trading accepts no liability for any errors, omissions or representations.

you may also like

The 10 Project Management Knowledge Areas

How to Write a CV for an Internship

How to Get an Internship that is Right for You

• SEFE Marketing & Trading London
• SEFE Marketing & Trading Singapore
• SEFE Energy Manchester
• SEFE Energy Paris
• SEFE Marketing & Trading Switzerland
• SEFE Energy Netherlands

20 Triton Street London NW1 3BF United Kingdom

[email protected]

Tel: +44 (0) 207 756 0000

Fax: +44 (0) 207 756 9740

Ocean Financial Centre, 10 Collyer Quay #41-01 Singapore 049315

Tel: +65 (0) 6671 9100

Fax: +65 (0) 6435 6200

8 First Street, Manchester United Kingdom M15 4RP

Tel: +44 (0) 845 230 0011

Fax: +44 (0) 845 230 0022

159 rue Anatole France – Bâtiment B, Levallois-Perret, 92300, Paris

Tel: +33 (0) 1 42 99 73 50

Fax: +33 (0) 1 42 99 73 99

Poststrasse 2, 6300 Zug, Switzerland

Tel: 0845 230 0011

Fax: 0845 230 0022

Utopialaan 48, ’s-Hertogenbosch, Netherlands 5232 CE

Tel: +31 (0)88 730 6103

Fax: +31 (0)88 7306133

• Meet Our Team
• Discover The LSA Difference
• Coaching Process
• Core Values
• What is Executive Functioning?
• Understanding the EF Ripple Effect
• For Daily Life
• ND-Friendly Tools
• Executive Functioning Assessment
• Executive Functioning Meal Plan
• Executive Functioning 101 Resource Hub
• Executive Functioning IEP Goal Resource Hub
• How To Make Stuff More EF Friendly

Teaching the IDEAL Problem-Solving Method to Diverse Learners

Written by:

  Amy Sippl

Filed under: EF 101 Series , Executive Functioning , Problem Solving

Published:  January 21, 2021

Last Reviewed: April 10, 2023

READING TIME:  ~ minutes

We may assume that teens and young adults come equipped with a strong sense of approaching difficult or uncertain situations. For many of the individuals we work with, problem-solving needs to be practiced and developed in the same way as academic and social skills. The IDEAL Problem Solving Method is one option to teach problem-solving to diverse learners.

What is problem-solving?

Problem-solving is the capacity to identify and describe a problem and generate solutions to fix it .

Problem-solving involves other executive functioning behaviors as well, including attentional control, planning , and task initiation . Individuals might use time management , emotional control, or organization skills to solve problems as well. Over time, learners can observe their behavior, use working memory , and self-monitor behaviors to influence how we solve future issues.

Why are problem-solving strategies important?

Not all diverse learners develop adequate problem-solving. Learners with a history of behavioral and learning challenges may not always use good problem-solving skills to manage stressful situations. Some students use challenging behaviors like talking back, arguing, property destruction, and aggression when presented with challenging tasks. Others might shut down, check out, or struggle to follow directions when encountering new or unknown situations.

Without a step-by-step model for problem-solving , including identifying a problem and choosing a replacement behavior to solve it, many of our children and students use challenging behaviors instead. The IDEAL Problem-Solving Method is one option to teach diverse learners to better approach difficult situations.

IDEAL Problem-Solving Method

In 1984, Bransford and Stein published one of the most popular and well-regarded problem-solving methods. It’s used both in industry and in education to help various learners establish a problem, generate solutions, and move forward quickly and efficiently. By teaching your learner each step of the IDEAL model, you can provide them with a set of steps to approach a problem with confidence.

The IDEAL Problem-Solving Method includes:

I – Identify the problem.

There’s no real way to create a solution to a problem unless you first know the scope of the problem. Encourage your learner to identify the issue in their own words. Outline the facts and the unknowns. Foster an environment where your learner is praised and supported for identifying and taking on new problems.

Examples of identifying problems:

• “I have a math quiz next week and don’t know how to do the problems.”
• “I can’t access my distance learning course website.”
• “The trash needs to be taken out, and I can’t find any trash bags.”

D – Define an outcome

The second step in the IDEAL problem-solving process is to define an outcome or goal for problem-solving. Multiple people can agree that a problem exists but have very different ideas on goals or outcomes. By deciding on an outlined objective first, it can speed up the process of identifying solutions.

Defining outcomes and goals may be a difficult step for some diverse learners. The results don’t need to be complicated, but just clear for everyone involved.

Examples of defining outcomes:

• “I want to do well on my math quiz.”
• “I get access to the course website.”
• “The trash gets taken out before the trash pickup day tomorrow.”

E – Explore possible strategies.

Once you have an outcome, encourage your learner to brainstorm possible strategies. All possible solutions should be on the table during this stage, so encourage learners to make lists, use sticky notes, or voice memos to record any ideas. If your learner struggles with creative idea generation, help them develop a plan of resources for who they might consult in the exploration stage.

Examples of possible strategies to solve a problem:

• “I review the textbook; I ask for math help from a friend; I look up the problems online; I email my teacher.”
• “I email my teacher for the course access; I ask for help from a classmate; I try to reset my password.”
• “I use something else for a trash bag; I place an online order for bags; I take the trash out without a bag; I ask a neighbor for a bag; I go shopping for trash bags.”

A – Anticipate Outcomes & Act

Once we generate a list of strategies, the next step in the IDEAL problem-solving model recommends that you review the potential steps and decide which one is the best option to use first. Helping learners to evaluate the pros and cons of action steps can take practice. Ask questions like, “What might happen if you take this step?” or “Does that step make you feel good about moving forward or uncertain?”

After evaluating the outcomes, the next step is to take action. Encourage your learner to move forward even if they may not know the full result of taking action. Support doing something, even if it might not be the same strategy, you might take to solve a problem or the ‘best’ solution.

L – Look and Learn

The final step in the IDEAL problem-solving model is to look and learn from an attempt to solve a problem. Many parents and teachers forget this critical step in helping diverse learners to stop and reflect when problem-solving goes well and doesn’t go well. Helping our students and children learn from experience can make problem-solving more efficient and effective in the future. Ask questions like “How did that go?” and “What do you think you’ll do differently next time?”

Examples of Look and Learn statements:

• “I didn’t learn the problems from looking at the textbook, but it did help to call a friend. I’ll start there next time.”
• “When I didn’t have access to the course website, resetting my password worked.”
• “I ran out of trash bags because I forgot to put them on the shopping list . I’ll buy an extra box of trash bags to have them on hand, so I don’t run out next time.”

Practice Problem-Solving

For ideas on common problems, download our deck of problem-solving practice cards. Set aside time to practice, role-play, give feedback, and rehearse again if needed.

How to teach the IDEAL problem-solving method

Top businesses and corporations spend thousands of dollars on training teams to implement problem-solving strategies like the IDEAL method. Employees practice and role-play common problems in the workplace . Coaches give supportive feedback until everyone feels confident in each of the steps.

Teachers and parents can use the same process to help students and children use the IDEAL problem-solving method. Set aside time to review common problems or social scenarios your learner might encounter. Practice using the IDEAL method when emotions and tensions aren’t running as high. Allow your learner to ask questions, work through problems, and receive feedback and praise for creating logical action plans.

Further Reading

• Bransford, J., and Stein, B., “The Ideal Problem Solver” (1993). Centers for Teaching and Technology – Book Library . 46. https://digitalcommons.georgiasouthern.edu/ct2-library/4
• Executive Functioning 101: Planning Skills
• Executive Functioning: Task Initiation
• Executive Functioning Skills by Age: What to Expect
• Kern, L., George, M. P., & Weist, M. D. (2016). Supporting students with emotional and behavioral problems. Baltimore, MD: Paul H. Brookes.

About The Author

Amy Sippl is a Minnesota-based Board Certified Behavior Analyst (BCBA) and freelance content developer specializing in helping individuals with autism and their families reach their best possible outcomes. Amy earned her Master's Degree in Applied Behavior Analysis from St. Cloud State University and also holds undergraduate degrees in Psychology and Family Social Science from University of Minnesota – Twin Cities. Amy has worked with children with autism and related developmental disabilities for over a decade in both in-home and clinical settings. Her content focuses on parents, educators, and professionals in the world of autism—emphasizing simple strategies and tips to maximize success. To see more of her work visit amysippl.com .

Related Posts

10-minute tips to improve self-monitoring skills, how to make studying executive function friendly, 25 board games that promote executive functioning skills, how to make riding public transit executive function friendly, how to make school more executive function friendly, 5 executive functioning strategies to create more free time.

Life Skills Advocate is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com. Some of the links in this post may be Amazon.com affiliate links, which means if you make a purchase, Life Skills Advocate will earn a commission. However, we only promote products we actually use or those which have been vetted by the greater community of families and professionals who support individuals with diverse learning needs.

Session expired

Please log in again. The login page will open in a new tab. After logging in you can close it and return to this page.

26 Expert-Backed Problem Solving Examples – Interview Answers

Published: February 13, 2023

Interview Questions and Answers

Actionable advice from real experts:

Biron Clark

Former Recruiter

Contributor

Dr. Kyle Elliott

Career Coach

Hayley Jukes

Editor-in-Chief

Biron Clark , Former Recruiter

Kyle Elliott , Career Coach

Hayley Jukes , Editor

As a recruiter , I know employers like to hire people who can solve problems and work well under pressure.

 A job rarely goes 100% according to plan, so hiring managers are more likely to hire you if you seem like you can handle unexpected challenges while staying calm and logical.

But how do they measure this?

Hiring managers will ask you interview questions about your problem-solving skills, and they might also look for examples of problem-solving on your resume and cover letter. 

In this article, I’m going to share a list of problem-solving examples and sample interview answers to questions like, “Give an example of a time you used logic to solve a problem?” and “Describe a time when you had to solve a problem without managerial input. How did you handle it, and what was the result?”

• Problem-solving involves identifying, prioritizing, analyzing, and solving problems using a variety of skills like critical thinking, creativity, decision making, and communication.
• Describe the Situation, Task, Action, and Result ( STAR method ) when discussing your problem-solving experiences.
• Tailor your interview answer with the specific skills and qualifications outlined in the job description.
• Provide numerical data or metrics to demonstrate the tangible impact of your problem-solving efforts.

What are Problem Solving Skills? 

Problem-solving is the ability to identify a problem, prioritize based on gravity and urgency, analyze the root cause, gather relevant information, develop and evaluate viable solutions, decide on the most effective and logical solution, and plan and execute implementation. 

Problem-solving encompasses other skills that can be showcased in an interview response and your resume. Problem-solving skills examples include:

• Critical thinking
• Analytical skills
• Decision making
• Research skills
• Technical skills
• Communication skills
• Adaptability and flexibility

Why is Problem Solving Important in the Workplace?

Problem-solving is essential in the workplace because it directly impacts productivity and efficiency. Whenever you encounter a problem, tackling it head-on prevents minor issues from escalating into bigger ones that could disrupt the entire workflow. 

Beyond maintaining smooth operations, your ability to solve problems fosters innovation. It encourages you to think creatively, finding better ways to achieve goals, which keeps the business competitive and pushes the boundaries of what you can achieve. 

Effective problem-solving also contributes to a healthier work environment; it reduces stress by providing clear strategies for overcoming obstacles and builds confidence within teams. 

Examples of Problem-Solving in the Workplace

• Correcting a mistake at work, whether it was made by you or someone else
• Overcoming a delay at work through problem solving and communication
• Resolving an issue with a difficult or upset customer
• Overcoming issues related to a limited budget, and still delivering good work through the use of creative problem solving
• Overcoming a scheduling/staffing shortage in the department to still deliver excellent work
• Troubleshooting and resolving technical issues
• Handling and resolving a conflict with a coworker
• Solving any problems related to money, customer billing, accounting and bookkeeping, etc.
• Taking initiative when another team member overlooked or missed something important
• Taking initiative to meet with your superior to discuss a problem before it became potentially worse
• Solving a safety issue at work or reporting the issue to those who could solve it
• Using problem solving abilities to reduce/eliminate a company expense
• Finding a way to make the company more profitable through new service or product offerings, new pricing ideas, promotion and sale ideas, etc.
• Changing how a process, team, or task is organized to make it more efficient
• Using creative thinking to come up with a solution that the company hasn’t used before
• Performing research to collect data and information to find a new solution to a problem
• Boosting a company or team’s performance by improving some aspect of communication among employees
• Finding a new piece of data that can guide a company’s decisions or strategy better in a certain area

Problem-Solving Examples for Recent Grads/Entry-Level Job Seekers

• Coordinating work between team members in a class project
• Reassigning a missing team member’s work to other group members in a class project
• Adjusting your workflow on a project to accommodate a tight deadline
• Speaking to your professor to get help when you were struggling or unsure about a project
• Asking classmates, peers, or professors for help in an area of struggle
• Talking to your academic advisor to brainstorm solutions to a problem you were facing
• Researching solutions to an academic problem online, via Google or other methods
• Using problem solving and creative thinking to obtain an internship or other work opportunity during school after struggling at first

How To Answer “Tell Us About a Problem You Solved”

When you answer interview questions about problem-solving scenarios, or if you decide to demonstrate your problem-solving skills in a cover letter (which is a good idea any time the job description mentions problem-solving as a necessary skill), I recommend using the STAR method.

STAR stands for:

It’s a simple way of walking the listener or reader through the story in a way that will make sense to them. 

Start by briefly describing the general situation and the task at hand. After this, describe the course of action you chose and why. Ideally, show that you evaluated all the information you could given the time you had, and made a decision based on logic and fact. Finally, describe the positive result you achieved.

Note: Our sample answers below are structured following the STAR formula. Be sure to check them out!

EXPERT ADVICE

Dr. Kyle Elliott , MPA, CHES Tech & Interview Career Coach caffeinatedkyle.com

How can I communicate complex problem-solving experiences clearly and succinctly?

Before answering any interview question, it’s important to understand why the interviewer is asking the question in the first place.

When it comes to questions about your complex problem-solving experiences, for example, the interviewer likely wants to know about your leadership acumen, collaboration abilities, and communication skills, not the problem itself.

Therefore, your answer should be focused on highlighting how you excelled in each of these areas, not diving into the weeds of the problem itself, which is a common mistake less-experienced interviewees often make.

Tailoring Your Answer Based on the Skills Mentioned in the Job Description

As a recruiter, one of the top tips I can give you when responding to the prompt “Tell us about a problem you solved,” is to tailor your answer to the specific skills and qualifications outlined in the job description. 

Once you’ve pinpointed the skills and key competencies the employer is seeking, craft your response to highlight experiences where you successfully utilized or developed those particular abilities. 

For instance, if the job requires strong leadership skills, focus on a problem-solving scenario where you took charge and effectively guided a team toward resolution. 

By aligning your answer with the desired skills outlined in the job description, you demonstrate your suitability for the role and show the employer that you understand their needs.

Amanda Augustine expands on this by saying:

“Showcase the specific skills you used to solve the problem. Did it require critical thinking, analytical abilities, or strong collaboration? Highlight the relevant skills the employer is seeking.”  

Interview Answers to “Tell Me About a Time You Solved a Problem”

Now, let’s look at some sample interview answers to, “Give me an example of a time you used logic to solve a problem,” or “Tell me about a time you solved a problem,” since you’re likely to hear different versions of this interview question in all sorts of industries.

The example interview responses are structured using the STAR method and are categorized into the top 5 key problem-solving skills recruiters look for in a candidate.

1. Analytical Thinking

Situation: In my previous role as a data analyst , our team encountered a significant drop in website traffic.

Task: I was tasked with identifying the root cause of the decrease.

Action: I conducted a thorough analysis of website metrics, including traffic sources, user demographics, and page performance. Through my analysis, I discovered a technical issue with our website’s loading speed, causing users to bounce. 

Result: By optimizing server response time, compressing images, and minimizing redirects, we saw a 20% increase in traffic within two weeks.

2. Critical Thinking

Situation: During a project deadline crunch, our team encountered a major technical issue that threatened to derail our progress.

Task: My task was to assess the situation and devise a solution quickly.

Action: I immediately convened a meeting with the team to brainstorm potential solutions. Instead of panicking, I encouraged everyone to think outside the box and consider unconventional approaches. We analyzed the problem from different angles and weighed the pros and cons of each solution.

Result: By devising a workaround solution, we were able to meet the project deadline, avoiding potential delays that could have cost the company $100,000 in penalties for missing contractual obligations.

3. Decision Making

Situation: As a project manager , I was faced with a dilemma when two key team members had conflicting opinions on the project direction.

Task: My task was to make a decisive choice that would align with the project goals and maintain team cohesion.

Action: I scheduled a meeting with both team members to understand their perspectives in detail. I listened actively, asked probing questions, and encouraged open dialogue. After carefully weighing the pros and cons of each approach, I made a decision that incorporated elements from both viewpoints.

Result: The decision I made not only resolved the immediate conflict but also led to a stronger sense of collaboration within the team. By valuing input from all team members and making a well-informed decision, we were able to achieve our project objectives efficiently.

4. Communication (Teamwork)

Situation: During a cross-functional project, miscommunication between departments was causing delays and misunderstandings.

Task: My task was to improve communication channels and foster better teamwork among team members.

Action: I initiated regular cross-departmental meetings to ensure that everyone was on the same page regarding project goals and timelines. I also implemented a centralized communication platform where team members could share updates, ask questions, and collaborate more effectively.

Result: Streamlining workflows and improving communication channels led to a 30% reduction in project completion time, saving the company $25,000 in operational costs.

5. Persistence 

Situation: During a challenging sales quarter, I encountered numerous rejections and setbacks while trying to close a major client deal.

Task: My task was to persistently pursue the client and overcome obstacles to secure the deal.

Action: I maintained regular communication with the client, addressing their concerns and demonstrating the value proposition of our product. Despite facing multiple rejections, I remained persistent and resilient, adjusting my approach based on feedback and market dynamics.

Result: After months of perseverance, I successfully closed the deal with the client. By closing the major client deal, I exceeded quarterly sales targets by 25%, resulting in a revenue increase of $250,000 for the company.

Tips to Improve Your Problem-Solving Skills

Throughout your career, being able to showcase and effectively communicate your problem-solving skills gives you more leverage in achieving better jobs and earning more money .

So to improve your problem-solving skills, I recommend always analyzing a problem and situation before acting.

 When discussing problem-solving with employers, you never want to sound like you rush or make impulsive decisions. They want to see fact-based or data-based decisions when you solve problems.

Don’t just say you’re good at solving problems. Show it with specifics. How much did you boost efficiency? Did you save the company money? Adding numbers can really make your achievements stand out.

To get better at solving problems, analyze the outcomes of past solutions you came up with. You can recognize what works and what doesn’t.

Think about how you can improve researching and analyzing a situation, how you can get better at communicating, and deciding on the right people in the organization to talk to and “pull in” to help you if needed, etc.

Finally, practice staying calm even in stressful situations. Take a few minutes to walk outside if needed. Step away from your phone and computer to clear your head. A work problem is rarely so urgent that you cannot take five minutes to think (with the possible exception of safety problems), and you’ll get better outcomes if you solve problems by acting logically instead of rushing to react in a panic.

You can use all of the ideas above to describe your problem-solving skills when asked interview questions about the topic. If you say that you do the things above, employers will be impressed when they assess your problem-solving ability.

More Interview Resources

• 3 Answers to “How Do You Handle Stress?”
• How to Answer “How Do You Handle Conflict?” (Interview Question)
• Sample Answers to “Tell Me About a Time You Failed”

About the Author

Biron Clark is a former executive recruiter who has worked individually with hundreds of job seekers, reviewed thousands of resumes and LinkedIn profiles, and recruited for top venture-backed startups and Fortune 500 companies. He has been advising job seekers since 2012 to think differently in their job search and land high-paying, competitive positions. Follow on Twitter and LinkedIn .

Read more articles by Biron Clark

About the Contributor

Kyle Elliott , career coach and mental health advocate, transforms his side hustle into a notable practice, aiding Silicon Valley professionals in maximizing potential. Follow Kyle on LinkedIn .

About the Editor

Hayley Jukes is the Editor-in-Chief at CareerSidekick with five years of experience creating engaging articles, books, and transcripts for diverse platforms and audiences.

Continue Reading

12 Expert-Approved Responses to ‘What Makes You Unique?’ in Job Interviews

15 most common pharmacist interview questions and answers, 15 most common paralegal interview questions and answers, top 30+ funny interview questions and answers, 60 hardest interview questions and answers, 100+ best ice breaker questions to ask candidates, top 20 situational interview questions (& sample answers), 15 most common physical therapist interview questions and answers.

What Are Problem-Solving Skills? Definition and Examples

• Share on Twitter Share on Twitter
• Share on Facebook Share on Facebook
• Share on LinkedIn Share on LinkedIn

Forage puts students first. Our blog articles are written independently by our editorial team. They have not been paid for or sponsored by our partners. See our full  editorial guidelines .

Why do employers hire employees? To help them solve problems. Whether you’re a financial analyst deciding where to invest your firm’s money, or a marketer trying to figure out which channel to direct your efforts, companies hire people to help them find solutions. Problem-solving is an essential and marketable soft skill in the workplace. 

So, how can you improve your problem-solving and show employers you have this valuable skill? In this guide, we’ll cover:

Problem-Solving Skills Definition

Why are problem-solving skills important, problem-solving skills examples, how to include problem-solving skills in a job application, how to improve problem-solving skills, problem-solving: the bottom line.

Problem-solving skills are the ability to identify problems, brainstorm and analyze answers, and implement the best solutions. An employee with good problem-solving skills is both a self-starter and a collaborative teammate; they are proactive in understanding the root of a problem and work with others to consider a wide range of solutions before deciding how to move forward. 

Examples of using problem-solving skills in the workplace include:

• Researching patterns to understand why revenue decreased last quarter
• Experimenting with a new marketing channel to increase website sign-ups
• Brainstorming content types to share with potential customers
• Testing calls to action to see which ones drive the most product sales
• Implementing a new workflow to automate a team process and increase productivity

Problem-solving skills are the most sought-after soft skill of 2022. In fact, 86% of employers look for problem-solving skills on student resumes, according to the National Association of Colleges and Employers Job Outlook 2022 survey . 

It’s unsurprising why employers are looking for this skill: companies will always need people to help them find solutions to their problems. Someone proactive and successful at problem-solving is valuable to any team.

“Employers are looking for employees who can make decisions independently, especially with the prevalence of remote/hybrid work and the need to communicate asynchronously,” Eric Mochnacz, senior HR consultant at Red Clover, says. “Employers want to see individuals who can make well-informed decisions that mitigate risk, and they can do so without suffering from analysis paralysis.”

Showcase new skills

Build the confidence and practical skills that employers are looking for with Forage’s free job simulations.

Problem-solving includes three main parts: identifying the problem, analyzing possible solutions, and deciding on the best course of action.

>>MORE: Discover the right career for you based on your skills with a career aptitude test .

Research is the first step of problem-solving because it helps you understand the context of a problem. Researching a problem enables you to learn why the problem is happening. For example, is revenue down because of a new sales tactic? Or because of seasonality? Is there a problem with who the sales team is reaching out to? 

Research broadens your scope to all possible reasons why the problem could be happening. Then once you figure it out, it helps you narrow your scope to start solving it. 

Analysis is the next step of problem-solving. Now that you’ve identified the problem, analytical skills help you look at what potential solutions there might be.

“The goal of analysis isn’t to solve a problem, actually — it’s to better understand it because that’s where the real solution will be found,” Gretchen Skalka, owner of Career Insights Consulting, says. “Looking at a problem through the lens of impartiality is the only way to get a true understanding of it from all angles.”

Decision-Making

Once you’ve figured out where the problem is coming from and what solutions are, it’s time to decide on the best way to go forth. Decision-making skills help you determine what resources are available, what a feasible action plan entails, and what solution is likely to lead to success.

On a Resume

Employers looking for problem-solving skills might include the word “problem-solving” or other synonyms like “ critical thinking ” or “analytical skills” in the job description.

“I would add ‘buzzwords’ you can find from the job descriptions or LinkedIn endorsements section to filter into your resume to comply with the ATS,” Matthew Warzel, CPRW resume writer, advises. Warzel recommends including these skills on your resume but warns to “leave the soft skills as adjectives in the summary section. That is the only place soft skills should be mentioned.”

On the other hand, you can list hard skills separately in a skills section on your resume .

Forage Resume Writing Masterclass

Learn how to showcase your skills and craft an award-winning resume with this free masterclass from Forage.

Avg. Time: 5 to 6 hours

Skills you’ll build: Resume writing, professional brand, professional summary, narrative, transferable skills, industry keywords, illustrating your impact, standing out

In a Cover Letter or an Interview

Explaining your problem-solving skills in an interview can seem daunting. You’re required to expand on your process — how you identified a problem, analyzed potential solutions, and made a choice. As long as you can explain your approach, it’s okay if that solution didn’t come from a professional work experience.

“Young professionals shortchange themselves by thinking only paid-for solutions matter to employers,” Skalka says. “People at the genesis of their careers don’t have a wealth of professional experience to pull from, but they do have relevant experience to share.”

Aaron Case, career counselor and CPRW at Resume Genius, agrees and encourages early professionals to share this skill. “If you don’t have any relevant work experience yet, you can still highlight your problem-solving skills in your cover letter,” he says. “Just showcase examples of problems you solved while completing your degree, working at internships, or volunteering. You can even pull examples from completely unrelated part-time jobs, as long as you make it clear how your problem-solving ability transfers to your new line of work.”

Learn How to Identify Problems

Problem-solving doesn’t just require finding solutions to problems that are already there. It’s also about being proactive when something isn’t working as you hoped it would. Practice questioning and getting curious about processes and activities in your everyday life. What could you improve? What would you do if you had more resources for this process? If you had fewer? Challenge yourself to challenge the world around you.

Think Digitally

“Employers in the modern workplace value digital problem-solving skills, like being able to find a technology solution to a traditional issue,” Case says. “For example, when I first started working as a marketing writer, my department didn’t have the budget to hire a professional voice actor for marketing video voiceovers. But I found a perfect solution to the problem with an AI voiceover service that cost a fraction of the price of an actor.”

Being comfortable with new technology — even ones you haven’t used before — is a valuable skill in an increasingly hybrid and remote world. Don’t be afraid to research new and innovative technologies to help automate processes or find a more efficient technological solution.

Collaborate

Problem-solving isn’t done in a silo, and it shouldn’t be. Use your collaboration skills to gather multiple perspectives, help eliminate bias, and listen to alternative solutions. Ask others where they think the problem is coming from and what solutions would help them with your workflow. From there, try to compromise on a solution that can benefit everyone.

If we’ve learned anything from the past few years, it’s that the world of work is constantly changing — which means it’s crucial to know how to adapt . Be comfortable narrowing down a solution, then changing your direction when a colleague provides a new piece of information. Challenge yourself to get out of your comfort zone, whether with your personal routine or trying a new system at work.

Put Yourself in the Middle of Tough Moments

Just like adapting requires you to challenge your routine and tradition, good problem-solving requires you to put yourself in challenging situations — especially ones where you don’t have relevant experience or expertise to find a solution. Because you won’t know how to tackle the problem, you’ll learn new problem-solving skills and how to navigate new challenges. Ask your manager or a peer if you can help them work on a complicated problem, and be proactive about asking them questions along the way.

Career Aptitude Test

What careers are right for you based on your skills? Take this quiz to find out. It’s completely free — you’ll just need to sign up to get your results!

Step 1 of 3

Companies always need people to help them find solutions — especially proactive employees who have practical analytical skills and can collaborate to decide the best way to move forward. Whether or not you have experience solving problems in a professional workplace, illustrate your problem-solving skills by describing your research, analysis, and decision-making process — and make it clear that you’re the solution to the employer’s current problems. 

Looking to learn more workplace professional skills? Check out Two Sigma’s Professional Skills Development Virtual Experience Program .

Image Credit: Christina Morillo / Pexels 

Related Posts

6 negotiation skills to level up your work life, how to build conflict resolution skills: case studies and examples, what is github uses and getting started, upskill with forage.

Build career skills recruiters are looking for.

• Search Search Please fill out this field.
• Career Planning
• Skills Development

What Are Problem-Solving Skills?

Definition & Examples of Problem-Solving Skills

• Problem-solving skills help you determine why an issue is happening and how to resolve that issue.

Learn more about problem-solving skills and how they work.

Problem-solving skills help you solve issues quickly and effectively. It's one of the  key skills that employers  seek in job applicants, as employees with these skills tend to be self-reliant. Problem-solving skills require quickly identifying the underlying issue and implementing a solution.

Problem-solving is considered a  soft skill  (a personal strength) rather than a hard skill that's learned through education or training. You can improve your problem-solving skills by familiarizing yourself with common issues in your industry and learning from more experienced employees.

How Problem-Solving Skills Work

Problem-solving starts with identifying the issue. For example, a teacher might need to figure out how to improve student performance on a writing proficiency test. To do that, the teacher will review the writing tests looking for areas of improvement. They might see that students can construct simple sentences, but they're struggling with writing paragraphs and organizing those paragraphs into an essay.

To solve the problem, the teacher would work with students on how and when to write compound sentences, how to write paragraphs, and ways to organize an essay.

Theresa Chiechi / The Balance

There are five steps typically used in problem-solving.

1. Analyze Contributing Factors

To solve a problem, you must find out what caused it. This requires you to gather and evaluate data, isolate possible contributing circumstances, and pinpoint what needs to be addressed for a resolution.

To do this, you'll use skills like :

• Data gathering
• Data analysis
• Fact-finding
• Historical analysis

2. Generate Interventions

Once you’ve determined the cause, brainstorm possible solutions. Sometimes this involves teamwork since two (or more) minds are often better than one. A single strategy is rarely the obvious route to solving a complex problem; devising a set of alternatives helps you cover your bases and reduces your risk of exposure should the first strategy you implement fail.

This involves skills like :

• Brainstorming
• Creative thinking
• Forecasting
• Project design
• Project planning

3. Evaluate Solutions

Depending on the nature of the problem and your chain of command, evaluating best solutions may be performed by assigned teams, team leads, or forwarded to corporate decision-makers. Whoever makes the decision must evaluate potential costs, required resources, and possible barriers to successful solution implementation.

This requires several skills, including:

• Corroboration
• Test development
• Prioritizing

4. Implement a Plan

Once a course of action has been decided, it must be implemented along with benchmarks that can quickly and accurately determine whether it’s working. Plan implementation also involves letting personnel know about changes in standard operating procedures.

This requires skills like:

• Project management
• Project implementation
• Collaboration
• Time management
• Benchmark development

5. Assess the Solution's Effectiveness

Once a solution is implemented, the best problem-solvers have systems in place to evaluate if and how quickly it's working. This way, they know as soon as possible whether the issue has been resolved or whether they’ll have to change their response to the problem mid-stream.

This requires:

• Communication
• Customer feedback
• Follow-through
• Troubleshooting

Here's an example of showing your problem-solving skills in a cover letter.

When I was first hired as a paralegal, I inherited a backlog of 25 sets of medical records that needed to be summarized, each of which was hundreds of pages long. At the same time, I had to help prepare for three major cases, and there weren’t enough hours in the day. After I explained the problem to my supervisor, she agreed to pay me to come in on Saturday mornings to focus on the backlog. I was able to eliminate the backlog in a month.

Here's another example of how to show your problem-solving skills in a cover letter:

When I joined the team at Great Graphics as Artistic Director, the designers had become uninspired because of a former director who attempted to micro-manage every step in the design process. I used weekly round-table discussions to solicit creative input and ensured that each designer was given full autonomy to do their best work. I also introduced monthly team-based competitions that helped build morale, spark new ideas, and improve collaboration.

Highlighting Problem-Solving Skills

• Since this is a skill that's important to most employers, put them front and center on your resume, cover letter, and in interviews.

If you're not sure what to include, look to previous roles—whether in academic, work, or volunteer settings—for examples of challenges you met and problems you solved. Highlight relevant examples in your  cover letter and use bullet points in your resume to show how you solved a problem.

During interviews, be ready to describe situations you've encountered in previous roles, the processes you followed to address problems, the skills you applied, and the results of your actions. Potential employers are eager to hear a  coherent narrative of the ways you've used problem-solving skills .

Interviewers may pose hypothetical problems for you to solve. Base your answers on the five steps and refer to similar problems you've resolved, if possible. Here are tips for answering problem-solving interview questions , with examples of the best answers.

Key Takeaways

• It's one of the key skills that employers seek in job applicants.
• Problem-solving starts with identifying the issue, coming up with solutions, implementing those solutions, and evaluating their effectiveness. 

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Med Educ Online

The Dreyfus model of clinical problem-solving skills acquisition: a critical perspective

Adolfo peña.

1 VA National Quality Scholars (VAQS) Fellowship Program

2 Birmingham VA Medical Center

3 University of Alabama at Birmingham

4 Center for Surgical, Medical Acute Care Research and Transitions (C-SMART)

The Dreyfus model describes how individuals progress through various levels in their acquisition of skills and subsumes ideas with regard to how individuals learn. Such a model is being accepted almost without debate from physicians to explain the ‘acquisition’ of clinical skills.

This paper reviews such a model, discusses several controversial points, clarifies what kind of knowledge the model is about, and examines its coherence in terms of problem-solving skills. Dreyfus' main idea that intuition is a major aspect of expertise is also discussed in some detail. Relevant scientific evidence from cognitive science, psychology, and neuroscience is reviewed to accomplish these aims.

Conclusions

Although the Dreyfus model may partially explain the ‘acquisition’ of some skills, it is debatable if it can explain the acquisition of clinical skills. The complex nature of clinical problem-solving skills and the rich interplay between the implicit and explicit forms of knowledge must be taken into consideration when we want to explain ‘acquisition’ of clinical skills. The idea that experts work from intuition, not from reason, should be evaluated carefully.

Models are conceptual constructs that aspire to represent real things or processes that to a large extent are hidden for the senses and to the ordinary experience. Models have a role to describe, represent, explain, and ‘translate’ the world. Some good examples are the Feynman diagrams of electrodynamic processes, the fluid mosaic membrane, and the DNA double helix. Although models are partial and just approximations to the truth, they are not fictional or conventional at all. They try to represent their referents in a truthful and objective way with the hope to constantly improve or replace them with better approximations or more precise explanations ( 1 ).

Dreyfus and Dreyfus ( 2 , 3 ) have offered a model of professional expertise that plots an individual's progression through a series of five levels: novice, advanced beginner, competent, proficient, and expert. In the novice stage a person follows rules that are context-free and feels no responsibility for anything other than following the rules. Competence develops after having considerable experience. Proficiency is shown in individuals who use intuition in decision making and develop their own rules to formulate plans. Expertise is characterized by a fluid performance that happens unconsciously, automatically, and no longer depends on explicit knowledge. Thus, the progression is envisaged as a gradual transition from a rigid adherence to taught rules and procedures through to a largely intuitive mode of operation that relies heavily on deep, implicit knowledge but accepts that sometimes at expert level analytical approaches are still likely to be used when an intuitive approach fails initially.

This model, a product of philosophical deliberation and phenomenological research, was initially adapted by Benner and other nursing educators to explain the development of nursing skills ( 4 ). However, this was not without debate, which still remains. Hargreaves and Lane criticized Benner's model, a linear model of skill acquisition that cannot sufficiently explain the everyday experiences of learning ( 5 ). Thompson ( 6 ), Purkis ( 7 ), and Rudge ( 8 ) criticized Benner's and Dreyfus' models because of their apparent absence of social structure or social knowledge. English pointed out that Benner's and Dreyfus' models fail to identify expert nurses because they neglect to specify objective qualifications for expertise ( 9 ). For Effken, the terms ‘expertise’ and ‘intuition’ do not have operational definitions: ‘structured measurement has been elusive because of the complexity of the domain and the degree to which skill is embedded in a particular situation’ ( 10 ). Gobet and Chassy, in contraposition to Dreyfus' and Benner's phenomenological philosophy, suggest an alternative conceptual framework to understand the role of intuition in expertise ( 11 ).

Assuming that nurses’ and physicians’ skills are of the same nature, physician educators have ‘translated’ and adjusted such a model to explain clinical skills not only in terms of simple routine tasks but also in terms of the most symbolic skills, i.e., clinical problem-solving skills ( 12 ). Many authors express their support for this. For Daaleman, Dreyfus provides a model of knowledge and skill acquisition that is relevant to the training of physicians in practical wisdom ( 13 ). Batalden, Holmboe and Hawkins recommend assuming Dreyfus' ideas as a framework to understand medical competencies [ 14 , 15 ]. The Accreditation Council for Graduate Medical Education (ACGME) recommends this model for curriculum-planning for residency training programs ( 16 ).

Contrary to the debate raised in academic nursing fields, judging by medical publications and recommendations from academic organizations, the current form of Dreyfus' model ( 2 , 3 , 17 – 19 ) is being accepted almost without explicit criticism from physicians. Thus, although there may be some debate among clinicians and educators, such a debate is not evident in published papers. The Dreyfus model is reaching out to the educative arena and thus plays an important role in modeling how physicians acquire clinical skills. This may generate important consequences for our education. As was mentioned in this introduction, even models that are born from science are not complete explanations or perfect approximations to the truth, and they might be erroneous. Different from those, the Dreyfus model comes from philosophical fields; this fact makes even more urgent a critical analysis and debate. This paper tries to stimulate both.

A brief inventory of the Dreyfus model

A very important requirement for any model is its referent, i.e., the object or process referred to by the model or that which the latter is about ( 20 ). The Dreyfus model postulates that when individuals acquire a skill through external instruction, they normally pass through several stages. It is undeniable that such a process implies the acquiring of some knowledge. This psychological result of perception, learning, and reasoning constitutes the Dreyfus model's primary referent. Because the acquisition of knowledge does not happen in a vacuum but in a very complex organ (the brain), it is desirable that any hypothetical construct that attempts to explain learning is defined not only psychologically but also neurologically ( 21 ). Unfortunately, neurological terms appear in the model only when Dreyfus gestures toward artificial neural networks to demonstrate that phenomenology can reveal objective structures of bodily praxis ( 18 , 22 ). Therefore, we may say that the brain is a secondary or spurious referent of such a model.

Postulates and propositions

The Dreyfus model has been proposed in prose style. Because it is easier to analyze a model when its content is structured in clear and unambiguous sentences (propositions) capable of being evaluated as true or false to some degree, two lists have been created and are presented in Boxes 1 and 2 . They were prepared after a careful review of Dreyfus' original works and summarize the model ( 2 , 3 , 17 – 19 , 23 – 25 ). To contrast Dreyfus' ideas, the author proposes some statements (listed to the right of the boxes) that were produced after reviewing various psychological, neuroscientific, and philosophical works ( 1 , 20 , 21 , 28 – 30 , 34 – 66 , 68 – 79 ).

Dreyfus's postulates versus alternative propositions

Dreyfus' postulates versus alternative propositions

History and scientific evidence

Some historical facts may be also interesting. The original model was not published immediately for public scrutiny. Four prior reports exist from the US Air Force ( 2 , 23 – 25 ), where some observations carried out on the instruction of jet pilots are described by Dreyfus. In those reports, few original scientific studies were cited and standardized protocols were not utilized. The only recent change in the model is the addition of two stages (‘master’ and ‘practical wisdom’) ( 17 ) to the five originally proposed ( 2 ).

All models have philosophical roots; Dreyfus' ideas are based on phenomenology ( 18 ), a philosophical doctrine proposed by Edmund Husserl based on the study of personal experience in which considerations of objective reality are not taken into account. This view opposes scientific realism; for Husserl, the world of things ‘is only a presumptive reality,’ whereas the subject is the absolute reality ( 26 ). The world is also ‘an infinite idea, a complete synthesis of possible experiences’ ( 27 ). Thus, the reality is subject-dependent because a thing is a complex of sensations. Moreover, according to Husserl, introspection through ordinary experience rather than through experiment, analysis, and modeling can yield deep knowledge of the world ( 28 ). For Martin Heidegger, another key proponent of phenomenology, ‘the word is the abode of being’ ( 29 , p. 280), and ‘things become and are only in the word, in language’ ( 30 ). In other words, reality is constituted in and through discourse. We smell this philosophy in Dreyfus' original work on the model of skill acquisition ( 2 ) and we discover his explicit adherence to phenomenology, especially to Heidegger's existential phenomenology, in one of the most authoritative texts on these matters: ‘Being in the world: a commentary on Heidegger's “being and time”’ ( 31 ).

Adaptation to clinical medicine

For the medical field, the model has been adapted with minor changes. For example, Dreyfus' main postulates are that the ‘immediate intuitive situational response is the characteristic of expertise’ ( 17 , p. 42), and that most expert performance is ongoing and non-reflective: ‘fluid performance happens unconsciously, automatically, naturally’ ( 3 , p. 32), and ‘the expert driver generally knows how to perform the act without evaluating and comparing alternatives’ ( 3 , p. 33). Medical educators have proposed a hybrid model where masters are highly intuitive as well as reflective: ‘the master is the practitioner who self-assesses and self-regulates and reflects in, on and for action’ ( 12 ). This current statement contradicts the original model. Frequently, it is also stated by physicians that the model postulates that experts use intuition where empirical and propositional knowledge does not yet exist. Actually, the original model was proposed the other way around: experts work intuitively on every problem and only use other types of knowledge in a few cases when intuition fails.

The following paragraphs will discuss the most controversial aspects the Dreyfus model proposes. The main body of this paper will go further about the referents and basically will clarify what kind of knowledge the model is about and will review its coherence with problem-solving skills; some relevant scientific evidence from cognitive science, psychology, and neuroscience will also be reviewed. The central idea of intuition as a major definition of expertise will be discussed in some detail. This is also a good time to advise that this manuscript does not have any intention to be ‘ecumenical.’ Readers interested in favorable opinions and sympathetic papers of the Dreyfus model are urged to read several of the publications included in the references ( 4 , 12 – 15 , 32 , 33 ).

Types of knowledge and the Dreyfus model

Because one of the most important referents of the model is knowledge, it would be of some benefit to review that concept. There are many kinds of knowledge and several ways of grouping these kinds into large categories ( 34 ). A division of knowledge that is relevant when analyzing Dreyfus model is into know-that and know-how. Traditionally, explicit knowledge or ‘knowing that’ has been understood as expressible in some languages; it can be attained easily from any codified information ( 35 ). By contrast, ‘knowing how,’ tacit or implicit knowledge, as it was proposed by philosopher Michael Polanyi, is not expressible in some languages. It is considered intuitive – acquired through practical experience – and as such, is subjective and contextual, and cannot be readily made explicit or formalized ( 36 ). Polanyi also suggested the supremacy of such implicit knowledge: ‘While tacit knowledge can be possessed by itself, explicit knowledge must rely on being tacitly understood and applied. Hence all knowledge is either tacit or rooted in tacit knowledge’ ( 37 ).

In psychology, the knowledge gained in implicit learning is defined by using several criteria ( 38 ). It is not fully accessible to consciousness. The learner cannot provide a full verbal account of what he has learned. Implicit knowledge does not involve processes of conscious hypothesis testing. In addition, implicit knowledge is preserved in cases of amnesia; thus, implicit learning relies on neuronal mechanisms other than the hippocampal memory system ( 39 ). Implicit knowledge is stored as abstract – and possibly instantiated – representations rather than aggregate or verbatim representations. This knowledge may also be inflexible because of its non-hippocampal base ( 40 ).

Knowledge that represents its content, attitude, and its holder explicitly is on the higher-order thought theory, conscious, and is considered explicit. Explicit mental representation is required to refer in verbal communication and thus a link emerges between explicitness and consciousness ( 41 ). The explicit processing of knowledge includes perceptual, cognitive, and motor processes, such as stimulus selection and search, attention focusing and maintenance, memorization, computation, decision making, response selection, and execution ( 42 ).

Recently, neuroscientists have proposed the Competition between Verbal and Implicit Systems (COVIS) model to explain the brain functional specialization and localization for the processing of these two types of knowledge ( 43 ). The verbal (explicit) system is mediated by frontal brain areas, such as the anterior cingulate, prefrontal cortex, and the head of the caudate nucleus. The implicit system is mainly mediated by the tail of the caudate nucleus and a dopamine-mediated reward signal ( 44 , 45 ). The role of the basal ganglia in implicit learning and knowledge has been investigated through the study of people with Huntington's or Parkinson's disease ( 46 , 47 ). Besides the COVIS model, there is evidence that the frontal lobes appear to be involved in the evaluation of implicit knowledge in making conceptual fluency judgments ( 38 ). Hippocampus-dependent memory systems subserve explicit memory formation ( 40 ).

There is considerable evidence in favor of this ‘specialization’ and division of knowledge ( 38 , 41 , 48 , 49 ). However, there is not any evidence that sophisticated skills are performed either without a rich connection of both neuronal subsystems or without a rich interplay of both domains of knowledge. Galanter and Smith observed that even in subjects who are not engaging in conscious hypothesis testing, they can still notice that there is a pattern and can develop explicit knowledge of it ( 50 ). Individual learners, during motor skill practice, can discover the correct solution to a movement problem using either their implicit, explicit or a combination of both domains of knowledge; each approach leads to motor skill learning ( 51 ). The serial reaction time task, a classical example of ‘implicit’ knowledge acquired during sequence learning, is available for intentional control and is, in this sense, explicit ( 52 ). Automatic and intentional forms of processing can be brought under intentional control ( 53 ). Besides, explicit knowledge is an important and active variable that influences problem-solving processes, especially problem representation. Individuals who have accumulated considerable explicit knowledge in a domain represent problems more efficiently than individuals without extensive knowledge bases ( 54 ). In the face of strongly held explicit beliefs, knowledge gained through implicit learning is disregarded ( 55 ). Hence, in normal humans, it is difficult to develop a pure task that allows only implicit or explicit knowledge to contribute to performance. In particular, sophisticated skills are fueled by explicit knowledge.

Although the Dreyfus brothers recognize this division of knowledge, they believe that skills are exclusive instances of know-how or implicit knowledge: ‘you can ride a bicycle because you possess something called “know-how,” which you acquired from practice and sometimes painful experience’ ( 3 , p. 16). The Dreyfus brothers assert that when we perform a skill, we basically execute implicit knowledge without a connection to explicit knowledge. They believe that skills are automatic dispositions that cannot be readily made explicit ( 2 , 3 ). They go further and propose that the net effect of learning is intuition and define it in terms of implicit knowledge: ‘when we speak of intuition or know-how, we are referring to the understanding that effortlessly occurs upon seeing similarities with previous experiences. We shall use intuition and know-how as synonyms’ ( 3 , p. 28). In summary, Dreyfus and Dreyfus define skills at expert level almost exclusively in terms of implicit knowledge.

A critical point is to accept whether or not clinical problem-solving skills are implicit in nature or if they are predominantly dependent upon implicit knowledge. As we reviewed above, it is difficult to develop a task exclusively in terms of implicit knowledge. Even more importantly, clinical problem-solving skills are also instances of explicit knowledge. The clearest cases of explicit knowledge of a fact are representations of one's own attitude of knowing that fact. Knowledge capable of such fully explicit representation provides the necessary and sufficient conditions for conscious knowledge ( 41 ). This is the case when a physician evaluates a patient. Although he is not aware of all of the cognitive steps needed to make a diagnosis, he needs to be conscious of at least of the following events: characterization of a patient's symptom, valuation of a patient's sign, and solicitation of a diagnostic test. Furthermore, physicians explicitly provide a representation (diagnosis) and express the degree of accuracy or inaccuracy and can judge their representations to be true, false or undecided. Hence, it is reasonable to accept that making a diagnosis also subsumes an explicit dimension of knowledge. Therefore, a model that does not respect the complex and rich interaction between both domains of knowledge will have difficulty explaining skills that are not just routines but instead very complex tasks, i.e., finding solutions to problems.

Inverse problems and clinical problem-solving skills

We will start the discussion of this section by pointing out that there is not only one type of problem, but several types. Most problems can be classified into direct, well-defined problems and inverse, ill-defined problems. Direct or forward problems are of the following type: given C (causes)→E (effects), find E (effects), where (→) symbolizes the causal relationships ( 29 , pp. 145–164). These types of problems call for analysis, or progressive reasoning, either from premises to conclusions or from causes to effects. In contrast, an inverse problem is a more complicated problem of the following type: given the clinical data E (effects = symptoms) and the acceptable causal hypothesis C 1 →E, C 2 →E,…, C n →E, find the original cause C. Inverse problems require synthesis, or regressive reasoning, from conclusions to premises or from effects to causes. Inverse problems also are ill-defined problems in the sense that a simple solution may not exist, there may be more than one solution, or a small change in the problem leads to a big change in the solution ( 56 ).

Well-defined and direct problems have a clear path to a solution. The problem may be solved by using a set of recursive operations or algorithms ( 57 , 58 ). In contrast, the cognitive processes involved in the solution of ill-defined problems are far more complicated and still ill-understood. In the case of ill-defined problems, all aspects of problem formulation are challenging. Most are fuzzy problems, often difficult to delineate and even harder to represent in a way that makes them solvable ( 59 ). In addition, inverse problems imply a novelty for each case, and expertise should reflect an ability to react to situations that experts have never encountered before. In this context, problems cannot be solved ‘automatically’ or only ‘intuitively.’

The Dreyfus model has been derived from observation of the performance of experts, such as jet pilots and dancers, experts who are used to tackling direct problems. Is it correct to use this model also to explain the performances of experts who are used to tackling inverse problems? It is plausible that often the skills involved in solving direct problems are not the same as those involved in solving inverse problems. Think about the skills needed to solve this short list of inverse problems: to ‘guess’ the intention of a person from his/her behavior, to discover the authors of a crime knowing the crime scene, to ‘imagine’ an internal body part from the attenuation in intensity of an X-ray beam, to guess the premises of an argument from some of its conclusions, or to diagnose a sickness on the strength of its symptoms. The investigation of those problems does not proceed downstream, from premises to conclusions or from causes to effects. Working on all those problems involves reversing the logical or causal stream. In medicine, physicians face inverse problems all of the time. In fact, the typical diagnosis problem is not the direct problem of inferring syndrome from disease, but the inverse problem of guessing disease from symptoms ( 60 ). Anyone who wants to propose a model to explain how we develop clinical problem-solving skills must recognize carefully that the skills used to solve inverse problems are of a different nature than the skills used to solve direct problems. A model should be specific for skills of different natures; the Dreyfus model is not specific enough.

Rules and context

In the Dreyfus model, a novice should memorize rules and should not feel responsible for other things: ‘to improve, the novice needs monitoring, either by self-observation or instructional feedback, so as to bring his behavior more and more completely into conformity with the rule.’ ( 2 , p. 7). Besides, the Dreyfus model supports the idea that at proficient and competent levels, performers should have developed ‘personal guidelines and maxims’ in order to be able to deal successfully with tasks and problems ( 2 , 3 ). Why do we have to assume that these Dreyfus propositions are right? Is that the way we learn skills of explicit or even of tacit nature? Is it a good idea to memorize rules at novice stages? Do proficient and competent physicians solve diagnostic problems using just a set of ‘personal’ rules and maxims?

Early problem-solving research proposed the ‘general problem solver model.’ In this model the solution of a problem is conceptualized as a movement between two states: a starting state, named ‘problem space,’ and a final state named ‘goal state’ ( 58 ). There are ‘rules of transition’ which refer to those functions that move the system from one state to another, and there are also heuristics tools, rules that determine which moves are to be made in the problem space. Although this model gives great value to the use of rules, it should be recognized that these components are well suited for solving well-defined and direct problems, where the space and transitions between states are unambiguous ( 59 ). However, the model offers no solution whatsoever for dealing with inverse problems, for which there do not exist simple rules to solve them.

In medicine, although there are clinical guidelines and algorithms available that can help physicians deal with some problems, physicians acknowledge that these ‘rules’ are just general recommendations. Besides, physicians use ‘guidelines’ after they have transformed an inverse problem into a direct one. This is after diagnostic hypotheses have been generated. However, there is not a recipe to generate hypotheses. Furthermore, physicians use heuristic rules, such as Occam's razor regarding parsimony, but these ‘rules’ are general recommendations. They are explicit (not personal), and still it is not well known what impact they have on clinical problem-solving skills ( 61 ).

Rules are instructions for doing something, and even when they may be constructed as a mapping of possible actions (algorithms), they do not describe or explain any particular event or thing because they prescribe what to do. If we accept that knowledge has a transferable content that has been encoded and externalized in cultural artifacts, such as a book, then we should recognize that rules are not the sole element of that content, because knowledge consists of thousands of concepts, propositions, and theories. This knowledge allows us to grasp the nature of disease; understanding is a pre-requisite to learning. The development of clinical reasoning skills for medical students is dependent on basic science achievements ( 62 , 63 ). Novices, who rely on biomedical knowledge, solve complicated diagnostic problems with more success ( 64 ).

Believing that students should only memorize rules has a dark side and can cause deleterious consequences. When rules are available for everything, novices can spare the effort of imagining a different way to solve an inverse problem. Hence, they would tend to proceed to solve problems in a rather mindless way. We should reflect on the fact that to learn, students need all kinds of stimuli, such as propositional from books and experience. But they also need freedom to develop the talent to produce diagnostic hypotheses by spotting, inventing, and sometimes guessing.

Other elements to analyze are Dreyfus ideas that learners at pre-competent stages have a complete ignorance of the ‘context,’ and that the education at this level should be decontextualized: ‘normally, the instruction process begins by decomposing the task environment into context-free features which the beginner can recognize without benefit of experience’ ( 2 , p. 7). Contrary to such an idea, we should acknowledge that everything in our world, including concepts, is interrelated. Learning, as any other event, happens under specific conditions and should not be detached from the real experience. Medical students always face the context. Of course, at the beginning, there is not enough insight into every detail. However, students’ minds are not like computers following a program; they have some ideas, some approaches, and some knowledge of the context. For example, medical students can generate numerous diagnostic inferences, even without considerable clinical experience ( 65 , 66 ). How can they do that if novices like them ‘ignore’ the context? Accumulating experience is not a passive recording. Learning is creative in the sense that it is new and not automatic to the individual. Even at the pre-beginner stage, learners gain experience and understanding of the context. Information, context, and experience cannot be separated.

The Dreyfus brothers propose that intuition is the endpoint of learning and a key characteristic of expertise: ‘the expert pilot, having finally reached this non-analytical stage of performance, responds intuitively and appropriately to his current situation’ ( 2 , p. 12). Hubert Dreyfus describes a master as one with a lot of experience who produces almost instantaneously appropriate perspectives, who thinks intuitively, not analytically, and who ceases to pay conscious attention to his performance turning it unconsciously: ‘the expert, like masters in the “long Zen tradition” or Luke Skywalker when responding to Obi Wan Kenobi's advice to use the force “transcends” “trying” or “efforting” and “just responds”’ ( 67 , p. 22).

Adults often learn to drive a car, type, play chess, ski, etc. In most cases we perform such skills intuitively, quickly, unconsciously, and ‘just respond.’ These everyday skills are relatively easy to acquire, at least to an acceptable level. It is plausible that some steps required to perform a simple task are so fast that we consider them on an unconscious level even though we are alert and oriented. Neuroscience tries to explain that there are two kinds of neuronal aggregations in the brain's organization: one is constituted of heavily interconnected neurons with long-range axons (named workspaces) and the others are system sets of specialized neuronal processors (perceptual, motor, memory, evaluative, and attentional) with short axons ( 68 ). The latter ones are not enough to perform tasks that require great effort, so the workspace neurons are activated, making the effort conscious. This mobilization is greater with complex cognitive tasks ( 68 , 69 ).

However, the popular conception that some simple everyday skills are performed fast and ‘unconsciously’ can explain neither the performance of difficult tasks nor the acquisition of sophisticated skills ( 70 ). In the case of problem-solving skills, empirical studies have demonstrated a distinction between expert and novice problem representation in terms of the time spent on various stages of the problem-solving process. Contrary to the idea that experts dedicate less time than novices, Lesgold ( 71 ) found that experts spent more time than novices determining an appropriate representation of the problem. Experts spent more time comparing their knowledge to the information they needed to discover in order to best represent the problem.

Even skilled rapid motor production, as in typing, is not simple nor is completely automatic. Studies showed that expert typists look ahead to prepare for what comes next. They acquire complex representations and skills to anticipate future actions ( 72 ). Something similar happens in music, where the mark of expert performance is the ability to control one's performance and its results; there is not such a thing as an automatic and immediate response. Expert music performance requires several different representations: ‘imagined music experience’ (desired performance goal), ‘playing a piece of music’ (how to execute the performance), and ‘listening to the played music’ (hearing one's performance) ( 73 ). The resulting music performance should not be seen as a fixed and automated sequence of motor actions. It should be viewed as a flexible, controllable outcome based on these representations ( 70 ).

Consequently, it is hard to believe that the whole clinical problem-solving process is intuitive in the sense that it is unconscious, effortless, and automated. Although the use of ‘pattern recognitions’ and ‘illness scripts’ can happen in an automatic way, especially when data or a prior experience triggers a possible diagnosis, this explains only one state of the whole problem-solving process. Good physicians, although esteeming intellectual intuition because of its suggestive power, know that it can be dangerous: first, because intuition does not have demonstrative force, and second, because intuition is never fine enough. Intuition, as a very fast and almost instant inference, consists of showing rather than demonstrating; in proving in a brief and imperfect way, and in rendering plausible the hypothesis that has been invented. It is a kind of rudimentary reasoning that uses incomplete evidence, visual images, and analogies (prior experiences) rather than complete data, refined concepts, and detailed inferences ( 74 ). A diagnosis formulated in an intuitive way will have to be worked out in a rational way and then tested by the usual procedures. This is because the suspicion generated by the illness scripts and pattern recognitions are not proof of a diagnosis. Further, this is why we use a lot of auxiliary tests and image studies. Expert clinicians intentionally avoid any tendency toward automatization as they often lose control of many relevant aspects of a clinical encounter. Ericson called this ‘deliberate practice’ ( 70 ).

There is evidence that experts use two modes of thinking: analytic (hypothetic–deductive) and non-analytic (pattern recognition), even in perceptual specialties ( 75 – 78 ). Both modes of thinking are part of a continuous process. Expert physicians do not use analytic reasoning only after a failed attempt with non-analytic reasoning or the other way around. Clinical medicine is one of the more complicated and challenging professions; it is very simplistic to explain problem-solving processes starting and ending with intuition. Many diagnostic errors are due to overconfidence and heuristic availability, and some errors occur during non-analytic reasoning ( 79 ). Intuition, because it is brief and readily accomplished and grasped, must be expanded to be validated.

Implications and conclusions

Any model is a representation of a thing, and in this representation two elements play important roles: the represented and the representing things. With this pair of elements we can make diverse kinds of representations: factual–factual (a scale model), factual–conceptual (a theoretical model), factual–semiotic (a scientific text), and semiotic–factual (a text illustration) ( 1 ). The acquisition of skills is a learning process and is obviously factual. Hence the Dreyfus model attempts to be a factual–conceptual model, a theory or at least an outlook of how we acquire diverse skills. Any fact–concept correspondence is of course difficult and not of the one-to-one type. However, because it tries to be truthful, a theory must attempt to be coherent and related to the facts.

Although the Dreyfus model is not taken strictly as a ‘prescription,’ it is plausible that its descriptive face is influencing us to generate a worldview, a general outlook of how we learn and teach medicine. Every worldview has an effect on our actions and policies. And here is the point of major implication, because this model can influence educative policies, recommendations, and guidelines. This model can also generate unhappy contradictions. For example, it has been said that the Dreyfus model provides us with a framework for consistency within the evaluation system ( 80 ). How can this model help us to ground our evaluation system if the model suggests explaining physicians’ performance in terms of implicit knowledge and intuition? By definition, if implicit knowledge is not expressible in some language, then it is inaccessible to evaluate. Certainly we need more debates and we need to evaluate this model not only in light of philosophical but also of scientific considerations.

Although the Dreyfus model could partially explain the ‘acquisition’ of some skills, it is another matter as to whether it can explain the acquisition of clinical skills. The occurrence of inverse problems and the rich interplay between the implicit and explicit domains of knowledge must be taken into consideration when we want to explain ‘acquisition’ of clinical skills. The idea that the net effect of education and training in medicine is that we start developing intuition about what we are doing must be revised and evaluated carefully.

Using this model in a prescriptive way must elicit a more critical eye to see if novices must receive an education where rules are the only important things to learn in a decontextualized environment. Finally, we must acknowledge the complexity of all the processes implied in learning. We cannot merely accept the temptation to oversimplify these complex processes, and ignore intentionally or not information from science, in particular from cognition, psychology, and neuroscience.

Acknowledgements

The author would like to offer special thanks to Dr Mario Bunge, Frothingham chair of Logic and Metaphysics at McGill University; Dr Gustavo Heudebert, Director of the UAB Internal Medicine Residency Program; and the anonymous reviewers for helpful comments and suggestions on an earlier draft of his article.

Conflict of interest and funding

This paper was prepared while the author was a VAQS fellow at the VA Birmingham Medical Center and the Center for Surgical, Medical Acute Care Research and Transitions (C-SMART), these institutions covered the publication costs for this paper.

Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model

• Open access
• Published: 24 June 2024

Cite this article

You have full access to this open access article

• Laura Brandl   ORCID: orcid.org/0000-0001-7974-7892 1 ,
• Matthias Stadler 1 , 2 ,
• Constanze Richters 1 ,
• Anika Radkowitsch 3 ,
• Martin R. Fischer 2 ,
• Ralf Schmidmaier 4 &
• Frank Fischer 1  

157 Accesses

Explore all metrics

Collaborative skills are crucial in knowledge-rich domains, such as medical diagnosing. The Collaborative Diagnostic Reasoning (CDR) model emphasizes the importance of high-quality collaborative diagnostic activities (CDAs; e.g., evidence elicitation and sharing), influenced by content and collaboration knowledge as well as more general social skills, to achieve accurate, justified, and efficient diagnostic outcomes (Radkowitsch et al., 2022). However, it has not yet been empirically tested, and the relationships between individual characteristics, CDAs, and diagnostic outcomes remain largely unexplored. The aim of this study was to test the CDR model by analyzing data from three studies in a simulation-based environment and to better understand the construct and the processes involved ( N = 504 intermediate medical students) using a structural equation model including indirect effects. We found various stable relationships between individual characteristics and CDAs, and between CDAs and diagnostic outcome, highlighting the multidimensional nature of CDR. While both content and collaboration knowledge were important for CDAs, none of the individual characteristics directly related to diagnostic outcome. The study suggests that CDAs are important factors in achieving successful diagnoses in collaborative contexts, particularly in simulation-based settings. CDAs are influenced by content and collaboration knowledge, highlighting the importance of understanding collaboration partners’ knowledge. We propose revising the CDR model by assigning higher priority to collaboration knowledge compared with social skills, and dividing the CDAs into information elicitation and sharing, with sharing being more transactive. Training should focus on the development of CDAs to improve CDR skills.

Similar content being viewed by others

Beyond experiential knowledge: a classification of patient knowledge

Interventions to improve team effectiveness within health care: a systematic review of the past decade

Strategies for teaching evidence-based practice in nursing education: a thematic literature review

Avoid common mistakes on your manuscript.

Introduction

Collaborative skills are highly relevant in many situations, ranging from computer-supported collaborative learning to collaborative problem-solving in professional practice (Fiore et al., 2018 ). While several broad collaborative problem-solving frameworks exist (OECD, 2017 ), most of them are situated in knowledge-lean settings. However, one example of collaborative problem-solving of knowledge-rich domains is collaborative diagnostic reasoning (CDR; Radkowitsch et al., 2022 )—which aligns closely with medical practice—as this is a prototypical knowledge-rich domain requiring high collaboration skills in daily practice. In daily professional practice, physicians from different specialties often need to collaborate with different subdisciplines to solve complex problems, such as diagnosing, that is, determining the causes of a patient’s problem. Moreover, research in medical education and computer-supported collaborative learning suggests that the acquisition of medical knowledge and skills is significantly enhanced by collaborative problem-solving (Hautz et al., 2015 ; Koschmann et al., 1992 ). For problem-solving and learning, it is crucial that all relevant information (e.g., evidence and hypotheses) is elicited from and shared with the collaboration partner (Schmidt & Mamede, 2015 ). However, CDR is not unique to the medical field but also relevant in other domains, such as teacher education (Heitzmann et al., 2019 ).

The CDR model has been the basis of empirical studies and describes how individual characteristics and the diagnostic process are related to the diagnostic outcome. However, it has not yet been empirically tested, and the relationships between individual characteristics, diagnostic process, and diagnostic outcome remain mostly unexplored (Fink et al., 2023 ). The aim of this study is to test the CDR model by analyzing data from three studies with similar samples and tasks investigating CDR in a simulation-based environment. By undertaking these conceptual replications, we aspire to better understand the construct and the processes involved. As prior research has shown, collaboration needs to be performed at a high quality to achieve accurate problem solutions respectively learning outcomes (Pickal et al., 2023 ).

Collaborative Diagnostic Reasoning (CDR) Model

Diagnosing can be understood as the process of solving complex diagnostic problems through “goal-oriented collection and interpretation of case-specific or problem-specific information to reduce uncertainty” in decision-making through performing diagnostic activities at a high quality (Heitzmann et al., 2019 , p. 4). To solve diagnostic problems, that is, to identify the causes of an undesired state, it is increasingly important to collaborate with experts from different fields, as these problems become too complex to be solved individually (Abele, 2018 ; Fiore et al., 2018 ). Collaboration provides advantages such as the division of labor, access to diverse perspectives and expertise, and enhanced solution quality through collaborative sharing of knowledge and skills (Graesser et al., 2018 ).

The CDR model is a theoretical model focusing on the diagnostic process in collaborative settings within knowledge-rich domains (Radkowitsch et al., 2022 ). The CDR model is based on scientific discovery as a dual-search model (SDDS; Klahr & Dunbar, 1988 ) and its further development by van Joolingen and Jong ( 1997 ). The SDDS model describes individual reasoning as the coordinated search through hypothetical evidence and hypotheses spaces and indicates that for successful reasoning it is important not only that high-quality cognitive activities within these spaces are performed but also that one is able to coordinate between them (Klahr & Dunbar, 1988 ). In the extended SDDS model (van Joolingen & Jong, 1997 ) focusing on learning in knowledge-rich domains, a learner hypothesis space was added including all the hypotheses one can search for without additional knowledge. Although Dunbar ( 1995 ) found that conceptual change occurs more often in groups than in individual work, emphasizing the importance of collaborative processes in scientific thinking and knowledge construction, the SDDS model has hardly been systematically applied in computer-supported collaborative learning and collaborative problem-solving.

Thus, the CDR model builds upon these considerations and describes the relationship between individual characteristics, the diagnostic process, and the diagnostic outcome. As in the SDDS model we assume that CDR involves activities within an evidence and hypotheses space; however, unlike the SDDS in the CDR model, these spaces are understood as cognitive storages of information. Which aligns more to the extended dual search space model of scientific discovery learning (van Joolingen & Jong, 1997 ). In summary we assume that coordinating between evidence (data) and hypothesis (theory) is essential for successful diagnosing. Further, the CDR model is extended to not only individual but also collaborative cognitive activities and describes the interaction of epistemic activities (F. Fischer et al., 2014 ) and collaborative activities (Liu et al., 2016 ) to construct a shared problem representation (Rochelle & Teasley, 1995 ) and effectively collaborate. Thus, we define CDR as a set of skills for solving a complex problem collaboratively “by generating and evaluating evidence and hypotheses that can be shared with, elicited from, or negotiated among collaborators” (Radkowitsch et al., 2020 , p. 2). The CDR model also makes assumptions about the factors necessary for successful CDR. First, we look at what successful CDR means, why people differ, and what the mediating processes are.

Diagnostic Outcome: Accuracy, Justification, and Efficiency

The primary outcome of diagnostic processes, such as CDR, is the accuracy of the given diagnosis, which indicates problem-solving performance or expertise (Boshuizen et al., 2020 ). However, competence in diagnostic reasoning, whether it is done individually or collaboratively, also includes justifying the diagnosis and reaching it effectively. This is why, in addition to diagnostic accuracy, diagnostic justification and diagnostic efficiency should also be considered as secondary outcomes of the diagnostic reasoning process (Chernikova et al., 2022 ; Daniel et al., 2019 ). Diagnostic justification makes the reasoning behind the decision transparent and understandable for others (Bauer et al., 2022 ). Good reasoning entails a justification including evidence, which supports the reasoning (Hitchcock, 2005 ). Diagnostic efficiency is related to how much time and effort is needed to reach the correct diagnosis; this is important for CDR, as diagnosticians in practice are usually under time pressure (Braun et al., 2017 ). Both diagnostic justification and diagnostic efficiency are thus indicators of a structured and high-quality reasoning process. So, while in many studies, the focus of assessments regarding diagnostic reasoning is on the accuracy of the given diagnosis (Daniel et al., 2019 ), the CDR model considers all three facets of the diagnostic outcome as relevant factors.

Individual Characteristics: Knowledge and Social Skills

Research has shown that content knowledge, social skills, and, in particular, collaboration knowledge are important prerequisites for, and outcomes of, computer-supported collaborative learning (Jeong et al., 2019 ; Vogel et al., 2017 ). CDR has integrated these dependencies into its model structure. Thus, the CDR model assumes that people engaging in CDR differ with respect to their content knowledge, collaboration knowledge, and domain general social skills.

Content knowledge refers to conceptual and strategic knowledge in a specific domain (Förtsch et al., 2018 ). Conceptual knowledge encompasses factual understanding of domain-specific concepts and their interrelations. Strategic knowledge entails contextualized knowledge regarding problem-solving during the diagnostic process (Stark et al., 2011 ). During expertise development, large amounts of content knowledge are acquired and restructured through experience with problem-solving procedures and routines (Boshuizen et al., 2020 ). Research has repeatedly shown that having high conceptual and strategic knowledge is associated with the diagnostic outcome (e.g., Kiesewetter et al., 2020 ; cf. Fink et al., 2023 ).

In addition to content knowledge, the CDR model assumes that collaborators need collaboration knowledge. A key aspect of collaboration knowledge (i.e., being aware of knowledge distribution in the group; Noroozi et al., 2013 ) is the pooling and processing of non-shared information, as research shows that a lack of collaboration knowledge has a negative impact on information sharing, which in turn has a negative impact on performance (Stasser & Titus, 1985 ).

Finally, general social skills influence the CDR process. These skills mainly influence the collaborative aspect of collaborative problem-solving and less the problem-solving aspect (Graesser et al., 2018 ). Social skills are considered particularly important when collaboration knowledge is low (F. Fischer et al., 2013 ). CDR assumes that in particular the abilities to share and negotiate ideas, to coordinate, and to take the perspective are relevant for the diagnostic process and the diagnostic outcome (Radkowitsch et al., 2022 ; see also Liu et al., 2016 , and Hesse et al., 2015 ).

Diagnostic Process: Collaborative Diagnostic Activities

The diagnostic process is thought to mediate the effect of the individual characteristics on the diagnostic outcome and is described in the CDR model using collaborative diagnostic activities (CDAs), such as evidence elicitation, evidence sharing, and hypotheses sharing (Heitzmann et al., 2019 ; Radkowitsch et al., 2022 ). One of the main functions of CDAs is to construct a shared problem representation (Rochelle & Teasley, 1995 ) by sharing and eliciting relevant information, as information may not be equally distributed among all collaborators initially. To perform these CDAs at a high quality, each collaborator needs to identify information relevant to be shared with the collaboration partner as well as information they need from the collaboration partner (OECD, 2017 ).

Evidence elicitation involves requesting information from a collaboration partner to access additional knowledge resources (Weinberger & Fischer, 2006 ). Evidence sharing and hypothesis sharing involve identifying the information needed by the collaborator to build a shared problem representation. This externalization of relevant information can be understood as the novelty aspect of transactivity (Vogel et al., 2023 ). However, challenges arise from a lack of relevant information due to deficient sharing, which can result from imprecise justification and insufficient clustering of information. In particular, research has shown that collaborators often lack essential information-sharing skills, such as identifying information relevant for others from available data, especially in the medical domain (Kiesewetter et al., 2017 ; Tschan et al., 2009 ).

It is crucial for the diagnostic outcome that all relevant evidence and hypotheses are elicited and shared for the specific collaborators (Tschan et al., 2009 ). However, diagnostic outcomes seem to be influenced more by the relevance and quality of the shared information than by their quantity (Kiesewetter et al., 2017 ; Tschan et al., 2009 ). In addition, recent research has shown that the diagnostic process is not only an embodiment of individual characteristics but also adds a unique contribution to diagnostic outcome (Fink et al., 2023 ). However, it remains difficult to assess and foster CDAs.

Collaboration in Knowledge-Rich Domains: Agent-Based Simulations

There are several challenges when it comes to modelling collaborative settings in knowledge-rich domains for both learning and research endeavors. First, many situations are not easily accessible, as they may be scarce (e.g., natural disasters) or too critical or overwhelming to be approached by novices (e.g., some medical procedures). In these cases, the use of simulation-based environments allows authentic situations approximating real-life diagnostic problems to be provided (Cook et al., 2013 ; Heitzmann et al., 2019 ). Further, the use of technology-enhanced simulations allows data from the ongoing CDR process to be collected in log files. This enables researchers to analyze process data without the need for additional assessments with dedicated tests. Analyzing process data instead of only product data (the assessment’s outcome) permits insights into the problem-solving processes leading to the eventual outcome (e.g., Goldhammer et al., 2017 ). Second, when using human-to-human collaboration, the results of one individual are typically influenced by factors such as group composition or motivation of the collaboration partner (Radkowitsch et al., 2022 ). However, we understand CDR as an individual set of skills enabling collaboration, as indicated by the broader definition of collaborative problem-solving (OECD, 2017 ). Thus, the use of simulated agents as collaboration partners allows a standardized and controlled setting to be created that would otherwise be hard to establish in collaborations among humans (Rosen, 2015 ). There is initial research showing that performance in simulations using computerized agents is moderately related to collaborative skills in other operationalizations (Stadler & Herborn et al., 2020 ). Thus, computerized agents allow for enhanced control over the collaborative process without significantly diverging from human-to-human interaction (Graesser et al., 2018 ; Herborn et al., 2020 ). Third, in less controlled settings it is hard to ensure a specific process is taking place during collaborative problem-solving. For example, when using a human-to-human setting, it is possible that, even though we envision measuring or fostering a specific activity (i.e. hypotheses sharing), it is not performed by the student. Through using an agent-based simulated collaboration partner, we can ensure that all required processes are taking place while solving the problem (Rosen, 2015 ).

Summarizing, by fostering a consistent and controlled setting, simulated agents facilitate the accurate measurement and enhancement of collaborative problem-solving. Evidential support for the application of simulated agents spans a variety of contexts, including tutoring, collaborative learning, knowledge co-construction, and collaborative problem-solving itself, emphasizing their versatility and effectiveness in educational settings (Graesser et al., 2018 ; Rosen, 2015 ).

Research Question and Current Study

In computer-supported collaborative learning there has been the distinction between approaches addressing collaboration to learn and approaches focusing on learning to collaborate. Our study is best understood as addressing the second approach, learning to collaborate. We want to better understand CDR to be able to facilitate collaborative problem-solving skills in learners. Thus, in this paper, we examine what it takes to be able to collaborate in professional practice of knowledge-rich domains, such as medical diagnosing.

When solving diagnostic problems, such as diagnosing a patient, it is often necessary to collaborate with experts from different fields (Radkowitsch et al., 2022 ). In CDR, the diagnostic outcome depends on effectively eliciting and sharing relevant evidence and hypotheses among collaborators, who often lack information-sharing skills (Tschan et al., 2009 ). Thus, the CDR model emphasizes the importance of high-quality CDAs influenced by content and collaboration knowledge as well as social skills to achieve accurate, justified, and efficient diagnostic outcomes (Radkowitsch et al., 2022 ).

This study reviews the relationships postulated in CDR model across three studies to test them empirically and investigate the extent to which the relationships in the CDR model are applicable across studies . By addressing this research question, the current study contributes to a better understanding of the underlying processes in collaborative problem-solving.

We derived a model (Fig. 1 ) from the postulated relationships made by the CDR model. We assume that the individual characteristics are positively related to the CDAs (Hypotheses 1–3), as well as that the CDAs are positively related to the diagnostic outcome (Hypotheses 4–6). Further, we expect that the relationship between the individual characteristics and the diagnostic outcome is partially mediated by the CDAs (Hypotheses 7–15).

Visualization of hypothesized relationships between individual characteristics, collaborative diagnostic activities, and diagnostic outcome

We used data from three studies with similar samples and tasks investigating CDR in an agent-based simulation in the medical domain. The studies can therefore be considered conceptual replication studies. Furthermore, we decided to use an agent-based simulation of a typical collaboration setting in diagnostic reasoning, namely the interdisciplinary collaboration between an internist and a radiologist (Radkowitsch et al., 2022 ).

To test the hypotheses, three studies were analyzed. Footnote 1 Study A was carried out in a laboratory setting in 2019 and included medical students in their third to sixth years. Study B included medical students in their fifth to sixth years. Data collection for this study was online due to the pandemic situation in 2020 and 2021. In both studies, participation was voluntary, and participants were paid 10 per hour. Study C was embedded as an online session in the curriculum of the third year of medical school in 2022. Participation was mandatory, but permission to use the data for research purposes was given voluntarily. All participants took part in only one of the three studies. All three studies received ethical approval from LMU Munich (approval numbers 18-261, 18-262 & 22-0436). For a sample description of each study, see Table 1 . We would like to emphasize that none of the students were specializing in internal medicine, ensuring that the study results reflect the competencies of regular medical students without specialized expertise.

Each of the three studies was organized in the same way, with participants first completing a pretest that included a prior knowledge test, socio-demographic questions, and questions about individual motivational-affective characteristics (e.g., social skills, interest, and motivation). Participants then moved on to the CDR simulation and worked on the patient case. The patient case was the same for studies B and C, but was different for study A. The complexity and difficulty of the patient case did not vary systematically between the patient cases.

Simulation and Task

In the CDR simulation, which is also used as a learning environment, the task was to take over the role of an internist and to collaborate with an agent-based radiologist to obtain further information by performing radiological examinations to diagnose fictitious patient cases with the chief symptom of fever. Medical experts from internal medicine, radiology, and general medicine constructed the patient cases. Each case was structured in the same way: by studying the medical record individually, then collaborating with an agent-based radiologist, and finally reporting the final diagnosis and its justification again individually. For a detailed description on the development and validation of the simulation, see Radkowitsch and colleagues ( 2020 ).

Before working within the simulation, participants were presented with an instruction for the simulated scenario and informed what they were to do with it. Then, we instructed participants how to access further information in the medical record by clicking on hyperlinks, as well as how they could use the toolbar to make notes for the later in the process. Furthermore, we acquainted the students with how they could request further information through collaborating with a radiologist.

During the collaboration with an agent-based radiologist, participants were asked to fill out request forms to obtain further evidence from radiological examinations needed to diagnose the patient case. To effectively collaborate with radiologists, it is crucial for internists to clearly communicate the type of evidence required to reduce uncertainty (referred to as “evidence elicitation”) and share any relevant patient information such as signs, symptoms, and medical history (referred to as “evidence sharing”) as well as suspected diagnoses under investigation (referred to as “hypotheses sharing”) that may impact the radiologists’ diagnostic process. Only when participants shared evidence and hypotheses appropriately for their requested examination did they receive a description and evaluation of the radiologist’s radiologic findings. What was considered appropriate was determined by medical experts for each case and examination in preparation of the cases. Therefore, this scenario involves more than a simple division of tasks, as the quality of one person’s activity (i.e., description and evaluation of the radiologic findings) depends on the collaborative efforts (i.e., CDAs) of the other person (OECD, 2017 )

Measures—Individual Characteristics

The individual characteristics were measured in the pretest. The internal consistencies of each measure per study are displayed in Table 4 in the Results section. We want to point out that the internal consistency of knowledge as a construct—determined by the intercorrelations among knowledge pieces—typically exhibits a moderate level. Importantly, recent research argues that a moderate level of internal consistency does not undermine the constructs’ capacity to explain a significant amount of variance (Edelsbrunner, 2024 ; Stadler et al., 2021 ; Taber, 2018 ).

Content knowledge was separated into radiology and internal medicine knowledge, as these two disciplines play a major role in the diagnosis of the simulated patient cases. For each discipline, conceptual and strategic knowledge was assessed (Kiesewetter et al., 2020 ; Stark et al., 2011 ). The items in each construct were presented in a randomized way in each study. However, the items for study C were shortened due to the embedding of the data collection in the curriculum. Therefore, items with a very high or low item difficulty in previous studies were excluded (Table 2 ).

Conceptual knowledge was measured using single-choice questions including five options adapted from a database of examination questions from the Medical Faculty of the LMU Munich, focusing on relevant and closely related diagnoses of the patient cases used in the simulation. A mean score of 0–1 was calculated, representing the percentage of correct answers and indicating the average conceptual knowledge of the participant per medical knowledge domain.

Strategic content knowledge was measured contextually using key features questions (M. R. Fischer et al., 2005 ). Short cases were introduced followed by two to three follow up questions (e.g., What is your most likely suspected diagnosis?, What is your next examination?, What treatment do you choose?). Each question had eight possible answers, from which the learners were asked to choose one. Again, a mean score of 0–1 was calculated, representing the percentage of correct responses, indicating the average strategic content knowledge of the participant per domain.

The measure of collaboration knowledge was consistent across the three studies and specific to the simulated task. Participants were asked to select all relevant information for seven different patient cases with the cardinal symptom of fever (internal medicine). The patient cases were presented in a randomized order and always included 12 pieces of information regarding the chief complaints, medical history, and physical examination of the patient cases. We then assessed whether each piece of information was shared correctly (i.e. whether relevant information was shared and irrelevant information was not shared) and assigned 1 point and divided it by the maximum of 12 points to standardized the range of measure to 0–1. Then we calculated a mean score for each case and then across all cases, resulting in a range of 0–1 indicating the participants’ collaboration knowledge

The construct of social skills was consistent across the three data collections and was measured on the basis of self-report on a 6-point Likert scale ranging from total disagreement to total agreement. The construct was measured using 23 questions divided into five subscales; for example items, see Table 3 . Five questions aimed to measure the overall construct, and the other four subscales were identified using the complex problem-solving frameworks of Liu et al. ( 2016 ) and Hesse et al. ( 2015 ): perspective taking (four questions), information sharing (five questions), negotiation (four questions), and coordination (five questions). For the final score, the mean of all subcategories was calculated, ranging from 1 to 6, representing general social skills.

Measures—Collaborative Diagnostic Activities (CDA)

We operationalize CDAs in the pretest case in terms of quality of evidence elicitation, evidence sharing, and hypotheses sharing. The internal consistencies of each measure per study are displayed in Table 4 in the Results section.

The quality of evidence elicitation was measured by assessing the appropriateness of the requested radiological examination for the indicated diagnosis. An expert solution was developed to indicate which radiological examinations were appropriate for each of the possible diagnoses. If participants requested an appropriate radiological examination for the indicated diagnoses, they received 1 point for that request attempt. Finally, a mean score across all request attempts (maximum of 3) was calculated and scored. The final mean score was transformed into a binary indicator, with 1 indicating that all requested radiological examinations were appropriated and 0 indicating that inappropriate radiological examinations were also requested, due to the categorical nature of the original data and its skewed distribution, with a majority of responses concentrated in a single category.

The quality of evidence sharing was measured using a precision indicator. This was calculated as the proportion of shared relevant evidence out of all shared evidence. Relevant evidence is defined per case and per diagnosis and indicated by the expert solution. The precision indicator was first calculated per radiological request. We then calculated the mean score, summarizing all attempts in that patient case. This resulted in a range from 0 points, indicating that only irrelevant evidence was shared, to 1 point, indicating that only relevant evidence was shared.

The quality of hypotheses sharing was also measured using a precision indicator. For each patient case, the proportion of diagnoses relevant for the respective patient case to all shared diagnoses was calculated. Which diagnoses were considered relevant for a specific case was determined by an expert solution. As with evidence elicitation, this score was evaluated and converted into a binary variable, where 1 indicated that only relevant diagnoses were shared and 0 indicated that also irrelevant diagnoses were shared, due to the categorical nature of the original data and its skewed distribution, with a majority of responses concentrated in a single category.

Measures—Diagnostic Outcome

We operationalize diagnostic outcome in the pretest case in terms of diagnostic accuracy, diagnostic justification, and diagnostic efficiency.

For diagnostic accuracy, a main diagnosis was assigned to each patient case as expert solution. After working on the patient case and requesting the radiological examination, participants indicated their final diagnosis. To do this, they typed in the first three letters of their desired diagnosis and then received suggestions from a list of 249 possible diagnoses. Diagnostic accuracy was then calculated by coding the agreement between the final diagnosis given and the expert solution. Accurate diagnoses (e.g., hospital-acquired pneumonia) were coded as 1, correct but inaccurate diagnoses (e.g., pneumonia) were coded as 0.5, and incorrect diagnoses were coded as 0. A binary indicator was used for the final diagnostic accuracy score, with 0 indicating an incorrect diagnosis and 1 indicating an at least inaccurate diagnosis, due to the categorical nature of the original data and its skewed distribution, with a majority of responses concentrated in a single category.

A prerequisite for diagnostic justification and diagnostic efficiency is the provision of at least an inaccurate diagnosis. If a participant provided an incorrect diagnosis (coded as 0), diagnostic justification and diagnostic efficiency were immediately scored as 0.

After choosing a final diagnosis, participants were asked to justify their decision in an open text field. Diagnostic justification was then calculated as the proportion of relevant reported information out of all relevant information that would have fully justified the final accurate diagnosis. Again, medical experts agreed on an expert solution that included all relevant information to justify the correct diagnosis. The participants’ solution was coded by two independent coders, each coding the full data, and differences in coding were discussed until the coders agreed. We obtained a range from 0 points, indicating a completely inadequate justification, to 1 point, indicating a completely adequately justified final diagnosis.

Diagnostic efficiency was defined as diagnostic accuracy (non-binary version) divided by the minutes required to solve the case.

Statistical Analyses

To answer the research question, a structural equation model (SEM) was estimated using MPlus Editor, version 8 (Muthén & Muthén, 2017 ). We decided to use a SEM, as it is a comprehensive statistical approach widely used in psychology and educational sciences for its ability to model complex relationships among observed and latent variables while accounting for measurement error (Hilbert & Stadler, 2017 ). SEM support the development and verification of theoretical models, enabling scholars to refine theories and interventions in psychology and education based on empirical evidence, as not only can one relationship be investigated but a system of regressions is also considered simultaneously (Nachtigall et al., 2003 ).

We included all links between the variables and applied a two-step approach, using mean-adjusted and variance-adjusted unweighted least squares (ULSMV, Savalei & Rhemtulla, 2013 ) as the estimator and THETA for parametrization, first examining the measurement model and then the structural model. The assessment of model fit was based on chi-square (χ2), root mean square error of approximation (RMSEA), and comparative fit index (CFI). Model fit is generally indicated by small chi-squared values; RMSEA values of < 0.08 (acceptable) and < 0.06 (excellent), and CFI values ≥ 0.90. We do not consider standardized root mean squared residual (SRMR), because, according to the definition used in MPlus, this index is not appropriate when the sample size is 200 or less, as natural variation in such small samples contributes to larger SRMR values (Asparouhov & Muthén, 2018 ). For hypotheses 1–6, we excluded path coefficients < 0.1 from our interpretation, as they are relatively small. In addition, at least two interpretable path coefficients, of which at least one is statistically significant, are required to find support for the hypothesis. For hypotheses 7–15, specific indirect effects (effect of an individual characteristic on diagnostic outcome through a specific CDA) and total indirect effects (mediation of the effect of an individual characteristic on diagnostic outcome through all mediators) were estimated.

We reported all measures in the study and outlined differences between the three samples. All data and analysis code have been made publicly available at the Open Science Framework (OSF) and can be accessed at https://osf.io/u8t62 . Materials for this study are available by email through the corresponding author. This study’s design and its analysis were not pre-registered.

The descriptive statistics of each measure per study are displayed in Table 4 . The intercorrelations between the measures per study can be found in Appendix Table 7 .

Overall Results of the SEM

All loadings were in the expected directions and statistically significant, except for conceptual knowledge in internal medicine in study C (λ = 0.241, p  = .120), conceptual knowledge in radiology in study A (λ = 0.398, p  = .018), and strategic knowledge in internal medicine (λ = 0.387, p  = .206) and radiology (λ = -0.166, p  = .302) in study B. Standardized factor loadings of the measurement model are shown in Appendix Table 8 .

The SEM has a good fit for study A [ X 2 (75) = 74.086, p = .508, RMSEA = 0.00, CFI = 1.00], study B [ X 2 (75) = 68.309, p  = .695, RMSEA = 0.000, CFI = 1.00], and study C [ X 2 (75) = 93.816, p  = .070, RMSEA = 0.036, CFI = 1.00].

Paths between Individual Characteristics, CDAs, and Diagnostic Outcome

The standardized path coefficients and hypotheses tests for the theoretical model are reported in Table 5 . An overview of the paths supported by the data is shown in Fig. 2 .

Evidence on supported relationships between individual characteristics, collaborative diagnostic activities, and diagnostic outcome

Overall, the R 2 for the CDAs ranged from medium to high for evidence elicitation and evidence sharing, depending on the study, and were consistently low for hypotheses sharing across all three studies. Looking at diagnostic outcome, R 2 is consistently large for diagnostic accuracy and medium to large for diagnostic justification and diagnostic efficiency (Table 6 ).

The path from content knowledge to evidence elicitation was positive and > 0.1 in all three studies, as well as statistically significant in two of them; therefore, we consider Hypothesis 1a supported. The path from content knowledge to evidence sharing was positive and > 0.1 in two studies, as well as statistically significant in one of them; therefore, Hypothesis 1b is also supported. In contrast, the path from content knowledge to hypotheses sharing was indeed also positive in two studies, but as neither was statistically significant, we conclude that Hypothesis 1c was not supported. The path from collaboration knowledge to evidence elicitation was positive and > 0.1 in only one study, but also not statistically significant. Thus, we found that Hypothesis 2a was not supported. For the path from collaboration knowledge to evidence sharing, we found relevant positive and statistically significant coefficients in all three studies. Hypothesis 2b is therefore fully supported by the data. This is not the case for Hypothesis 2c, for which we found no coefficient > 0.1 for the path from collaboration knowledge to hypotheses sharing. For the path from social skills to evidence elicitation, we found positive coefficients > 0.1 in two out of three studies, of which one was also statistically significant. Thus, we consider Hypothesis 3a to be supported. For the path from social skills to evidence sharing, we again found one statistically significant positive coefficient, but in the other two studies it was < 0.1. Therefore, we do not consider Hypothesis 3b to be supported by the data. The same applies to the path from social skills to hypotheses sharing, where the coefficient is < 0.1 in two studies. We therefore do not consider Hypothesis 3c to be supported.

The path from evidence elicitation to diagnostic accuracy was statistically significant and large in magnitude in two out of three studies. Hypothesis 4a is therefore supported. The path from evidence elicitation to diagnostic justification was only positive and > 0.1 in one study, which was also not statistically significant. Therefore, we find no support for Hypothesis 4b. In contrast, the path from evidence elicitation to diagnostic efficiency was positive and statistically significant in two out of three studies, with one large effect. Hypothesis 4c is therefore supported. The path from evidence sharing to diagnostic accuracy was only positive and reasonably large in one study. Therefore, we do not find support for Hypothesis 5a. The path from evidence sharing to diagnostic justification was positive and > 0.1 in two studies as well as statistically significant in one of them, so Hypothesis 5b is supported. In contrast, we did not find a positive coefficient > 0.1 for the path from evidence sharing to diagnostic efficiency. Therefore, Hypothesis 5c is not supported by the data. Although we found coefficients > 0.1 in two studies for the path from hypotheses sharing to diagnostic accuracy, we found no support for Hypothesis 6a, as none of these was statistically significant. This is different for Hypothesis 6b, as we found two positive paths from hypotheses sharing to diagnostic justification, one of which was statistically significant and large. Finally, we found two positive paths from evidence sharing to diagnostic efficiency in three studies, one of which was statistically significant. Hypothesis 6c is therefore supported.

Indirect Effects between Individual Characteristics, CDA, and Diagnostic Outcome

Indirect effects of CDAs on the effect of individual characteristics on the diagnostic outcome in CDR were estimated to test hypotheses 7–15. Although we found a mediating effect of all CDAs (β = .31, p = .008), and specifically for evidence elicitation (β = .27, p = .021) from content knowledge on diagnostic accuracy in study C, and some significant overall and direct effects for other relationships (Appendix Table 9 ), none of these were consistent across all of the studies. Thus, we conclude no consistent support for any of the Hypotheses 7–15.

The aim of the current study was to investigate the extent to which the relationships specified in the CDR model (Radkowitsch et al., 2022 ) are applicable across studies, to better understand the processes underlying CDR in knowledge-rich domains. Not only is this exploration crucial for the medical field or collaborative problem-solving in knowledge-rich domains, but it also offers valuable insights for computer-supported collaborative learning research. Despite CDR’s specific focus, the principles and findings have relevant implications for understanding and enhancing collaborative processes in various educational and professional settings.

Specifically, we investigated how individual learner characteristics, the CDAs, and the diagnostic outcome are related. We therefore analyzed data from three independent studies, all from the same context, a simulation-based environment in the medical domain. Our study found positive relationships between content knowledge and the quality of evidence elicitation as well as the quality of evidence sharing, but not for the quality of hypotheses sharing. Furthermore, collaboration knowledge is positively related to the quality of evidence sharing, but not to the quality of evidence elicitation and the quality of hypotheses sharing. Social skills are only positively related to the quality of evidence elicitation. This underscores the multifaceted nature of collaborative problem-solving situations. Thus, effective CDR, a form of collaborative problem-solving, necessitates a nuanced understanding of the interplay between individual characteristics and CDAs.

The relevance of content knowledge for diagnostic competence is well established in research (Chernikova et al., 2020 ). To develop any diagnostic skills in knowledge-rich domains, learners need to acquire large amounts of knowledge and to restructure it through experience with problem-solving procedures and routines (Boshuizen et al., 2020 ). In the case of CDR this enables the diagnostician to come up with an initial suspected diagnosis, which is likely to be relevant information for the collaboration partner and to guide the further CDAs effectively. The finding that content knowledge only has a relation to the quality of evidence elicitation but none of the other CDAs can be explained by the fact that evidence elicitation is the least transactive CDA within the collaborative decision-making process. When eliciting evidence, the collaboration partner is used as an external knowledge resource (Weinberger & Fischer, 2006 ). So, despite being a collaborative activity, evidence elicitation is about what information from the collaboration partner is needed rather than what the collaboration partner needs. Thus, elicitation is less transactive than sharing, which is focused at what the collaboration partner needs.

Not only content knowledge but also collaboration knowledge is related to the quality of evidence sharing. This finding implies that collaboration knowledge may influence the CDR above and beyond individual content knowledge. It also supports the differentiation of knowledge types made in the CDR model (Radkowitsch et al., 2022 ). Thus, it is important to learn not only the conceptual and strategic medical knowledge that is required for diagnosing but also knowledge about what information is relevant for specific collaboration partners when diagnosing collaboratively. This finding underpins the importance of being aware of the knowledge distribution among collaboration partners and the relevance of the transactive memory (Wegner, 1987 ). Thus, for collaborative problem-solving in knowledge-rich domains—as for computer-supported collaborative learning more generally—knowledge and information awareness is crucial (Engelmann & Hesse, 2010 ).

Thus, the relevance of collaboration knowledge in collaborative problem-solving is an important finding of our study, highlighting that it is critical in facilitating effective collaborative processes and outcomes. The current findings emphasize the need for educational strategies that explicitly target the development of collaborative knowledge to ensure that learners have the knowledge and skills necessary to participate in productive collaborative problem-solving and computer-supported collaborative learning processes. In doing so, the CDR model emphasizes the need for learners to master collaborative skills and build shared problem representations to take full advantage of collaborative learning opportunities.

As CDR is conceptualized to be an interplay of cognitive and social skills (Hesse et al., 2015 ), we also assumed that social skills are related to CDAs. However, we only found evidence of the expected relationship between social skills and CDAs for the quality of evidence elicitation. One explanation could be that collaboration knowledge was relatively high in all three samples, outweighing the influences of general skills. This is consistent with the assumption of the CDR model that the influence of more general social skills is reduced with an increasing level of professional collaboration knowledge (Radkowitsch et al., 2022 ). When collaboration knowledge is available to the diagnosticians, it becomes more important than social skills. This finding again underlines the importance of collaboration knowledge, which can be seen as a domain- and profession-specific development of social skills. However, another explanation could be that, when collaborating with an agent, the effect of social skills decreases, as the agent was not programmed to respond to social nuances. The design of the simulation would thus buffer against the effect of social skills. Although the study by Herborn et al. ( 2020 ) found no differences between human-to-human and human-to-agent collaboration, this does not necessarily invalidate the potential variability in outcomes associated with the social skills incorporated into the agent. For a thorough investigation into the impact of social skills, the agent would need variable social abilities, enabling the variation of the importance of basic social skills for successful collaboration.

Further, we need to conclude that there is no support for a relationship between the individual characteristics and hypotheses sharing, as we found no stable support for the relationship between any of the individual characteristics and the quality of hypotheses sharing. One possible explanation could be that the binary precision measure used to operationalize quality in hypotheses sharing is not sensitive enough or is not capturing the relevant aspect of quality in that activity. Another explanation could be that there is no direct relationship between the individual characteristics and hypotheses sharing, as this relationship is mediated by evidence sharing and thus influenced by the activated knowledge scripts (Schmidt & Rikers, 2007 ).

Looking at the relationships between CDAs and the diagnostic outcome, the current results highlight the need to distinguish between primary (diagnostic accuracy) and secondary (diagnostic justification and efficiency) outcomes of diagnostic reasoning (Daniel et al., 2019 ). Achieving diagnostic accuracy, a purely quantitative outcome measure, is less transactive than other aspects of the diagnostic outcome. This is also where we find the link to evidence elicitation, as we consider this to be the least transactive CDA within the collaborative decision-making process. However, the ability to justify and reach this decision efficiently is then highly dependent on evidence sharing and hypotheses sharing, activities that are more focused on transactivity within CDR (Weinberger & Fischer, 2006 ).

Although individual learner characteristics are found to have an effect on CDAs, and CDAs impact the diagnostic outcome, the effect is not mediated by CDAs across studies. Thus, we assume that, for effective collaborative problem-solving in knowledge-rich domains, such as CDR, it is not enough to have sufficient content and collaboration knowledge; it is also necessary to be able to engage in high quality CDAs to achieve a high-quality diagnostic outcome. This is consistent with research on individual diagnostic reasoning, which shows that diagnostic activities have a unique contribution to the diagnostic outcome after controlling for content knowledge (Fink et al., 2023 ).

In summary, we explored evidence elicitation, evidence sharing, and hypotheses sharing as crucial CDAs. The findings revealed diverse associations of these CDAs with individual characteristics and facets of the diagnostic outcome, supporting the notion that the CDR-process involves a variety of different skills (instead of being one overarching skill). On the basis of these results, we propose categorizing CDAs into activities primarily focused on individual goals and needs (e.g., elicitation) and more transactive activities directly targeted at the collaborator (e.g., sharing). To enhance quality in CDAs, instructional support should be considered. For instance, providing learners with an adaptive collaboration script has been shown to improve evidence sharing quality and promote the internalization of collaboration scripts, fostering the development of collaboration knowledge (Radkowitsch et al., 2021 ). Further, group awareness tools, such as shared concept maps, should be considered to compensate for deficits in one’s collaboration knowledge (Engelmann & Hesse, 2010 ). However, what is required to engage in high-quality CDAs remains an open question. One starting point is domain-general cognitive skills. These could influence CDAs, particularly in the early stages of skill development (Hetmanek et al., 2018 ). Previous research showed that, in diagnostic reasoning, instructional support is more beneficial when being domain-specific than domain-general (Schons et al., 2022 ). Thus, there is still a need for further research on how such instructional support might look like.

Future Research

Although we used data from three studies, all of them were in the same domain; thus, it remains an open question whether these findings are applicable across domains. The CDR model claims that the described relationships are not limited to the medical domain, but rather are valid across domains for collaboratively solving complex problems in knowledge-rich domains. Future research should explore generalizability, for example, for teacher education, which is a distinct field that also requires diagnosing and complex problem-solving (Heitzmann et al., 2019 ).

Regardless of domain, the non-mediating relationship of CDAs between individual characteristics and diagnostic outcomes, as well as the found effects of the CDAs in the current study, suggests that an isolated analysis of CDAs does not fully represent the complex interactions and relationships among activities, individual characteristics, and diagnostic outcomes. Future studies might assess CDAs as a bundle of necessary activities, including a focus on their possible non-linear interactions. We propose to use process data analysis to account for the inherent complexity of the data, as different activities in different sequences can lead to the same outcome (Y. Chen et al., 2019 ). More exploratory analyses of fine-grained, theory-based sequence data are needed to provide insights into more general and more specific processes involved in successful solving complex problems collaboratively (Stadler et al., 2020 ).

As our results have shown, collaboration knowledge and thus awareness of the knowledge distribution among collaboration partners is highly relevant. While a recent meta-analyses showed a moderate effect of group awareness of students’ performance in computer-supported collaborative learning (D. Chen et al., 2024 ), it has so far not been systematically investigated in collaborative problem-solving. Thus, more research on the influence collaboration knowledge in collaborative problem-solving is needed.

Further, additional factors associated with success in collaborative problem-solving—not yet incorporated into the model and thus not yet investigated systematically—include communication skills (OECD, 2017 ), the self-concept of problem-solving ability (Scalise et al., 2016 ), and positive activating emotions during problem-solving tasks (Camacho-Morles et al., 2019 ).

Limitations

There are, however, some limitations to be considered. One is that we have only considered CDAs and how they relate to individual characteristics and outcomes. However, the CDR model also introduces individual diagnostic activities, such as the generation of evidence and the drawing of conclusions. These occur before and after the CDAs and may therefore also have an impact on the described relationships. However, we decided to focus on the CDAs within the CDR process because they are particularly relevant for constructing a shared problem representation, being central to CDR. Future research might consider these individual diagnostic activities, as they could, for example, further explain the how content knowledge is related to the diagnostic outcome.

Another limitation of the current analyses is the operationalization of quality for the CDAs. We chose the appropriateness of radiological examination for the indicated diagnosis for quality of evidence elicitation and precision for quality of evidence sharing and hypotheses sharing. However, all of these only shed light on one perspective of each activity, while possibly obscuring others. For example, it may be that content knowledge is not related to the precision of hypotheses sharing, but this may be different when looking at other quality indicators, such as sensitivity or specificity. However, we decided to use the precision aspect of activities, as research shows that collaborators often fail to identify relevant information, and the amount of information is not related to performance (Tschan et al., 2009 ). Future research may explore a broader variety of quality indicators to be able to assess the quality of CDAs as comprehensively as possible. It should also be noted that in study B a suppression effect (Horst, 1941 ) between hypothesis sharing and evidence elicitation artificially inflated the observed effect size. This is to be expected with process data that can be highly correlated and needs to be considered when interpreting the effect sizes.

In addition, it should be noted that the omega values obtained for the conceptual and strategic knowledge measures were below the commonly accepted threshold of 0.7. While we chose to use omega values as a more appropriate measure of reliability in our context, given the complex and multifaceted nature of the knowledge constructs, these lower-than-expected values raise important questions about the quality of the data and the robustness of the findings. Thus, it is important to understand that knowledge constructs, by their very nature, may not always exhibit high levels of internal consistency due to the diverse and interrelated components they encompass (Edelsbrunner, 2024 ; Stadler et al., 2021 ; Taber, 2018 ). This complexity may be reflected in the moderate omega values observed, which, while seemingly counterintuitive, does not invalidate the potential of the constructs to account for substantial variance in related outcomes. However, findings related to these constructs should be interpreted with caution, and the results presented should be considered tentative. Future research should further explore the implications of using different reliability coefficients in assessing complex constructs within the learning sciences, potentially providing deeper insights into the nuanced nature of knowledge and its measurement.

Another limitation of this study is related to the agent-based collaboration, as a predictive validation of collaborative problem-solving for later human-to-human collaboration in comparable contexts has not yet been systematically conducted. Although the agent-based collaboration situation used has been validated in terms of perceived authenticity, it still does not fully correspond to a real collaboration situation (Rosen, 2015 ). This could be an explanation for the low influence of social skills, as the setting might not require the application of a broad set of social skills (Hesse et al., 2015 ; Radkowitsch et al., 2020 ). In a real-life collaboration, the effects of social skills might be more pronounced. However, research showed that the human-to-agent approach did not lead to different results in collaborative problem-solving than the human-to-human approach in the 2015 PISA study, and correlations with other measures of collaborative skills have been found (Herborn et al., 2020 ; Stadler, Herborn et al., 2020 ). Future studies should specifically test the relevance of social skills for CDR in a human-to-human setting to strengthen the generalizability of our findings.

In conclusion, the current study highlights the importance of individual characteristics and CDAs as independent predictors for achieving good diagnoses in collaborative contexts, at least in the simulation-based settings we used in the studies included in our analysis. Collaboration knowledge emerged as a critical factor, demonstrating its importance over early acquired, general social skills. Therefore, it is imperative to revise the CDR approach by giving higher priority to the proficiency of collaboration knowledge compared with social skills. Furthermore, we conclude that, in simulation-based CDR, content knowledge does not play such a crucial role in predicting diagnostic success compared with many other educational settings, most probably because of the endless opportunities for retrying and revising in simulation-based learning environments.

With respect to CDAs, we suggest refining the perspective on the quality of CDAs and consider revising the CDR model by summarizing CDAs as information elicitation and information sharing, with the former being less transactive, and thus, less demanding than the latter. Adequate performance in both types of CDA is presumed to result in a high-quality shared problem representation, resulting in good diagnostic outcome. Collaborative problem-solving skills are highly relevant in professional practice of knowledge-rich domains, highlighting the need to strengthen these skills in students engaged in CDR and to provide learning opportunities accordingly. Further, the ability to effectively collaborate and construct shared problem representations is important, not only in CDR but also in collaborative problem-solving and computer-supported collaborative learning more in general, highlighting the need for integrating such skills into curricula and instructional design.

By emphasizing these aspects, we can improve the diagnostic skills of individuals in collaborative settings. Through advancing our understanding of CDR, we are taking a key step forward in optimizing collaborative problem-solving and ultimately contributing to improved diagnostic outcomes in various professional domains beyond CDR in medical education. In particular, integrating collaboration knowledge and skills into computer-supported collaborative learning environments can enrich learning experiences and outcomes in various knowledge-rich domains.

Please note that the data employed in this study have been used in previous publications (e.g., Brandl et al., 2021 ; Radkowitsch, et al., 2021 ; Richters et al., 2022 ). However, the research question and the results reported in this study are completely unique to this study.

Abele, S. (2018). Diagnostic problem-solving process in professional contexts: theory and empirical investigation in the context of car mechatronics using computer-generated log-files. Vocations and Learning, 11 (1), 133–159. https://doi.org/10.1007/s12186-017-9183-x

Article   Google Scholar  

Asparouhov, T., & Muthe´n, B. (2018). SRMR in Mplus . https://www.statmodel.com/download/SRMR2.pdf

Bauer, E., Sailer, M., Kiesewetter, J., Fischer, M. R., & Fischer, F. (2022). Diagnostic argumentation in teacher education: Making the case for justification, disconfirmation, and transparency. Frontiers in Education , 7 , Article 977631. https://doi.org/10.3389/feduc.2022.977631

Boshuizen, H. P., Gruber, H., & Strasser, J. (2020). Knowledge restructuring through case processing: the key to generalise expertise development theory across domains? Educational Research Review, 29 , 100310. https://doi.org/10.1016/j.edurev.2020.100310

Brandl, L., Richters, C., Radkowitsch, A., Obersteiner, A., Fischer, M. R., Schmidmaier, R., Fischer, F., & Stadler, M. (2021). Simulation-based learning of complex skills: Predicting performance with theoretically derived process features. Psychological Test and Assessment Modeling, 63 (4), 542–560. https://www.psychologie-aktuell.com/fileadmin/Redaktion/Journale/ptam-2021-4/PTAM__4-2021_6_kor.pdf

Braun, L. T., Zottmann, J. M., Adolf, C., Lottspeich, C., Then, C., Wirth, S., Fischer, M. R., & Schmidmaier, R. (2017). Representation scaffolds improve diagnostic efficiency in medical students. Medical Education, 51 (11), 1118–1126. https://doi.org/10.1111/medu.13355

Camacho-Morles, J., Slemp, G. R., Oades, L. G., Morrish, L., & Scoular, C. (2019). The role of achievement emotions in the collaborative problem-solving performance of adolescents. Learning and Individual Differences, 70 , 169–181. https://doi.org/10.1016/j.lindif.2019.02.005

Chen, D., Zhang, Y., Luo, H., Zhu, Z., Ma, J., & Lin, Y. (2024). Effects of group awareness support in CSCL on students’ learning performance: a three-level meta-analysis. International Journal of Computer-Supported Collaborative Learning, 19 (1), 97–129. https://doi.org/10.1007/s11412-024-09418-3

Chen, Y., Li, X., Liu, J., & Ying, Z. (2019). Statistical analysis of complex problem-solving process data: an event history analysis approach. Frontiers in Psychology , 10 , Article 486. https://doi.org/10.3389/fpsyg.2019.00486

Chernikova, O., Heitzmann, N., Fink, M. C., Timothy, V., Seidel, T., & Fischer, F. (2020). Facilitating diagnostic competences in higher education—a meta-analysis in medical and teacher education. Educational Psychology Review, 32 (1), 157–196. https://doi.org/10.1007/s10648-019-09492-2

Chernikova, O., Heitzmann, N., Opitz, A., Seidel, T., & Fischer, F. (2022). A theoretical framework for fostering diagnostic competences with simulations in higher education. In F. Fischer & A. Opitz (Eds.),  Learning to Diagnose with Simulations . Springer, Cham. https://doi.org/10.1007/978-3-030-89147-3_2

Cook, D. A., Brydges, R., Zendejas, B., Hamstra, S. J., & Hatala, R. M. (2013). Technology-enhanced simulation to assess health professionals: a systematic review of validity evidence, research methods, and reporting quality. Academic Medicine: Journal of the Association of American Medical Colleges, 88 (6), 872–883. https://doi.org/10.1097/ACM.0b013e31828ffdcf

Daniel, M., Rencic, J., Durning, S. J., Holmboe, E. S., Santen, S. A., Lang, V., Ratcliffe, T., Gordon, D., Heist, B., Lubarsky, S., Estrada, C. A., Ballard, T., Artino, A. R., Da Sergio Silva, A., Cleary, T., Stojan, J., & Gruppen, L. D. (2019). Clinical reasoning assessment methods: a scoping review and practical guidance. Academic Medicine: Journal of the Association of American Medical Colleges, 94 (6), 902–912. https://doi.org/10.1097/ACM.0000000000002618

Dunbar, K. (1995). How scientists really reason: scientific reasoning in real-world laboratories. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 365–395). MIT Press.

Google Scholar  

Edelsbrunner, P. A. (2024). Does interference between intuitive conceptions and scientific concepts produce reliable inter-individual differences? Science & Education. Advance online publication. https://doi.org/10.1007/s11191-024-00500-8

Book   Google Scholar  

Engelmann, T., & Hesse, F. W. (2010). How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 5 (3), 299–319. https://doi.org/10.1007/s11412-010-9089-1

Fink, M. C., Heitzmann, N., Reitmeier, V., Siebeck, M., Fischer, F., & Fischer, M. R. (2023). Diagnosing virtual patients: the interplay between knowledge and diagnostic activities. Advances in Health Sciences Education : Theory and Practice , 1–20. https://doi.org/10.1007/s10459-023-10211-4

Fiore, S. M., Graesser, A. C., & Greiff, S. (2018). Collaborative problem-solving education for the twenty-first-century workforce. Nature Human Behaviour, 2 (6), 367–369. https://doi.org/10.1038/s41562-018-0363-y

Fischer, F., Kollar, I., Stegmann, K., & Wecker, C. (2013). Toward a script theory of guidance in computer-supported collaborative learning. Educational Psychologist, 48 (1), 56–66. https://doi.org/10.1080/00461520.2012.748005

Fischer, F., Kollar, I., Ufer, S., Sodian, B., Hussmann, H., Pekrun, R., Neuhaus, B. J., Dorner, B., Pankofer, S., Fischer, M. R., Strijbos, J.‑W., Heene, M., & Eberle, J. (2014). Scientific reasoning and argumentation: advancing an interdisciplinary research agenda in education. Frontline Learning Research , 2 (3), 28–45. https://doi.org/10.14786/flr.v2i2.96

Fischer, M. R., Kopp, V., Holzer, M., Ruderich, F., & Jünger, J. (2005). A modified electronic key feature examination for undergraduate medical students: validation threats and opportunities. Medical Teacher, 27 (5), 450–455. https://doi.org/10.1080/01421590500078471

Förtsch, C., Sommerhoff, D., Fischer, F., Fischer, M. R., Girwidz, R., Obersteiner, A., Reiss, K., Stürmer, K., Siebeck, M., Schmidmaier, R., Seidel, T., Ufer, S., Wecker, C., & Neuhaus, B. J. (2018). Systematizing professional knowledge of medical doctors and teachers: development of an interdisciplinary framework in the context of diagnostic competences. Education Sciences, 8 (4), 207. https://doi.org/10.3390/educsci8040207

Goldhammer, F., Naumann, J., Rölke, H., Stelter, A., & Tóth, K. (2017). Relating product data to process data from computer-based competency assessment. In D. Leutner, J. Fleischer, J. Grünkorn, & E. Klieme (Eds.), Methodology of educational measurement and assessment. competence assessment in education (pp. 407–425). Springer International Publishing. https://doi.org/10.1007/978-3-319-50030-0_24

Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018). Advancing the science of collaborative problem solving. Psychological Science in the Public Interest: A Journal of the American Psychological Society, 19 (2), 59–92. https://doi.org/10.1177/1529100618808244

Hautz, W. E., Kämmer, J. E., Schauber, S. K., Spies, C. D., & Gaissmaier, W. (2015). Diagnostic performance by medical students working individually or in teams. JAMA, 313 (3), 303–304. https://doi.org/10.1001/jama.2014.15770

Heitzmann, N., Seidel, T., Hetmanek, A., Wecker, C., Fischer, M. R., Ufer, S., Schmidmaier, R., Neuhaus, B. J., Siebeck, M., Stürmer, K., Obersteiner, A., Reiss, K., Girwidz, R., Fischer, F., & Opitz, A. (2019). Facilitating diagnostic competences in simulations in higher education: a framework and a research agenda. Frontline Learning Research , 1–24. https://doi.org/10.14786/flr.v7i4.384

Herborn, K., Stadler, M., Mustafić, M., & Greiff, S. (2020). The assessment of collaborative problem solving in PISA 2015: can computer agents replace humans? Computers in Human Behavior, 104 , 105624. https://doi.org/10.1016/j.chb.2018.07.035

Hesse, F. W., Care, E., Buder, J., Sassenberg, K., & Griffin, P. (2015). A framework for teachable collaborative problem solving skills. In P. Griffin & E. Care (Eds.), Assessment and Teaching of 21 st Century Skills (pp. 37–56). Springer.

Chapter   Google Scholar  

Hetmanek, A., Engelmann, K., Opitz, A., & Fischer, F. (2018). Beyond intelligence and domain knowledge. In F. Frank, C. Clark A., E. Katharina, O. Jonathan, F. Fischer, C. A. Chinn, K. Engelmann, & J. Osborne (Eds.), Scientific reasoning and argumentation (pp. 203–226). Routledge. https://doi.org/10.4324/9780203731826-12

Hilbert, S., & Stadler, M. (2017). Structural equation models. In V. Zeigler-Hill & T. K. Shackelford (Eds.), Encyclopedia of Personality and Individual differences (pp. 1–9). Springer International Publishing. https://doi.org/10.1007/978-3-319-28099-8_1285-1

Hitchcock, D. (2005). Good reasoning on the Toulmin model. Argumentation, 19 (3), 373–391. https://doi.org/10.1007/s10503-005-4422-y

Horst, P. (1941). The prediction of personnel adjustment. Socia LScience Research and Council Bulletin  (48), 431–436.

Jeong, H., Hmelo-Silver, C. E., & Jo, K. (2019). Ten years of computer-supported collaborative learning: a meta-analysis of CSCL in STEM education during 2005–2014. Educational Research Review, 28 , 100284. https://doi.org/10.1016/j.edurev.2019.100284

Kiesewetter, J., Fischer, F., & Fischer, M. R. (2017). Collaborative clinical reasoning—a systematic review of empirical studies. The Journal of Continuing Education in the Health Professions, 37 (2), 123–128. https://doi.org/10.1097/CEH.0000000000000158

Kiesewetter, J., Sailer, M., Jung, V. M., Schönberger, R., Bauer, E., Zottmann, J. M., Hege, I., Zimmermann, H., Fischer, F., & Fischer, M. R. (2020). Learning clinical reasoning: how virtual patient case format and prior knowledge interact. BMC Medical Education, 20 (1), 73–83. https://doi.org/10.1186/s12909-020-1987-y

Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12 (1), 1–48. https://doi.org/10.1207/s15516709cog1201_1

Koschmann, T. D., Feltovich, P. J., Myers, A. C., & Barrows, H. S. (1992). Implications of CSCL for problem-based learning. ACM SIGCUE Outlook, 21 (3), 32–35. https://doi.org/10.1145/130893.130902

Liu, L., Hao, J., Davier, A. A. von, Kyllonen, P., & Zapata-Rivera, J.‑D. (2016). A tough nut to crack: measuring collaborative problem solving. In J. Keengwe, Y. Rosen, S. Ferrara, & M. Mosharraf (Eds.), Handbook of Research on Technology Tools for Real-World Skill Development (pp. 344–359). IGI Global. https://doi.org/10.4018/978-1-4666-9441-5.ch013

Muthén, L. K., & Muthén, B. O. (2017). Mplus: Statistical Analysis with Latent Variables: User’s Guide (Version 8) [Computer software]. Authors.

Nachtigall, C., Kroehne, U., Funke, F., & Steyer, R. (2003). (Why) should we use SEM? Pros and cons of structural equation modeling. Methods of Psychological Research Online, 8 (2), 1–22.

Noroozi, O., Biemans, H. J., Weinberger, A., Mulder, M., & Chizari, M. (2013). Scripting for construction of a transactive memory system in multidisciplinary CSCL environments. Learning and Instruction, 25 , 1–12. https://doi.org/10.1016/j.learninstruc.2012.10.002

OECD. (2017). PISA 2015 Assessment and Analytical Framework: Science, Reading, Mathematic, Financial Literacy and Collaborative Problem Solving, revised edition. PISA, OECD Publishing. https://doi.org/10.1787/9789264281820-en

Pickal, A. J., Engelmann, K., Chinn, C. A., Girwidz, R., Neuhaus, B. J., & Wecker, C. (2023). Fostering the collaborative diagnosis of cross-domain skills in video-based simulations. In Proceedings of the International Conference on Computer-supported for Collaborative Learning, Proceedings of the 16 th International Conference on Computer-Supported Collaborative Learning — CSCL 2023 (pp. 139–146). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2023.638463

Radkowitsch, A., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2020). Learning to diagnose collaboratively: Validating a simulation for medical students. GMS Journal for Medical Education, 37(5), Doc51. https://doi.org/10.3205/zma001344

Radkowitsch, A., Sailer, M., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2022). Diagnosing collaboratively: A theoretical model and a simulation-based learning environment. In F. Fischer & A. Opitz (Eds.),  Learning to Diagnose with Simulations . Springer, Cham. https://doi.org/10.1007/978-3-030-89147-3_10

Radkowitsch, A., Sailer, M., Schmidmaier, R., Fischer, M. R., & Fischer, F. (2021). Learning to diagnose collaboratively—effects of adaptive collaboration scripts in agent-based medical simulations. Learning and Instruction, 75 , 101487. https://doi.org/10.1016/j.learninstruc.2021.101487

Richters, C., Stadler, M., Radkowitsch, A., Behrmann, F., Weidenbusch, M., Fischer, M. R., Schmidmaier, R., & Fischer, F. (2022). Making the rich even richer? Interaction of structured reflection with prior knowledge in collaborative medical simulations. In A. Weinberger, W. Chen, D. Hernández-Leo, & B.Che (Chair), International Society of the Learning Sciences. Hiroshima, Japan.

Rochelle, J., & Teasley, S. (1995). The construction of shared knowledge in collaborative problem solving. In C. O’Malley (Ed.), Computer-Supported Collaborative Learning (pp. 66–97). Springer.

Rosen, Y. (2015). Computer-based assessment of collaborative problem solving: exploring the feasibility of human-to-agent approach. International Journal of Artificial Intelligence in Education, 25 (3), 380–406. https://doi.org/10.1007/s40593-015-0042-3

Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66 (2), 201–223. https://doi.org/10.1111/j.2044-8317.2012.02049.x

Scalise, K., Mustafic, M., & Greiff, S. (2016). Dispositions for collaborative problem solving. In S. Kuger, E. Klieme, N. Jude, & D. Kaplan (Eds.), Methodology of Educational Measurement and Assessment. Assessing Contexts of Learning (pp. 283–299). Springer International Publishing. https://doi.org/10.1007/978-3-319-45357-6_11

Schmidt, H. G., & Mamede, S. (2015). How to improve the teaching of clinical reasoning: a narrative review and a proposal. Medical Education, 49 (10), 961–973. https://doi.org/10.1111/medu.12775

Schmidt, H. G., & Rikers, R. M. J. P. (2007). How expertise develops in medicine: knowledge encapsulation and illness script formation. Medical Education, 41 (12), 1133–1139. https://doi.org/10.1111/j.1365-2923.2007.02915.x

Schons, C., Obersteiner, A., Reinhold, F., Fischer, F., & Reiss, K. (2022). Developing a simulation to foster prospective mathematics teachers’ diagnostic competencies: the effects of scaffolding . Advance online publication. https://doi.org/10.1007/s13138-022-00210-0

Stadler, M., Herborn, K., Mustafić, M., & Greiff, S. (2020). The assessment of collaborative problem solving in PISA 2015: an investigation of the validity of the PISA 2015 CPS tasks. Computers & Education, 157 , 103964. https://doi.org/10.1016/j.compedu.2020.103964

Stadler, M., Hofer, S., & Greiff, S. (2020). First among equals: log data indicates ability differences despite equal scores. Computers in Human Behavior, 111 , 106442. https://doi.org/10.1016/j.chb.2020.106442

Stadler, M., Sailer, M., & Fischer, F. (2021). Knowledge as a formative construct: a good alpha is not always better. New Ideas in Psychology , 60. https://doi.org/10.1016/j.newideapsych.2020.100832

Stark, R., Kopp, V., & Fischer, M. R. (2011). Case-based learning with worked examples in complex domains: two experimental studies in undergraduate medical education. Learning and Instruction, 21 (1), 22–33. https://doi.org/10.1016/j.learninstruc.2009.10.001

Stasser, G., & Titus, W. (1985). Pooling of unshared infomration in group decision making: biased information sampling during discussion. Journal of Personality and Social Psychology, 48 (6), 1467–1478.

Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48 (6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2

Tschan, F., Semmer, N. K., Gurtner, A., Bizzari, L., Spychiger, M., Breuer, M., & Marsch, S. U. (2009). Explicit reasoning, confirmation bias, and illusory transactive memory: a simulation study of group medical decision making. Small Group Research, 40 (3), 271–300. https://doi.org/10.1177/1046496409332928

van Joolingen, W. R., & de Jong, T. (1997). An extended dual search space model of scientific discovery learning. Instructional Science, 25 (5), 307–346. https://doi.org/10.1023/A:1002993406499

Vogel, F., Wecker, C., Kollar, I., & Fischer, F. (2017). Socio-cognitive scaffolding with computer-supported collaboration scripts: a meta-analysis. Educational Psychology Review, 29 (3), 477–511. https://doi.org/10.1007/s10648-016-9361-7

Vogel, F., Weinberger, A., Hong, D., Wang, T., Glazewski, K., Hmelo-Silver, C. E., Uttamchandani, S., Mott, B., Lester, J., Oshima, J., Oshima, R., Yamashita, S., Lu, J., Brandl, L., Richters, C., Stadler, M., Fischer, F., Radkowitsch, A., Schmidmaier, R., . . . Noroozi, O. (2023). Transactivity and knowledge co-construction in collaborative problem solving. In Proceedings of the International Conference on Computer-supported for Collaborative Learning, Proceedings of the 16 th International Conference on Computer-Supported Collaborative Learning — CSCL 2023 (pp. 337–346). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2023.646214

Wegner, D. M. (1987). transactive memory: a contemporary analysis of the group mind. In B. Mullen & G. R. Goethals (Eds.), Theories of Group Behavior (pp. 185–208). Springer New York. https://doi.org/10.1007/978-1-4612-4634-3_9

Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46 (1), 71–95. https://doi.org/10.1016/j.compedu.2005.04.003

Download references

Open Access funding enabled and organized by Projekt DEAL. The research presented in this contribution was funded by a grant of the Deutsche Forschungsgemeinschaft (DFG, FOR 2385) to Frank Fischer, Martin R. Fischer and Ralf Schmidmaier (FI 792/11-1 & FI 792/11-2) 

Author information

Authors and affiliations.

Department of Psychology, Ludwig-Maximilians-Universität München, Leopoldstr. 13, 80802, Munich, Germany

Laura Brandl, Matthias Stadler, Constanze Richters & Frank Fischer

Institute of Medical Education, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany

Matthias Stadler & Martin R. Fischer

IPN Leibniz Institute for Science and Mathematics Education, Department of Mathematics Education, Kiel, Germany

Anika Radkowitsch

Medizinische Klinik und Poliklinik IV, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany

Ralf Schmidmaier

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Laura Brandl .

Ethics declarations

Conflict of interest statement.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Please note that the data employed in this study have been used in previous publications (e.g., Brandl et al., 2021; Radkowitsch, et al., 2021; Richters et al., 2022 ). However, the research question and the results reported in this study are completely unique to this study. An initial version of this article is presented as a poster at ISLS 2024.

see Tables 7 , 8 and 9

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Brandl, L., Stadler, M., Richters, C. et al. Collaborative Problem-Solving in Knowledge-Rich Domains: A Multi-Study Structural Equation Model. Intern. J. Comput.-Support. Collab. Learn (2024). https://doi.org/10.1007/s11412-024-09425-4

Download citation

Received : 18 September 2023

Accepted : 09 May 2024

Published : 24 June 2024

DOI : https://doi.org/10.1007/s11412-024-09425-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Collaborative Problem-solving
• Simulation-based Learning Environment
• Diagnostic Activities
• Diagnostic Reasoning
• Medical Education
• Find a journal
• Publish with us
• Track your research