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Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

• Published: 28 May 2021
• Volume 26 , pages 7321–7338, ( 2021 )

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• Jessie S. Barrot   ORCID: orcid.org/0000-0001-8517-4058 1 ,
• Ian I. Llenares 1 &
• Leo S. del Rosario 1  

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Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

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1 Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

2 Literature review

2.1 education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

2.2 Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

2.3 Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

3 Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

3.1 Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

3.2 Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

3.3 Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

4.1 The extent of students’ online learning challenges

Table 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x̅  = 2.62, SD  = 1.03) with scores ranging from x̅  = 1.72 ( to some extent ) to x̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x̅  = 2.51, SD  = 1.31), SIC ( x̅  = 2.77, SD  = 1.34), and LRC ( x̅  = 2.93, SD  = 1.31).

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

4.2 Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

4.3 Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary.

5 Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Barrot, J.S., Llenares, I.I. & del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ Inf Technol 26 , 7321–7338 (2021). https://doi.org/10.1007/s10639-021-10589-x

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Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

• Junyi Zhang, 
• Yigang Ding, 
• Xinru Yang, 
• Jinping Zhong, 
• XinXin Qiu, 
• Zhishan Zou, 
• Yujie Xu, 
• Xiunan Jin, 
• Xiaomin Wu, 

• Published: August 23, 2022
• https://doi.org/10.1371/journal.pone.0273016
• Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

• (1) Can the COVID-19 pandemic promote the expansion of online learning?
• (2a) What are the characteristics of online learning interaction during the pandemic?
• (2b) What are the characteristics of online learning interaction after the pandemic?
• (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

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https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

• (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
• (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
• (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
• (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
• (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
• (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

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ORIGINAL RESEARCH article

Challenges of online learning amid the covid-19: college students’ perspective.

• 1 Education College, Shanghai Normal University, Shanghai, China
• 2 Environmental and Geographical College, Shanghai Normal University, Shanghai, China
• 3 Foreign Languages College, Shanghai Normal University, Shanghai, China

Universities in China’s transition to online education in response to the COVID-19 pandemic have spawned several research studies. However, studies exploring college students’ technological skills, relationships with their peers and instructors, and collaborative learning experiences during the pandemic are scarce. Three aspects were explored in this mixed study: (1) changes in students’ engagement in class and the main factors involved; (2) students’ feelings and reactions during online learning; and (3) how students related to their peers and instructors. Data were collected through a qualitative survey supplemented by quantitative data about students’ attitudes to online learning using the SAROL scale. This paper argues that online learning may not produce the desired results due to lack of interaction with instructors, no campus socialization or well-trained technology skills, and appropriate content for online courses and group work. The findings further revealed that online learning offers college students new ways to learn independently, collaborate and build relationships with their peers. It encourages them to reconsider ways to improve their technology skills, learning methods, communication skills and reconceptualize their responsibilities as team members.

1. Introduction

The sudden outbreak of COVID-19 has affected the lives of people all over the world since 2019 ( Ayittey et al., 2020 ; Villela et al., 2021 ). Health and safety concerns forced many schools to close temporarily ( Jena, 2020 ). In China, the need for online learning increased rapidly, causing the traditional face-to-face learning mode to change to online learning as educators have strived to ensure students receive their formal education programs ( Lei and Medwell, 2021 ).

The pandemic has brought unprecedented challenges to the education system, placing higher demands on emergency preparations as schools need to adapt to the changing environment and repeated outbreaks ( Xue et al., 2020 ) - the so-called “new normal” ( Wang, 2020 p.11). Educational institutions struggle to find alternative options to face-to-face education to deal with this challenging situation ( Rieley, 2020 ). They shut down campuses to enable students keep a social distance from each other ( Toquero, 2020 ). However, it is impossible to make a smooth transition from a traditional educational environment to online learning in a very short time. The rapid transition has brought many obstacles and challenges ( Crawford et al., 2020 ). Students appear unable to understand the educational role of online technologies and consider them irrelevant or even an obstacle to learning ( Ginns and Ellis, 2007 ; Ellis and Bliuc, 2019 ). For instance, on a learning platform called Xuexitong, the target students were not involved in virtual class activities and unable to achieve the desired improvement to their studies ( Lei and Medwell, 2021 ). Cui et al. (2020) study showed that the proportion of students who completed their courses and homework on time decreased over time. Although the strength of the impact of the COVID-19 outbreak on education may take time to become fully apparent, educational institutions around the world are currently doing everything they can to create better online learning environments and resources for students in all academic fields by utilizing their limited resources to their utmost ( Kaur, 2020 ).

An important aspect of assessing online learning is discovering how to identify problems from a student’s viewpoint in order to improve the quality of online courses. Students’ perspectives are invaluable, and their first-hand input comes from their experiences and expectations ( Dawson et al., 2019 ). Furthermore, how college students reacted to online courses during the epidemic plays a crucial role in helping education professionals to meet the learning needs of students better in future when teaching modes of delivery change and new technologies emerge ( Crews and Butterfield, 2014 ; Van Wart et al., 2020 ). Therefore, it is essential that the students’ perspective is central. Pragmatically, many valuable and practical findings and insights have been achieved through studies on teachers’ teaching efficiency, constraints, and challenges during COVID-19 ( Arora and Chauhan, 2021 ; Ober et al., 2022 ). However, the students’ standpoint has received less attention than the teachers’ perspective in the assessment of online education’s effectiveness presented in previous studies.

The findings of this study can give university administrators and teachers a better understanding of what needs to be done to adjust to the future of online learning and help students overcome common challenges they are likely to face so they have a better learning experience. Due to the sudden transition in learning mode and learning environment, we consider in-depth insights into college students’ feelings and in-class performance, vital. Our study addresses the following research questions:

1. What factors affected students’ engagement in online learning?

2. What were college students’ feelings/reactions during online learning?

3. How did college students’ relationships with their peers and instructors change during online learning?

2. Literature review

2.1. challenges presented by online learning before covid-19.

The rapid development of electronic technologies has made distance education easier ( McBrien et al., 2009 ), but sometimes there can be many obstacles. Often, difficulties and problems associated with modern technology come from downloading errors, issues with installation, login problems, problems with audio and video, etc. Previous research has shown that some features like file sharing, whiteboards, and annotation are not easy to use, resulting in the underutilization of conferencing functions ( Ming et al., 2021 ). In asynchronous learning environments, learning content cannot be provided in the same format as in offline classes, that is, it is impossible to provide real-time feedback and responses ( Littlefield, 2018 ). At the same time, students feel a lack of learning community, experience technical problems, and have difficulty understanding instructional goals, which are the major barriers to online learning ( Song et al., 2004 ). It is worth mentioning that certain challenges experienced in online courses are due to educators’ lack of online teaching skills or lesson preparation in the form of detailed teaching plans, lack of appropriate support from technical teams, and traffic overload in online education platforms.

One big problem of online courses is the monotonous learning scenario and the easy visual fatigue of the learners ( Zhou and Ren, 2019 ). Sometimes, students found online teaching boring and unappealing because online teaching videos were too long, reducing learners’ enthusiasm and interests in learning ( Li and Wang, 2019 ). Although asynchronous online learning provides a lot of response time and high degrees of flexibility for students, they still have difficulty finding enough time to complete tasks ( Knox, 2016 ). Moreover, mediocre course content is also a major issue. Students’ level of preparedness in using Learning Management Systems ( Parkes et al., 2014 ) is low. Online programs need to be designed to be creative, interactive, relevant, student-centered, and group-based ( Partlow and Gibbs, 2003 ).

Not only teachers but students also face challenges due to a lack of appropriate learning materials, their attitude to learning, lack of self-discipline, and the inadequate learning environment in some of their homes during self-isolation ( Brazendale et al., 2017 ). Furthermore, Willging and Johnson (2009) found that students may not have been interested in the learning materials used because they lacked pre-knowledge of the course, so were unable to follow the learning material offered ( Pierrakeas et al., 2004 ).

2.2. Challenges of online learning during COVID-19 isolation

COVID-19 was a blow to traditional learning methods in academic institutions around the world. The administration systems of educational institutions around the world chose online course tuition to restore education provision when the physical presence of students and tutors was impossible. Online learning during COVID-19 could be delivered synchronously or asynchronously. Obviously, the current situation is not like traditional online learning but more like crisis learning and has posed huge challenges for students. They may be faced with unstable Internet connections, which makes it impossible to ensure equity between students through online learning ( la Velle et al., 2020 ; Xue et al., 2020 ). At the same time, this causes attendance and engagement issues in online sessions, so online education can be less adaptable than supposed. Moreover, students had to rapidly turn to unfamiliar learning methods, while responding as individuals and members of social groups to the impact of the epidemic on their daily lives, physical and mental health ( Macintyre et al., 2020 ). It is not hard to understand why teachers’ techno-pedagogical skills appear to be the major factor affecting student engagement during this time. Researchers have found a positive correlation between the students’ grades and their technological abilities—if teachers are not proficient in using the functions of network equipment, students’ learning is correspondingly negatively affected ( Masry-Herzallh and Stavissky, 2021 ). Therefore, in future, teachers need to improve their teaching skills to facilitate the transfer of knowledge and their communication with students ( Palanisamy et al., 2020 ), and it is necessary to explore online teaching strategies that focus on students’ interests as a way to ensure higher levels of student engagement.

Most importantly, the uncertainty about when the outbreak restrictions will end has led to much anxiety and fear among students isolated at home. Research has revealed that personal challenges (such as economic and psychological stress) have reduced students’ willingness to learn online in future, while the quality of the online experience (including instructional and assessment quality) has improved their attitude to learning online in future ( Al-Salman and Haider, 2021 ). Therefore, teachers need to communicate with their students regularly to help alleviate any inner turmoil and cater to their other psychological needs during these stressful times ( Anderson, 2020 ; Snelling and Fingal, 2020 ; Tate, 2020 ). It is suggested that closely monitoring students’ feelings can have a positive impact on their learning ( Morgan, 2020 ).

2.3. Effectiveness of online learning

While online learning has been shown to help protect students and faculty from infection during the COVID-19 pandemic, it has not been as effective as traditional learning. Five common criteria have been proposed for assessing the effectiveness of the digital transformation in higher education institutions; the changes, their speed, technology involved, users and system capacity, and economic implications ( Kopp et al., 2019 ). Online learning means the use of technological devices, the Internet as a tool. Adedoyin and Soykan’s research ( Adedoyin and Soykan, 2020 ) noted that technical issues, socio-economic factors, human and pet intrusion, digital competence, assessment and supervision, and heavy workload can affect the effectiveness of online learning.

The intervention of teachers can improve students’ learning efficiency to a certain extent. Ahmad’s (2020) study found that most students struggled with online learning, particularly in underdeveloped locations with poor connectivity ( Ming et al., 2021 ). In addition, the content of the online course material discussed in class requires students to type messages through the chat box of the virtual conferencing applications, which requires responding within time limits ( Zhong, 2020 ).

2.4. Changes of students’ relationships with others in online learning

Online learning lacks the physical presence of a face-to-face interactive relationship between fellow students, and students and their educators ( Means et al., 2009 ; Alawamleh et al., 2020 ), so how students and instructors interact and how students collaborate with each other has to change. Although there are a variety of online applications, many tutors cannot provide students with remote care and timely feedback on their academic performance ( Collazos et al., 2021 ). This makes students dissatisfied with online learning. Research has found that Arab students, for instance, have negative feelings about online learning ( Masry-Herzallh and Stavissky, 2021 ). Likewise, college students from Pakistan perceive conventional learning as more motivating than online learning; for example, they enjoy participating in conventional learning activities and become more easily immersed in the atmosphere of conventional interaction ( Muhammad and Kainat, 2020 ). In essence, students are “social learners” who long for interaction with their peers and instructors; they can be easily distracted and pay less attention to the content of online courses ( Bozkurt and Sharma, 2020 ) and have difficulty maintaining self-discipline ( Nishimwe et al., 2022 ). Generally, students tend to prefer face-to-face teaching and learning.

Specifically, research has found that the learning performance of students who participated in online discussion activities was significantly better than those who did not, even when their other learning experiences were similar ( Du et al., 2019 ). Collaborative learning among peers can also facilitate the exchange of ideas and information to improve their knowledge level ( Luaran et al., 2014 ). However, group collaborative learning appears to be less effective because students have weak cooperative aims ( Stahl, 2005 ; Collazos et al., 2007 ). Therefore, many instructors employed more technology and applications for synchronous learning to increase student motivation and improve learning efficiency and learning achievement (e.g., PowerPoint voiceover slides for uploading course content, using the lounge feature in video conferencing to increase interaction with students and encourage communication between students, using WeChat for class discussion and group collaboration; Gao and Zhang, 2020 ; Farrell and Stanclik, 2021 ; Gan et al., 2022 ; Moorhouse and Wong, 2022 ).

3. Methodology

The purpose of the study is to explore the effectiveness and challenges of online learning during the COVID-19 pandemic and to propose possible solutions derived from analyzing the underlying causes of the challenges faced by higher education students in an eastern city in China. The present study employed a mixed-mode research design to answer the research questions. The researchers used a qualitative survey as the primary data collection tool and supplemented it with quantitative data from a students’ attitudes regarding online learning (SAROL) scale ( Muhammad and Kainat, 2020 ) to investigate the attitudes of Chinese higher education students during the COVID-19 pandemic to the online learning mode compared to the traditional learning mode, as well as the challenges and opportunities this new online learning mode presented.

3.1. Sampling strategy and participants

The researchers used purposive sampling for the qualitative phase by sending an invitation email to college students who were following online courses using online learning platforms like Tencent Conference and Xuexitong. There were 102 male and 128 female participants from five universities in an eastern city in China. The participants’ ages ranged from 18 to 20 years old. Snowball sampling was also adopted in the quantitative phase. The researchers shared the questionnaire links with currently enrolled research participants and encouraged them to spread the project on social media platforms such as WeChat, QQ, and Weibo to capture a growing chain of participants ( Creswell, 2011 ).

3.2. Data collection instruments

Data were collected through a demographic questionnaire, qualitative survey, and a SAROL instrument. The demographic section included questions about the participants’ age, gender, grades, and online learning experience.

The qualitative survey adopted a semi-structured interview ( Bryman, 2016 ) that focused on students’ course engagement, relationship with their peers and instructors, their experience of collaborative learning, and the effectiveness of online learning. Research instruments were designed collaboratively by the researchers. During the pilot, researchers wrote open questions about students’ online engagement, students’ relatedness, and the experience of collaborative learning from the perception of students. For example, “what do you think has affected your group work completion?” The research instruments were then refined into several main themes—views on learning, collaborative learning, and active learning. These are detailed in Table 1 .

Table 1 . Interview questions.

To explore the effectiveness and challenges of online learning from the perception of college students, we modified the SAROL scale from Muhammad and Kainat’s (2020) study to investigate students’ attitudes toward online learning during the epidemic. SAROL has been widely used to explore higher education students’ responses to online learning (e.g., Coman et al., 2020 ; Guo et al., 2020 ; Serhan, 2020 ). The questionnaire consists of eight questions, the first question multiple-choice to elicit the major challenges of online education during the COVID-19 outbreak, the second to eighth rated on the Likert Scale as strongly agreeing, generally agreeing, or disagreeing to investigate the respondents’ attitudes toward online teaching. The questionnaire format and items were piloted and revised with a group of 20 students at S University (anonymized). The researchers used the SAROL results to better understand participants’ responses to the open-ended questions.

3.2.1. Modifications to the SAROL scale

Considering the differences in cultural background and technological level between Pakistan and China, the necessary modifications were made.

Online learning can be effective in digitally developed countries, such as China. However, in some underdeveloped countries, such as Pakistan, much of the learning and teaching, as well as the management of academic institutions, is handled manually. The lack of fast, affordable, and reliable Internet connections has hampered the progress of online learning in that context. The original questionnaire cited by Muhammad and Kainat (2020) asked students in Pakistan about their attitudes toward online learning based on factors about students’ limited access to the Internet, such as inability to use electronic devices and price; these factors were removed in the context of this research based in China. Familiarity with online functions, privacy concerns, and signal strength issues were added to illustrate the main reasons for the low frequency of online learning software functions.

While the COVID-19 pandemic has prompted Chinese universities to turn to online education, little is known about the impact of students’ skill in using virtual conferencing functions on their views of teaching quality. Therefore, the researchers changed the second question to “I am proficient with conferencing applications functions.” Table 2 shows the questionnaire’s final version.

Table 2 . Students’ attitudes regarding online learning.

To exclude the interference of gender in this study, the researchers conducted a Chi-square test. The results in Table 3 indicate no significant difference between gender and students’ attitudes to online learning [ r (230) = 0.93, p > 0.05, representing a small effect].

Table 3 . Pearson correlation; Sarol and gender.

3.2.2. Refining the scale

An item analysis was done to remove any items which did not meet the statistical standard. Of the 7 items in the initial SAROL scale, items VarA2 and VarA8’s association with the total score of all variables were 0.269 and − 0.166, so each failed to reach the required level of significance ( p  < 0.01) and were removed. The remaining 5 items demonstrated good differentiation.

In this study, the reliability analysis was done according to the SAROL scale. The overall Cronbach’s α coefficient was 0.702, an acceptable internal reliability ( Creswell, 2003 ), as shown in Figure 1 , the internal consistency being ideal. After removing VarA2 and VarA8, total correlations of individual items were all higher than 0.4, while deleting the two items did not lead to an increase of Cronbach’s α coefficient. This indicated that the scale’s internal consistency and reliability were acceptable.

Figure 1 . Cronbach’s alpha.

3.3. Data collection procedures

Questionnaires are considered a cost-effective and human resource-efficient method of data collection ( Creswell, 2003 ). The researchers emailed potential participants information about the study and the link to the online questionnaire platform used to collect data. Consent forms were attached to the questionnaire stating that the information provided would only be used for academic and research purposes and assuring the respondents of their rights to privacy, to be informed, and of the confidentiality of the research ( Creswell, 2003 ). The participants were informed their participation was voluntary, with the right to withdraw from the study at any stage. The second part of survey included demographic information, while the third and the final part consisted of the SAROL scale and the qualitative survey questions, respectively. The content of the interviews was recorded and transcribed professionally verbatim. Each participant was given a code to protect their identity (e.g., S1 stands for College Student 1) and participants were asked not to identify themselves to the others during recording. Sixteen participants agreed to participate in the survey and allowed their responses to be recorded.

3.4. Data analysis

3.4.1. qualitative data analysis.

We used NVivo 12.0 to conduct a thematic data induction analysis of the recorded content ( Braun and Clarke, 2006 ). We developed codes based on our literature review and research questions and modified codes when conducting the data analysis. We double coded the qualitative data to avoid unnecessary or duplicate codes, then organized the final codes into a thematic structure, and finally recoded the transcripts to ensure consistency. Examples are included in Table 4 . This inductive approach was more appropriate to the contextual and exploratory nature of this research ( Bryman, 2016 ).

Table 4 . Recommendations for practice.

3.4.2. Quantitative data analysis

The analysis of the reliability and validity of the data was completed using the Statistical Package of Social Science (SPSS 28.0) and all figures presented through excel. The percentage of students’ participation in collaborative activities and online learning and the frequency of using the conferencing applications functions were calculated through descriptive statistical analysis. Inferential analysis was used to assess the availability and convenience of online classes and the differences from traditional teaching, as the opportunities and challenges students experienced during the COVID-19 pandemic. To enhance the reliability and validity of this study, the data analysis was conducted individually by each of the researchers, followed by discussions to reach consensus on the results.

The following sections show the results of the questionnaire and interviews given together to the participants who had been experiencing COVID-19 pandemic restrictions on physical content. The data and interview feedback revealed the obstacles to online learning and emotional feedback concerning online learning. Although the responses varied, three main themes emerged. We selected a representative sample of interviewees for each main theme to give an indication of the feelings surrounding them.

4.1. The challenge from poor student engagement in online learning

4.1.1. lack of technical skills.

The reasons for the low frequency of online participation also suggest why participants were reluctant to use conferencing functions during COVID-19. The reasons include unfamiliarity with online systems and lack of confidence, poor signal or strength issues, and fear of privacy exposure. Based on the results of the questionnaire, the main reason participants rarely used voice and screen sharing in conferencing applications was they felt too shy to speak (69.57%). The other primary reason for using functions less frequently was signal reception or strength issues. 20.87% of participants responded that they sometimes could not hear others’ voices, could not see others’ shared files, or videos would stall. Another problem was that participants were afraid their privacy might be invaded (4.35%) and they were unfamiliar with numerous functions of conferencing applications (5.22%). In other words, due to the sudden outbreak of the COVID-19 epidemic, students had no time to fully explore these features and they seldom used virtual conferencing applications in offline learning settings, always arranging a time to meet and discuss assignments in person. The findings strongly suggest participants preferred offline communication to online communication when they had to attend to online courses concerns due to online uncertainties. Figure 2 highlights the reasons for the low use of the current virtual conferencing applications (Tencent Meeting). Figure 3 is a bar chart illustrating user’s familiarity with the features on virtual conferencing tools. For example, S2 stated that:

Figure 2 . Distribution of reasons for the low frequency of the virtual conferencing tools.

Figure 3 . Distribution of user’s familiarity with the features on virtual conferencing tools.

We have some virtual conferencing applications like Tencent Conference. It is not completely paperless learning although I often learn with my computer and tablet, completing homework such as courseware on them. These technological devices bring convenience, but also require self-discipline and proper use, which can affect my participation and engagement in class. (S2).

4.1.2. Low learning motivation

In response to the question of whether online and conventional learning are the same, 12.17% reported that online learning is very different from the conventional learning mode, while 58.7% felt that there was little difference between online and conventional learning. According to the questionnaire, only 18.7% of students felt that online learning was more motivating than conventional learning, while more than half the students (50.43%) disagreed that online learning was more motivating than conventional learning disrupted by the COVID-19 epidemic. Figure 4 shows the results of students’ motivation to learn during the COVID-19 pandemic.

Figure 4 . Bar chart of adaptability of online learning.

4.2. What were college students’ feelings/reactions during online learning?

4.2.1. students’ feelings/reactions during online learning.

The usability study asked participants whether they felt comfortable without voice or video features while performing collaborative activities or attending online courses. Almost half the responses (46.96%) were neutral, meaning for them, it did not matter if voice or video were on or off, while nearly half (45.65%) reported feeling more comfortable and relaxed (less nervous) without opening voice or video. Around 7.9% of participants experienced negative feelings, such as loneliness or boredom. To sum up, silent online communication seemed to alleviate anxiety during the COVID-19 pandemic, so participants preferred typed communication to online communication through voice and video, particularly during collaborative activities (when some group members did not know others very well) or attending subject classes. Figure 5 shows whether participants felt more relaxed by not opening voice or video during virtual conferencing. For example, referring to his/her feelings during online collaborative learning, as S5 stated that:

Figure 5 . Distributions of whether user feel comfortable without voice or video during a virtual conference meeting.

The access to and sharing of information and materials is more convenient, and I also communicate more closely with my team members. (S5).

4.2.2. How did students value online learning?

The questionnaire asked participants about their experiences with online collaborative learning during the COVID-19 pandemic. 44.78% of respondents thought that it was very challenging to effectively complete entire college courses through online learning. Moreover, 57.39% of students reported that they felt difficult while doing group projects or assignments through distance learning, while 23.48% of students valued their online learning experience as they found conducting group projects or assignments digitally was easy in actual practice. As further illustrated in interviews, students acknowledged that their online learning experiences had “forced” them to “continually develop technological skills to function effortlessly” (S8) and “increased their awareness of participation in a digital world” (S13). Overall, the participants rated the collaborative experience to be neutral and the efficiency of collaborative learning and the mastery of course content could be challenging more than rewarding.

Figure 6 shows the distribution of collaborative experience gained through the uses of online course platforms. When making a reference to the effectiveness of face-to-face communications with teachers, 68.7% of students agreed that face-to-face communication with teachers is necessary for online learning, this emphasizes the importance of teacher’s presence. This sense of social presence could counteract students’ loneliness when a direct interpersonal touch is missing, which can be “extremely important during the current pandemic crisis” (S6).

Figure 6 . Distribution of students’ collaborative experience during online learning.

4.3. How did college students’ relationships with their peers and instructors change during online learning?

4.3.1. face-to-face communications between college students and their instructors.

Figure 6 shows the distribution of collaborative experiences gained during the use of online class platforms. While referring to the effectiveness of face-to-face communication with their instructors, 68.7% of the students felt that face-to-face contact with their instructors is necessary in order to learn. In traditional offline classes, college students could have face-to-face communication with their instructors and ask them for guidance, but on transferring to online learning, they could only communicate through social apps and online meetings. Communicating in real time with instructors seems to help college students to “better understand their instructors’ tasks and guiding concepts” (e.g., S7, S9, and S10):

I couldn’t finish some of my academic tasks without face-to-face communications with my instructor. Sometimes I cannot get a reply in good time from my instructors when asking online. (S7)

4.3.2. Collaborative learning between peers

The majority of respondents considered that online learning is different from offline learning in terms of group collaboration. In the past, college students could conduct collaborative learning by booking group discussion rooms to complete groupwork, but after switching to online learning, they carry out collaborative learning in virtual meetings.

The questionnaire investigated the participants’ collaborative experience of online learning due to the impact of COVID-19 pandemic. 57.39% of students reported having difficulties doing group projects or assignments through online learning, while 23.48% felt able to easily finish group projects or assignments digitally. Overall, the participants rated the collaborative experience to be neutral, but the effectiveness of collaborative learning and the mastery of course content were problematic. S11’s quote is a typical example:

I think communication ability, team member responsibility, reasonable work distribution, management ability are the keys to collaborative learning. Without good communication skills, it is easy to cause internal strife, and team member’s poor sense of responsibility leads to low work involvement, which then affects the quality of work. (S11)

5. Discussion

The majority of the college students surveyed were not satisfied with online learning. Low engagement in online learning and the effectiveness of online learning were the major challenges faced by college students in this study. According to Means et al. (2009) and Alawamleh et al. (2020) , the efficiency of online learning is questionable and there are many challenges to the success of online learning ( Adedoyin and Soykan, 2020 ). This research also revealed an additional challenge faced by students, i.e., their relationships with instructors and peers.

Based on this research, lack of technology skills and low learning motivation result in students’ low engagement in online learning. Being shy or reticent about turning on voice and video is the main barrier faced by higher education students (69.57%) of S University, while unfamiliarity with virtual conferencing application functions, signal reception, strength issues, and fear of loss of privacy are additional obstacles; hence, full advantage of features in the virtual conferencing application is not taken. Students in Song et al.’s (2004) research encountered some additional technical problems like downloading errors, issues with installation, login problems with audio and video, etc. Ming et al. (2021) found that some features like file sharing, whiteboard, and annotation are not easy to use, resulting in the underapplication of conferencing functions. It is worth making clear that teachers’ mastery of technology also affects students’ engagement ( Masry-Herzallh and Stavissky, 2021 ). Therefore, students need to overcome their shyness in front of the camera, while teachers need to explore and expand their online learning strategies.

One of the less discussed areas of online education is the need to motivate students to learn online. 50.43% of participants indicated they felt a strong incentive for to learn offline. This concurs with Muhammad and Kainat’s (2020) conclusion that conventional learning was more motivating than online learning. In traditional classes, students more easily immersed themselves and participated in academic tasks actively through their face-to-face engagement with teachers. Furthermore, students believed that they cannot do their homework effectively and on time without checks and mandatory provisions by teachers, hence their tendency to procrastinate.

This research indicated that conventional learning was more effective than online learning, the same as Kopp et al.’s (2019) findings. While comparing the effectiveness of conventional and online learning, 68.7% of respondents felt that face-to-face communication with their teachers was crucial to effective learning. According to our questionnaire, 44.78% of students reported being unable to complete entire college online courses effectively through online learning. Also, most of the interviewees surveyed preferred offline learning. They asserted the most effective aspect of online learning was the easily accessible learning resources, and the least productive aspect is the lack of supervision. Such reactions have been explained by distraction ( Bozkurt and Sharma, 2020 ) and lack of discipline ( Nishimwe et al., 2022 ).

The majority of participants reflected meeting great challenges in the process of online learning. Only by switching off voice and video, did the surveyed students (45.65%) feel comfortable. A minority (7.9%) experienced negative feelings, such as loneliness or boredom, which made them sometimes uncomfortable and reduced their passion for online learning. This result is explained by the fact that today’s students seem to be shy and prefer to be alone, so shutting down video and voice functions makes them feel safer and more relaxed. However, if lack of face-to-face social interaction continues, students may suffer psychological distress at all levels ( McCarthy, 2020 ). According to Macintyre et al. (2020) , the epidemic impacts students’ daily lives, and their physical and mental health.

What’ s more, lack of face-to-face communication and collaborative learning with peers and instructors is an extra barrier, challenging college students’ relationships with their instructors and peers, even though group work online can be as effective as face-to-face learning ( Ocker and Yaverbaum, 1999 ). Due to physical limitations caused by the pandemic, 57.39% of the students think that they have difficulty in completing group projects because group study is boring and unappealing. Group study online needs to be (re)designed to be creative, interactive, relevant, student-centered, and group-based, as suggested by Partlow and Gibbs (2003) . Lack of appropriate support from instructors makes work more time-consuming, thus the importance of clear and relevant instructions in group study cannot be ignored. However, previous studies ( Gao and Zhang, 2020 ; Farrell and Stanclik, 2021 ) have shown how several instructors have made the most of technology (e.g., PowerPoint voiceover slides WeChat, and so on) to address the challenge of online communication and instruction.

6. Conclusion

Although online learning can help safeguard the health of students and faculty, it has proven to be less successful than traditional learning. The amount of student-teacher contact and campus socialization, level of technical competence, and appropriateness of learning content for online courses and group work are key factors for whether or not online learning produces the desired results. Therefore, students’ poor performance in online learning can be partly due to their dissatisfaction with the format and quality of course delivery and lack of interaction with others, leading to boredom and low motivation to learn. The findings of our study have revealed that online learning offers college students a new way to learn independently and to collaborate and build relationships with peers, which can encourage students to reconsider how to improve their technical skills, learning methods, and communication skills and review their responsibilities as team members. Technical skills training in future should be given to both faculty and students in order to improve students’ proficiency in applied skills and eliminate communication barriers based on poor skills. It is also advisable to allow students more time to find online learning methods that work for them and to provide them with guidance for following learning materials in to improve their learning efficiency and understanding and application of the content. What is more, teachers should improve their pedagogical skills and applications to increase the frequency of interaction with students by regularly checking their production and providing feedback on students’ academic performance as well as responding to psychological problems. Similarly, in a collaborative learning environment, students themselves need to develop more techniques to improve their communication with other class members and, most importantly, they need to develop a positive attitude toward group work, increase their own sense of team responsibility, and actively participate in group discussions and task completion during this difficult time. The authors hope that these findings will help students who need to learn online to better address similar challenges they encounter, since some new forms of learning, such as “blended learning” and “project-based learning,” are likely to continue to exist in post-epidemic learning.

The study’s greatest limitation is that it addresses the situation in one eastern city in China, so it is impossible to make broad claims. In the event of the epidemic’s resurgence in China, the researchers have had no opportunity to interview more college students, meaning the existing questionnaire data may not as comprehensive and detailed as desirable. To obtain broader and more reliable results, the design of the questionnaire could be improved, and more comparative studies could be conducted with colleges students in other contexts to better understand the similarities and differences through a larger capacity in sample files.

However, even though the sample size is small, the results can shed light on common challenges that students experienced in online class during COVID-19 pandemic. Understanding how students and their instructors perceive the online mode of higher education instruction in China can aid the development of more efficient ways of taking online classes and adapting better to online learning. There was a lot of agreement between students and instructors when it came to their impressions of online learning. The students and teachers’ views reflected and bolstered each other’s, so this level of agreement provides a basis for designing new online courses and improving the online teaching and learning experience.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving human participants were reviewed and approved by Shanghai Normal University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

YX, YH, CW, and LY are undergraduates, and ML is an Assistant Professor. ML has made substantial contributions to the conception and design of the work. She supervised the project and designed the theoretical framework, and research methods of the manuscript. She has contributed to the revision of the manuscript, to the acquisition, analysis, and interpretation of data for the work. YX has made great contributions to the design of the research framework and has organized the database, drafted, and written the abstract, literature review, introduction, and conclusion. YH has written the discussion section. CW has drafted and written the interpretation of data of this manuscript. LY has helped to perform the statistical analysis and written the methodology. All authors have collected the data, helped write the first draft of the manuscript, revised the manuscript several times and approved the submitted version.

This research was sponsored by the research project “Exploring the reform and latest practice of teacher education” which was sponsored by Foreign Languages College, Shanghai Normal University.

Acknowledgments

We appreciate the constructive suggestions from the editor and reviewers.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: COVID-19, online learning, college students, challenges, students’ engagement, relationships, students’ feelings

Citation: Xia Y, Hu Y, Wu C, Yang L and Lei M (2022) Challenges of online learning amid the COVID-19: College students’ perspective. Front. Psychol . 13:1037311. doi: 10.3389/fpsyg.2022.1037311

Received: 05 September 2022; Accepted: 05 December 2022; Published: 22 December 2022.

Reviewed by:

Copyright © 2022 Xia, Hu, Wu, Yang and Lei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Man Lei, ✉ [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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What we know about online learning and the homework gap amid the pandemic

America’s K-12 students are returning to classrooms this fall after 18 months of virtual learning at home during the COVID-19 pandemic. Some students who lacked the home internet connectivity needed to finish schoolwork during this time – an experience often called the “ homework gap ” – may continue to feel the effects this school year.

Here is what Pew Research Center surveys found about the students most likely to be affected by the homework gap and their experiences learning from home.

Children across the United States are returning to physical classrooms this fall after 18 months at home, raising questions about how digital disparities at home will affect the existing homework gap between certain groups of students.

Methodology for each Pew Research Center poll can be found at the links in the post.

With the exception of the 2018 survey, everyone who took part in the surveys is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

The 2018 data on U.S. teens comes from a Center poll of 743 U.S. teens ages 13 to 17 conducted March 7 to April 10, 2018, using the NORC AmeriSpeak panel. AmeriSpeak is a nationally representative, probability-based panel of the U.S. household population. Randomly selected U.S. households are sampled with a known, nonzero probability of selection from the NORC National Frame, and then contacted by U.S. mail, telephone or face-to-face interviewers. Read more details about the NORC AmeriSpeak panel methodology .

Around nine-in-ten U.S. parents with K-12 children at home (93%) said their children have had some online instruction since the coronavirus outbreak began in February 2020, and 30% of these parents said it has been very or somewhat difficult for them to help their children use technology or the internet as an educational tool, according to an April 2021 Pew Research Center survey .

Gaps existed for certain groups of parents. For example, parents with lower and middle incomes (36% and 29%, respectively) were more likely to report that this was very or somewhat difficult, compared with just 18% of parents with higher incomes.

This challenge was also prevalent for parents in certain types of communities – 39% of rural residents and 33% of urban residents said they have had at least some difficulty, compared with 23% of suburban residents.

Around a third of parents with children whose schools were closed during the pandemic (34%) said that their child encountered at least one technology-related obstacle to completing their schoolwork during that time. In the April 2021 survey, the Center asked parents of K-12 children whose schools had closed at some point about whether their children had faced three technology-related obstacles. Around a quarter of parents (27%) said their children had to do schoolwork on a cellphone, 16% said their child was unable to complete schoolwork because of a lack of computer access at home, and another 14% said their child had to use public Wi-Fi to finish schoolwork because there was no reliable connection at home.

Parents with lower incomes whose children’s schools closed amid COVID-19 were more likely to say their children faced technology-related obstacles while learning from home. Nearly half of these parents (46%) said their child faced at least one of the three obstacles to learning asked about in the survey, compared with 31% of parents with midrange incomes and 18% of parents with higher incomes.

Of the three obstacles asked about in the survey, parents with lower incomes were most likely to say that their child had to do their schoolwork on a cellphone (37%). About a quarter said their child was unable to complete their schoolwork because they did not have computer access at home (25%), or that they had to use public Wi-Fi because they did not have a reliable internet connection at home (23%).

A Center survey conducted in April 2020 found that, at that time, 59% of parents with lower incomes who had children engaged in remote learning said their children would likely face at least one of the obstacles asked about in the 2021 survey.

A year into the outbreak, an increasing share of U.S. adults said that K-12 schools have a responsibility to provide all students with laptop or tablet computers in order to help them complete their schoolwork at home during the pandemic. About half of all adults (49%) said this in the spring 2021 survey, up 12 percentage points from a year earlier. An additional 37% of adults said that schools should provide these resources only to students whose families cannot afford them, and just 13% said schools do not have this responsibility.

While larger shares of both political parties in April 2021 said K-12 schools have a responsibility to provide computers to all students in order to help them complete schoolwork at home, there was a 15-point change among Republicans: 43% of Republicans and those who lean to the Republican Party said K-12 schools have this responsibility, compared with 28% last April. In the 2021 survey, 22% of Republicans also said schools do not have this responsibility at all, compared with 6% of Democrats and Democratic leaners.

Even before the pandemic, Black teens and those living in lower-income households were more likely than other groups to report trouble completing homework assignments because they did not have reliable technology access. Nearly one-in-five teens ages 13 to 17 (17%) said they are often or sometimes unable to complete homework assignments because they do not have reliable access to a computer or internet connection, a 2018 Center survey of U.S. teens found.

One-quarter of Black teens said they were at least sometimes unable to complete their homework due to a lack of digital access, including 13% who said this happened to them often. Just 4% of White teens and 6% of Hispanic teens said this often happened to them. (There were not enough Asian respondents in the survey sample to be broken out into a separate analysis.)

A wide gap also existed by income level: 24% of teens whose annual family income was less than $30,000 said the lack of a dependable computer or internet connection often or sometimes prohibited them from finishing their homework, but that share dropped to 9% among teens who lived in households earning $75,000 or more a year.

• Coronavirus (COVID-19)
• COVID-19 & Technology
• Digital Divide
• Education & Learning Online

Katherine Schaeffer is a research analyst at Pew Research Center .

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Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, mental health and the pandemic: what u.s. surveys have found, most popular.

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The Digital Divide: Researching the Challenges of Online Learning for Many Students

In this lesson plan, students will learn about what remote learning means for children without stable housing. Then, they will research the online education plan in their region.

By Nicole Daniels

Find all our Lessons of the Day here.

Lesson Overview

Featured Article: “ She’s 10, Homeless and Eager to Learn. But She Has No Internet. ” by Nikita Stewart

In recent weeks, many schools have turned to online learning in an attempt to stop the spread of Covid-19, the disease caused by the novel coronavirus. On March 23, the New York City public school system moved its 1,800 schools online. However, the city has an estimated 114,000 children who live in shelters and unstable housing, which makes offering accessible online education a challenge.

In this lesson you will read about several New York City kids who are navigating online education while living in homeless shelters or homes without internet access. Then, you will create a “one-pager” response to the article, or research your region’s plan for providing online education for students who are homeless.

The featured article profiles six children and teenagers in New York City, beginning with this three-minute video focusing on one child: Allia Phillips. As you watch the video, write down your responses:

One quote from the video that you found moving or insightful.

Two questions or ideas that you have after watching the video.

Three images or stills that you found interesting or meaningful.

She’s an Honors Student. And Homeless. Will the Virtual Classroom Reach Her?

This week new york city’s public schools began remote learning. but for the more than 100,000 students who are homeless, virtual education may be out of reach..

“Can you move just a little bit to this way? Perfect.” “A-L-L-I-A, my name is Allia Phillips. I am 10 years old. I live in New York, New York. And I live in a shelter. And I love playing violin. OK, I’m going to have to stand up for this. A lot of things are really happening right now, like Covid-19. [Violin playing] I’ve heard that it is a very contagious virus. And when people get it, a lot of people have to go to the hospital. But some of them have to get isolated. It’s changed my life because now, we don’t get to go in school. My teacher told us that she didn’t know if it was going to close for the whole year. Ta-da.” “You’ve got blanky fuzz in your hair. Right now, me and Allia are actually living in a family shelter.” “I don’t like the way you brush it. You brush it so hard. Basically, it’s one room with a bathroom, and then one bed and a bunk bed. I guess, it’s now my classroom, too.” “I got contacted by her school to go pick up her iPad device. And her iPad does not have internet. The shelter that we’re in, they do not allow internet. What classroom stuff are we going to do today? Math?” “Well, science is already too hard. It keeps on pausing itself, just freezing.” “Oh OK, I will look into that in a little bit.” “Across the room.” “And see what’s going on. I have a cellphone. We use the hot spot. I don’t have a lot of data. And it’s a little spotty and slow. Essentially, that’s just where we’ve been is puttering outside to get a better signal, hoping it gets better.” “A school has 17 tables in the cafeteria. Each table has 12 seats.” “I am worried that children are going to get left behind because they don’t have their devices or they didn’t have the access to the internet. Let’s try that one again.” “So we’ll probably have to redo our whole grade, and relearn everything we already know.” “Right now, my biggest worry for Allia would be the social interaction. My mother, who’s disabled, and her service animal lives with us. So we’re all in this really tiny space.” “Yeah. I’m worried about that because my grandmother, she is really old. And if she gets it or if my mother gets sick, they could get hurt. And if they both get it, who’s going to take care of me that day? And then if we’re all locked in the same room, how are we going to be safe?”

Now, read the featured article in its entirety to learn more about Allia and other young people in New York City who are also navigating online education without having permanent housing.

Questions for Writing and Discussion

Read the article , then answer the following questions:

1. In your own words, summarize the different struggles that students, parents and school administrators have faced during New York City’s move to online education. Then, summarize some of the attempted solutions and their effectiveness.

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Instructional Design

7 top challenges with online learning for students (and solutions), share this article.

We'll discuss the biggest challenges of online learning and possible solutions to these problems to create a more impactful experience for students.

We live in a world where anything and everything you could ever want to know is ~ literally ~ at your fingertips. Thanks to the internet and the rapid growth of technology, online learning has never been more popular and effective. 

While there are some great perks associated with online learning, such as accessibility, flexibility, and affordability, that’s not to say that online learning doesn’t come with its downsides… online learning can be challenging to get accustomed to at first, and there are some obstacles that come with the territory. 

In this blog we will go into depth on the different problems encountered in online learning, and provide valuable solutions for the problems faced by students in online classes. This way, if you’re a teacher, you’ll know how to support students who are struggling. And if you’re a student, you’ll know what to do when you face challenges with online learning. 

Continue reading or jump ahead: 

• Types of online learning  

Synchronous learning

Asynchronous learning, microlearning, mobile learning (m learning).

• Gamification  
• Isolation  
• Lack of motivation 

Time management 

Distractions .

• Technical issues  
• Communication 

Personal barriers 

Overview of top solutions to overcome online learning challenges.

Check out: Thinkific Online Learning Trends 2024

What is online learning?

Before we dive into all of the problems faced by students in online classes, let’s go over exactly what online learning consists of, and break down the different types of online learning out there. 

Online learning – also commonly referred to as eLearning – has rapidly grown in popularity over the past few years, and eLearning is essentially any kind of learning you can do from your own device and an internet connection. The best part is that you can be anywhere in the world while learning online. 

Online learning has earned its seat in the education industry as it provides more accessibility and flexibility for students, allowing them to choose a learning style that works best for them. It can also be more cost efficient for both students and teachers, cutting back on the overhead expenses that are associated with in-person learning. 

Think of online learning as an umbrella term for all of the different types of eLearning out there. There are multiple ways in which you can learn or teach online, and there is no one right way to approach online education. 

Below we have listed some of the most common forms of online learning that are currently being offered. 

Types of online learning 

First off, there are two ways in which you can consume information online: asynchronous or synchronous learning. 

Asynchronous online learning involves a self-paced learning environment where learners can access the course material and complete assignments at any time and from any place in the world. This is a major appeal for most people when it comes to online learning. 

On the other hand, synchronous online learning is an interactive and live teaching style that mimics in-person instruction, and uses real-time participation and active discussions to facilitate learning goals, regardless of location. This is very similar to learning in-person, however with the added convenience of not having to leave your bed if you don’t wish to!   

Here are some other common forms of online learning: 

• Microlearning – Short-form lessons that mainly involve bite-sized, digestible content. The goal of microlearning is to focus on learning core concepts and theories, while consuming this information within 5 and 10 minutes for better understanding. This style of eLearning weeds out the fluff and unnecessary details, so that students can grasp key points more efficiently.   
• Mobile learning (M Learning) – The use of mobile technology like smartphones to facilitate  educational purposes. With M Learning,  students are able to learn on-the-go through videos, podcasts, and bite-sized lessons. Students are more likely to take up M Learning as it is flexible and convenient, and it’s easy to form a habit or routine this way. 
• Gamification – Involves the use of game elements in the learning process. Examples of this include point systems, leaderboards, and rewards to incentivize learning. This style boots student engagement and creates an immersive environment where learning doesn’t have to feel like a chore. 

Related: The Advantages and Disadvantages of Learning in Online Classes in 2023

Top challenges with online learning 

Now that we’ve covered our basis and explored the various types of online learning, let’s go deeper into some of the challenges faced with these specific modalities of eLearning. 

While distance education and short-form learning techniques have lowered costs, increased flexibility, and reduced the need for physical infrastructure for both students and teachers, it does not come without its downsides. Listed below are some common challenges with online learning. 

Synchronous learning is great for student participation, however there are some key challenges that are worth noting. This particular online learning style closely mimics in-person lectures, so for those who learn best in-person, this is your next best option as there is an emphasis on live lectures and student participation. 

However, synchronous courses don’t always have the flexibility that is often desired when it comes to online learning. In fact, it can be tough for those in different timezones to engage with this type of eLearning. It can also be difficult for some students to find a quiet and private environment to be fully engaged with lectures. As well, if there are any internet troubles, then you are potentially missing out on valuable class time, especially if lectures aren’t recorded.  

Asynchronous classes can be very similar to synchronous learning, just without the live “in-person” component. While there are deadlines and due dates to meet, students have more flexibility with how and when they learn, and can allocate their time in a way that works with their schedule. 

The top problem with asynchronous learning is the lack of personal interactions and peer-to-peer support. Another big challenge is that it can be tough to receive immediate instructor feedback or help – there will most likely be a d elay before an instructor can respond to a query, which negatively impacts the learning experience. Asynchronous courses are also known for having a lack of structure, which can make learning confusing and unmotivating for students.

Microlearning caters to those looking to quickly grasp the concept of a subject, and is generally for learners with limited time. Due to this time constraint, it is challenging to learn complex problems or skill development with microlearning. This limits microlearning to only a few concepts, or more of a surface-level learning experience. 

Microlearning also runs a high risk of fragmented learning if the course is not managed correctly, which can be frustrating for learners. Because of this, it can sometimes be a struggle to keep track of student success and progress with microlearning. 

Worldwide, there are approximately 6.94 billion smartphones – which means that M Learning is only increasing in popularity. The ability to learn from your smartphone is a huge breakthrough in the online learning industry, however there are some noteworthy challenges with this learning style. 

A major challenge is content compatibility. Most times, content created for eLearning doesn’t always smoothly transfer over to mobile devices, affecting both the student and instructor. In this case, content has to be refurbished or recreated so mobile learners can access it properly, which can be very time-consuming.  

Other challenges include small screens, difficulty reading text, and learner retention. M Learning typically produces ultra-short-form content, also making it difficult to learn in-depth concepts. 

Gamification 

Gamification learning uses play for educational goals, and many smartphone apps have mastered the art of game-based learning. While this is a fantastic short-form learning technique that is rapidly growing, some challenges include it being seen as “mandatory fun,” difficulty boosting user engagement, and misaligned motivation to earn rewards instead of retaining core concepts. 

Gamification can also be prone to technological issues such as accessibility, usability, and reliability – which can easily deter potential learners. 

Related: Top Advantages and Disadvantages of Mobile Learning

Problems faced by students in online classes

Now that we’ve gone over some of the problems encountered in online learning, let’s switch gears to the more specific problems faced by students in online classes. 

Noting these challenges will be beneficial for both students and teachers, which is why we will also provide some key solutions to overcome these challenges with online learning. 

Listed below are some of the most common challenges (and solutions) with online learning that students face: 

Humans, by nature, are social animals. One of the biggest obstacles to overcome with online learning is isolation – it can be incredibly lonely to enroll in an online course, and students can often feel disconnected from their peers and instructors. Although students sometimes get to interact with their classmates over Zoom or Google Meet, it is not the same as physical interaction.  

Feeling isolated can lead to students feeling disconnected from class, and they may not engage the way they normally would in an in-person setting. This is especially prevalent with asynchronous learning, where there is even less of a chance to interact with other students.

It’s easy to get frustrated when you can’t talk to your teachers and classmates face-to-face and voice the concerns you have immediately. However, there are things you can do to power through, including:  

• Find out if your course has a student support system in place. Some online courses have advisors who guide and support students throughout the duration of their online program .
• Check if your course offers networking opportunities for students. Some courses allow students to interact with their peers via chats and forums. It’s similar to interacting with classmates in a physical class, except it requires a little more effort to reach out. 
• Interact with your teachers and classmates during your online classes as much as possible. You can do this through social media outlets like Facebook groups and WhatsApp, email chat rooms, and classroom forums. To facilitate more interactions, be sure to ask lots of questions, organize group projects, and participate in discussions with your peers.

Combating isolation with online learning will take some effort on the students’ behalf, however once you’ve laid the foundation of pushing yourself out of your comfort zone to communicate with others, the rest will be easy! 

Online instructors, you can also help students overcome feelings of isolation by creating group projects and encouraging classmate interactions. As well, try to make yourself available at certain hours for students who want to reach you.

Lack of motivation

Feeling isolated also trickles into our next big problem that online students face, which is a lack of motivation to participate. Lack of motivation is a common issue amongst students. It requires a significant amount of self-discipline to learn online, and this is often a skill that needs to be consistently worked on. 

Due to a lack of face-to-face interaction, some students find it hard to focus during online classes. The physical absence of teachers or classmates takes away the sense of urgency to attend classes on time, meet deadlines, and make progress. This could lead to procrastination and declining grades. 

Staring at a screen for hours on end – even outside of online classes – can also deter learners from attending classes and completing their coursework in a timely manner. Learning online is not always as exciting as in-person lessons, so it can take a while to adjust. 

Here are some ways that students can increase their motivation to learn online and succeed academically: 

• Set realistic short-term and long-term goals to help stay on track with classes, assignments, and projects. To-do lists are great reminders for meeting deadlines, and crossing activities off a to-do list can be highly motivating.
• Reach out to a classmate (this also helps combating isolation) and hold each other accountable for attending online lectures, completing coursework, and finishing assignments and projects. 
• Practicing positive affirmations will help increase your motivation and drive to succeed with online learning. Giving yourself short pep-talk to affirm that you can do whatever you set your mind to will help keep you on track during tough times. 
• Regularly participating in class can provide a sense of belonging that keeps you motivated to continue learning. Ask questions, share your opinions, and engage in healthy debate. 

Teachers can also incorporate gamification in their online courses to motivate their students to attend and participate during online classes.

It’s hard enough to juggle your normal day-to-day activities without being a student. Adding online learning into the mix can make it even more of a challenge to navigate all these responsibilities. 

While online learning provides students with unparalleled flexibility to do other activities, the tradeoff is being able to manage your time in a responsible and effective manner. It can be extremely easy to fall into the habit of letting things slip, and before you know it you’ll be struggling to keep up with your online course. 

Time management is an important skill that helps students stay focused and disciplined – keeping your priorities in line will help you not only with online courses, but in all aspects of your life. 

Here are some ways to manage your time better for online classes: 

• Set a schedule and stick to it. This will help build discipline and keep yourself accountable. Make sure to include lots of mini breaks so that you don’t exhaust yourself!
• Create a priorities list, and work from most to least important. With time, this habit will increase your overall productivity.
• Set early deadlines so that you’re not scrambling to stay on top of your assignments. 
• Break tasks into smaller chunks instead of trying to complete them all at once. Trust us, your brain will thank you!

Teachers can also try to make it a priority to check-in on students, especially with asynchronous learning.  

We all know how easy it is to become distracted, nevermind learning online at home with ALL the distractions that you could ever imagine present! It takes some serious dedication and commitment to work successfully from the comfort of your own space. 

Along with in-person distractions, such as your TV, bed, making food, or roommates, there are also online distractions to be wary of. As wonderful as the internet is for learning purposes, it also comes with constant notifications from blogs, videos, and social media platforms. This can easily distract students from their classes and assignments, and it’s dangerous territory for falling into that rabbithole of mindless scrolling. 

If you’re getting distracted by your surroundings or procrastinating with social media, here are some things you can do to focus: 

• Dedicate a quiet area of your home that is free of distractions. This will help focus your mindset on the task at hand, which are your online classes. 
• Turn on social media blockers during classes and when you are working on assignments .
• Tell people around you about your daily schedule. You become more accountable when you tell others about your commitments and plans. Think of these people like human alarm clocks. 
• Leave your phone (and any other distractions) in a different room while you complete your coursework. You will feel less compelled to procrastinate, leaving you with a more efficient study sesh. 

If you are an instructor, you can help combat any learning distractions by using a dynamic learning design to make classes engaging for students . Encouraging your students to build things, take surveys, and have debates can help them concentrate more on their studies.

Technical issues 

Technical issues are the culprit of disengagement for online learning. Learning online requires teachers and students to understand how to use multiple forms of technology – some of which have steep learning curves.

From low internet bandwidth, spotty reception, and video glitches (to name a few), these issues disrupt the flow of learning and make it a tedious task.

With online learning, students need to find proactive ways to become their own IT department, as technological assistance may not always be available right away. 

To reduce technical issues that students and teachers experience during online classes, here’s some preventative measures to take: 

• Before enrolling in an online class, students should check if they have access to the necessary technology they need to succeed at home. If they don’t, they should check if the school offers technical help (via phone, email, and live chat) to online students.
• When attending online classes, students and teachers should use a high-quality internet service provider (ISP) for fast connection. If they don’t have access to a good ISP at home, they can use free Wi-Fi at a public library or coffee shop nearby. 
• As an online student, search engines are your best friend! More often than not, you can find the answer to your tech problem by plugging your question into Google. 

Teachers should provide a comprehensive guide that contains IT information and digital literacy guidelines to streamline the process for students if something goes wrong. It’s also very helpful for teachers to record class sessions in case some students miss lectures due to tech issues.

Communication

It can be more challenging for students to communicate with their peers and instructor in an online environment. Learning online doesn’t come with the option to walk up to the teacher after class (unless your instructor allows questions in synchronous classes), so students can feel more alone if they are confused. 

Even when a student asks a question online, they might not receive a response right away depending on the availability and timezone of their instructor. 

• Most of the time, the answers will be in the student syllabus. Make sure you carefully go over the course outline, as you may have missed the answer you are looking for. 
• Post your questions in student groups. Chances are, one of your peers will be able to help you out, especially if they’ve already asked the same question or have taken the course before. 
• Take advantage of online office hours if the teacher provides them. Then you know an exact time for when your instructor can provide assistance. 

As a teacher, you will want to be proactive when planning your course. Be sure to provide you students with an in-depth outline of the course that covers common questions and solutions. This will help in the long run, so you don’t have a herd of students banging on your virtual door looking for answers!

Some students may have problems with online classes due to learning difficulties or disabilities. Students with dyslexia, autism, poor vision, hearing impairment, and other disabilities need extra attention to succeed academically. 

Online learning is praised for its adaptability and inclusivity, which means that if you inquire about accommodations, the course creator or institution could most likely work with you to improve usability. 

As an instructor, here are some ways you can make your online course more universally accessible to all learners, including those with learning disabilities: 

• Include captions to your audio and video content for students with hearing impairments.
• Have voice-over descriptions of text and images.
• Provide alternative learning options like keyboard shortcuts for certain exercises.
• Use AI-powered personal assistants for students with special needs.
• Hold extra office hours for those who need extra assistance.
• Offer assignment extensions.

Related: The Most Common Barriers to Learning – And How to Overcome Them

Since we’ve covered A LOT of information in this post on how to overcome challenges with online learning, here is a summary of the most important takeaways: 

• Practice self-discipline by creating an online learning schedule 
• Connect with classmates to motivate each other 
• Increase motivation by practicing good online study habits 
• Take study breaks to avoid burnout and lower screen-time levels 
• Dedicate a quiet study space with no distractions
• Be proactive when looking for answers – but don’t be shy when asking questions
• Set early deadlines to stay on top of assignments 
• Become familiar with online support systems in place 

There you have it! A complete overview of the top challenges with online learning, and how to effectively manage these obstacles.

We hope you are able to implement these solutions into your online learning journey, and embrace online education with confidence. 

If you’re an online creator looking to break into the lucrative industry of online teaching, try Thinkific today. 

This blog was originally published in August 2022, it’s since been updated in April 2024 to become even more useful.

Highly creative and curious about life, Megan is a blog writer and content creator who loves to inspire and uplift people with the written word. During her free time she is an avid yogi, travel junkie, beach enthusiast, and reader.

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Challenges of Distance Learning for Students

Distance learning enables students to access and engage with quality educational content, all from the comfort and convenience of home. Though COVID-related restrictions will likely begin to relax as the pandemic wanes and vaccination rates rise, it seems unlikely that the e-learning industry, which experienced explosive growth in 2020 , will become obsolete in 2021. On the contrary, the virtual education industry had a value of $171 billion in 2019 — that is, prior to the coronavirus pandemic — “and is expected to grow [by] 10.85% by 2025,” according to PR Newswire . 

While it’s easy to see both the benefits and relevance of distance education, such as increased flexibility, mobility, and affordability, it’s equally important to acknowledge the potential challenges with distance learning for students. From grade-schoolers to graduate students, e-learners at all age levels must overcome obstacles that are unique to virtual education, such as asynchronous communication and ever-present opportunities for time-wasting distractions. E-learners also face challenges that, while often present in the traditional classroom, can be amplified in a virtual setting, such as delayed or unclear instructor feedback. 

As an educator or aspiring teacher, it’s important for you not only to understand and empathize with the challenges of online education for students — but even more critically, to help implement effective solutions. While e-learning may pose certain difficulties for students, it also creates exciting educational opportunities, opening doors for young and adult learners alike. To help your students seize these opportunities, learn about the challenges of online distance learning they face — and what steps you can take as a digital instructor to help your e-learners succeed. 

What Are the Challenges of Online Learning for Students?

What are the challenges of distance education for students who attend classes online? Here are nine virtual learning disadvantages that educators need to be aware of. 

1. Ineffective Time Management 

Research has proven that successful “time management is associated with greater academic performance and lower levels of anxiety in students.” Unfortunately, the same research also uncovered that “many students find it hard to find a balance between their studies and their day-to-day lives” — and worse, that ineffective time management was associated with outcomes like “poor sleep patterns” and “increased levels of stress.” Effective time management can be especially difficult in a distance learning environment, where students are challenged to pace themselves — without the support from friends and peers that would help them stay focused in class. 

2. Lack of Communication 

In an in-person setting, communication happens instantaneously, making it easy for students to get answers and clarify points of confusion. In an e-learning setting, communication is often asynchronous, which means there’s a gap between teacher and student. It’s easy for misunderstandings to develop in these gaps — sometimes, allowing a problem to snowball before it can be corrected. 

3. Not Receiving Timely Feedback  

Providing feedback is one of the most important and meaningful ways that a teacher engages with a student. When feedback is delayed by additional days or weeks because of an online format, students can become confused or uncertain about your expectations, their progress, and their performance in your class. 

4. Not Receiving Clear Instructions or Expectations

It’s always crucial to set clear expectations for students. Otherwise, they can only guess at whether they’re performing tasks and projects correctly. While setting clear standards is a challenge in any classroom, asynchronous communication can make it a greater obstacle. 

6. Technical Difficulties

Technical issues represent a significant barrier to effective online learning. Many students face challenges related to inadequate access to technology or unreliable internet connectivity. These technical difficulties can disrupt the learning process, leading to frustration and disengagement. Institutions and educators must ensure that students have access to the necessary technological resources and provide support for those who encounter technical issues. This may include offering alternatives for students with limited access to high-speed internet or providing technical support hotlines.

7. Isolation and Lack of Social Interaction

The absence of physical presence in a classroom setting can lead to feelings of isolation among online learners. The lack of face-to-face interaction with peers and instructors can diminish the sense of community and support that is often found in traditional educational settings. To address this challenge, educators can create opportunities for social interaction through virtual study groups, online discussion boards, and live video conferencing sessions, helping students feel more connected and supported.

8. Adapting to New Learning Styles

Online learning requires students to adapt to new styles of learning that may differ significantly from traditional classroom experiences. This adaptation process can be challenging, as students must become proficient in navigating digital platforms and learning resources. The shift to online learning necessitates the development of new skills, such as researching online databases and effectively communicating through digital mediums. Educators can facilitate this transition by providing comprehensive guides and tutorials on using online learning platforms and digital tools.

9. Distractions at Home

The home environment, while comfortable, is often filled with distractions that can impede the ability to focus on studies. From household chores to social media, numerous distractions can detract from the learning experience. Students must find strategies to minimize these distractions, such as setting up a dedicated study space and using time management tools to allocate specific times for studying. Educators can support students by offering advice on creating an effective learning environment at home and encouraging regular breaks to maintain focus.

Challenges for Adult Learners

Adult learners face a unique set of challenges when engaging in online learning. Balancing work, family responsibilities, and education is a complex juggling act that requires significant time management and organizational skills. Moreover, re-adapting to an educational setting after a prolonged absence from formal learning environments can present additional hurdles. Let’s delve deeper into these challenges and explore strategies to navigate them successfully.

Balancing Work, Family, and Education

The task of balancing work and family responsibilities with educational pursuits is a significant challenge for many adult learners. Unlike traditional students, adult learners often have full-time jobs, family obligations, and other life responsibilities that demand their time and attention. This can make dedicating time to coursework and studying a challenge.

Now that we’ve explored the challenges of online learning for students, let’s focus on something even more important: how to overcome them.

6 Practical Solutions to Distance Learning Problems for Students

Fortunately, it’s possible for educators to mitigate distance education issues and challenges for students. Here are four steps that teachers can take to help position e-learners for greater success in the virtual classroom.

1. Share Time Management Apps and Resources for Students

Effective time management is a fundamental skill for distance learners. Encourage your students to take advantage of the numerous time management apps and resources that are available to e-learners — many of them for free. For example, National University offers a comprehensive suite of time management resources for students , including daily planner worksheets, infographics, links to apps, helpful time management tips , and even a time management calculator. 

Education experts also recommend periodically surveying your students, which provides you with actionable insights into how your students allocate their time toward various tasks. Once you identify the trouble spots that are slowing your students down, you can offer them tailored guidance — especially if you notice patterns emerging in your survey data. 

2. Overcoming Technical Challenges

To mitigate technical issues and boost digital literacy, it’s crucial for institutions to offer robust technical support, guiding learners through any technical difficulties encountered. Furthermore, providing digital literacy training enhances students’ ability to navigate online platforms and digital tools with ease. Ensuring that learning platforms are accessible on mobile devices allows students the flexibility to engage with their coursework from anywhere, at any time, making online learning more adaptable to their lifestyles.

3. Building a Community

Cultivating a sense of community is vital in online learning environments. By promoting the creation of virtual study groups using tools like Zoom or Google Meet, students can collaborate and support one another academically. Online forums and platform-specific social media groups offer spaces for learners to connect, share insights, and foster a supportive network. These initiatives are key to making online education a more interactive and socially connected experience.

4. Utilize Educational Technology (“EdTech”)  

Just because communication occurs over the internet doesn’t mean it has to be lagged or asynchronous. In fact, there are countless tools — many of them free to use — to help students and teachers communicate in real-time. For example, you can use video conferencing software to have live conversations with your students, either one-on-one or in group settings. This gives your students a chance to ask you questions, raise concerns, and work through complex course material more successfully. In addition to video conferencing software, you can also use instant messaging apps for students who prefer to communicate via text. Examples include Skype, Google Meet, FaceTime, Zoom, and Google Hangouts. 

5. Increase Peer Review 

Students need timely, meaningful feedback in order to gauge and improve their performance. There are several ways you can improve the feedback your students receive. 

One method is to schedule one-on-one or group sessions with your students — for instance, on a weekly or bi-weekly basis — that are dedicated to providing feedback on recent assignments. Providing verbal feedback lets you save time on writing and editing documents, without sacrificing the detail or quality of your evaluation. As a result, your students receive better feedback, sooner. 

Another method is to let your students engage in more peer review, or the process of providing feedback on each other’s work. In one fascinating experiment, a Duke University instructor permitted 100% of grading to be handled by a peer review process, with unsurprising results : increased satisfaction for students, and decreased stress on instructors. 

Want to take a deeper dive into this subject? Here are even more tips on providing effective feedback to online students .

6. Provide Clear Grading Rubrics  

Rubrics and syllabi are important tools in the traditional classroom. Make use of them in the virtual classroom, too! Be sure to provide your online students with a clear and detailed overview of the course, including information about: 

• What type of material you’ll be covering
• What items each student will need
• How each type of assignment will be graded
• How to share or upload documents
• What to do if they experience technical issues
• Deadlines, exam dates, days off, and other special calendar events 
• How to contact you 

While there are many distance education issues and challenges for students to overcome, there are also countless opportunities for them to seize — and the approach you take as an educator can make all the difference. We hope that, by sharing some of these solutions and strategies, we’ve made it a little easier for you to help your e-students turn their challenges into success stories. 

Future of Online Learning

Addressing these challenges can significantly shape the future of online education, making it more accessible, engaging, and effective. Innovations in technology and pedagogy will continue to transform online learning, offering personalized, flexible learning experiences that meet the needs of diverse learners.

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School Climate

45 survey questions to understand student engagement in online learning.

In our work with K-12 school districts during the COVID-19 pandemic, countless district leaders and school administrators have told us how challenging it's been to  build student engagement outside of the traditional classroom. 

Not only that, but the challenges associated with online learning may have the largest impact on students from marginalized communities.   Research   suggests that some groups of students experience more difficulty with academic performance and engagement when course content is delivered online vs. face-to-face.

As you look to improve the online learning experience for students, take a moment to understand  how students, caregivers, and staff are currently experiencing virtual learning. Where are the areas for improvement? How supported do students feel in their online coursework? Do teachers feel equipped to support students through synchronous and asynchronous facilitation? How confident do families feel in supporting their children at home?

Below, we've compiled a bank of 45 questions to understand student engagement in online learning.  Interested in running a student, family, or staff engagement survey? Click here to learn about Panorama's survey analytics platform for K-12 school districts.

Download Toolkit: 9 Virtual Learning Resources to Engage Students, Families, and Staff

45 Questions to Understand Student Engagement in Online Learning

For students (grades 3-5 and 6-12):.

1. How excited are you about going to your classes?

2. How often do you get so focused on activities in your classes that you lose track of time?

3. In your classes, how eager are you to participate?

4. When you are not in school, how often do you talk about ideas from your classes?

5. Overall, how interested are you in your classes?

6. What are the most engaging activities that happen in this class?

7. Which aspects of class have you found least engaging?

8. If you were teaching class, what is the one thing you would do to make it more engaging for all students?

9. How do you know when you are feeling engaged in class?

10. What projects/assignments/activities do you find most engaging in this class?

11. What does this teacher do to make this class engaging?

12. How much effort are you putting into your classes right now?

13. How difficult or easy is it for you to try hard on your schoolwork right now?

14. How difficult or easy is it for you to stay focused on your schoolwork right now?

15. If you have missed in-person school recently, why did you miss school?

16. If you have missed online classes recently, why did you miss class?

17. How would you like to be learning right now?

18. How happy are you with the amount of time you spend speaking with your teacher?

19. How difficult or easy is it to use the distance learning technology (computer, tablet, video calls, learning applications, etc.)?

20. What do you like about school right now?

21. What do you not like about school right now?

22. When you have online schoolwork, how often do you have the technology (laptop, tablet, computer, etc) you need?

23. How difficult or easy is it for you to connect to the internet to access your schoolwork?

24. What has been the hardest part about completing your schoolwork?

25. How happy are you with how much time you spend in specials or enrichment (art, music, PE, etc.)?

26. Are you getting all the help you need with your schoolwork right now?

27. How sure are you that you can do well in school right now?

28. Are there adults at your school you can go to for help if you need it right now?

29. If you are participating in distance learning, how often do you hear from your teachers individually?

For Families, Parents, and Caregivers:

30 How satisfied are you with the way learning is structured at your child’s school right now?

31. Do you think your child should spend less or more time learning in person at school right now?

32. How difficult or easy is it for your child to use the distance learning tools (video calls, learning applications, etc.)?

33. How confident are you in your ability to support your child's education during distance learning?

34. How confident are you that teachers can motivate students to learn in the current model?

35. What is working well with your child’s education that you would like to see continued?

36. What is challenging with your child’s education that you would like to see improved?

37. Does your child have their own tablet, laptop, or computer available for schoolwork when they need it?

38. What best describes your child's typical internet access?

39. Is there anything else you would like us to know about your family’s needs at this time?

For Teachers and Staff:

40.   In the past week, how many of your students regularly participated in your virtual classes?

41. In the past week, how engaged have students been in your virtual classes?

42. In the past week, how engaged have students been in your in-person classes?

43. Is there anything else you would like to share about student engagement at this time?

44. What is working well with the current learning model that you would like to see continued?

45. What is challenging about the current learning model that you would like to see improved?

Elevate Student, Family, and Staff Voices This Year With Panorama

Schools and districts can use Panorama’s leading survey administration and analytics platform to quickly gather and take action on information from students, families, teachers, and staff. The questions are applicable to all types of K-12 school settings and grade levels, as well as to communities serving students from a range of socioeconomic backgrounds.

In the Panorama platform, educators can view and disaggregate results by topic, question, demographic group, grade level, school, and more to inform priority areas and action plans. Districts may use the data to improve teaching and learning models, build stronger academic and social-emotional support systems, improve stakeholder communication, and inform staff professional development.

To learn more about Panorama's survey platform, get in touch with our team.

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The challenges and opportunities of online learning and teaching at engineering and theoretical colleges during the pandemic

Emad mushtaha.

a Department of Architectural Engineering, Civil and Environmental Engineering, Industrial Engineering and Engineering Management, Sustainable Engineering Asset Management (SEAM) Research Group, College of Engineering, University of Sharjah, United Arab Emirates

Saleh Abu Dabous

Imad alsyouf, naglaa raafat abdraboh.

b Department of Biochemistry, Dubai Medical College, United Arab Emirates

Education has been one of the major areas disrupted by the COVID-19 pandemic. This study aims to assess the impact of the COVID-19 pandemic on users’ (students and faculty members) learning in higher education, examining how engineering students and faculty perceived the abrupt transition in comparison to other colleges. The current research aims to investigate the outcomes of enforcing eLearning to facilitate teaching and learning processes in higher education after this unprecedented pandemic and identify the most significant challenges and opportunities the users face. This study uses a quantitative approach; it included 1713 respondents, 227 full-time faculty members, and 1486 students at the University of Sharjah. The survey analysis indicated general agreement that the most significant advantage of online learning implementation was its flexibility in place and time, with 77.2% of users providing positive feedback. Moreover, the accessibility and effectiveness of the assessment and communication methods used showed a positive trend in the hypotheses, 80.3% of the users. The sudden implementation of eLearning during the COVID-19 pandemic had discouraging implications for users' mental health and socialization, where 55.6% of the sample agreed that they had been affected negatively. 75% of the users prefer a flexible model blending face-to-face and e-learning techniques rather than solely depending on either of them. Therefore, A Hybrid-Flexible (HyFlex) is recommended for the university to apply based on the nature of the courses.

1. Introduction

The spread of the infective disease caused by the COVID-19 has caused an international health pandemic that has had challenging ramifications socially and economically and on education as one of the significant areas disrupted. However, this disruption could be the beginning of a surprising innovation because the institutions, including educational ones, responded to the travel bans and home quarantines by shifting to online interaction. Although major issues arise from the crisis that caused the shift, users worldwide wonder whether COVID-19 could act as a catalyst for the online progress in educational procedures and accelerate the use of the latest technology to the point where the future becomes the present. The coronavirus pandemic has revolutionized global education through the unprecedented introduction of new solutions, tools, and applications. According to UNESCO’s statistics, the closure of universities worldwide to contain the COVID-19 pandemic has affected 91% of the student population. The educational organizations in the UAE have taken action to ensure that faculty and students are equipped with the technical support and tools needed to go through the crisis, and the decision was made to implement e-learning in all schools and universities until further notice. This raised many doubts [9] whether enough flexibility had been built into the education system and whether students and faculty members were capable of enough adaptability, decision making and problem-solving skills, and above all of dealing with the technological advances. However, as [11] state, today’s generations are digitally driven and do not find it hard to use online platforms. Online learning could become the main form of education and respond to situations students cannot meet on campus. Countering the current challenges may lead to an improved educational environment in the future. The case study in the present research has offered several online courses; however, the ratio of online to traditional courses was comparatively low. However, the outbreak of Coronavirus compelled the university to launch eLearning programs to ensure regular teaching action for 15,952 students, 13,235 undergraduate students, and 1,613 graduate students. Moreover, a further 453 were enrolled on two-year diploma courses, and of course, 636 enrolled faculty members were also involved. These statistics are based on documents retrieved from the statistical specialist at the university and last updated in the academic year 2019/2020. The action was taken to avoid any delay or interruption in the education system; at great speed, millions of teachers were obliged to teach students on-screen who had to stay at home and receive tuition through the internet. Beyond the UAE, after the spread of Coronavirus across the globe, on 13th March 2020, around 60 countries in Africa, Europe, Asia, the Middle East, and North/South America announced or implemented school and university closures [ 41 ]. Although online learning has already been incorporated in many universities, the disruption caused by COVID-19 enforced the online learning programs to serve the whole off-campus population without considering users' perceptions or their readiness to make use of them. The enforcement of e-learning was designed to lessen the effects that accompanied the sudden closure of higher educational institutes; faculty members and students had to face and depend entirely on the use of high-tech tools and platforms to guarantee sustained teaching and learning. This research aims to analyze the opportunities and challenges that higher education users (students and faculty members) have faced as a result of the COVID-19 pandemic’s online learning. A case study assessed the perception of the University of Sharjah users on the performance and effectiveness of the e-Learning techniques and methods put into action during the COVID-19 pandemic for learning and teaching. The study analyzes people’s views, particularly those of the engineering college (the largest practical college), and compares it with the other theoretical colleges to determine contrasts and parallels. Furthermore, the study addresses future implications and the potential of blended learning as a future solution.

1.1. Overview on eLearning

Technology is an essential element of learning in the 21st century. The increase in the use of technology in education had altered educators’ attitudes from the traditional ones when they were distributors of knowledge to a new and more flexible attitude now that they are considered more as supporters and motivators who urge and encourage students to participate and learn [26] . Moreover, as Shadiev & Sintawati [35] suggested, technology supports intercultural learning on many levels. The role of technology could also be the facilitator of personalized learning that allows students to achieve better learning outcomes [51] . E-Learning is a futuristic mode of education that accommodates the different requirements and expectations of different users; in this way, it allows varied methods of educational technology to operate, redesigns instructional methods, and refines performance and effectiveness to adapt to the priorities of eLearning [12] . Strydom [38] reasons that university structures are rigid and untested in integrating eLearning courses; this is not easy to implement without the support of faculty members and the cooperation of its users. Volery and Lord [43] suggest that the fast growth of technological development has allowed higher education organizations to develop the quality of their students’ learning by introducing eLearning courses, which counter social requirements and allow resources to be used effectively. However, there is not enough eLearning technology yet. This lack could limit the transfer of knowledge from universities to more people worldwide, which runs counter to the concept of globalization. In the COVID-19 crisis, the greatest challenge was whether the current higher education structures could adapt quickly to the process of change and cope with the unexpected implementation of technology.

1.2. E-Learning during a pandemic

After the sudden shift due to COVID-19, faculty members who had to adapt to technology faced several challenges, such as a lack of eLearning experience and/or too little time to prepare online courses. Their efforts to deploy the new educational delivery system using online techniques and materials affected the performance of the students and instructors. Although users were already familiar with the LMSs (Learning Management Systems) that used to complement face-to-face interaction, the change to online instruction revolutionized learning concepts and gave greater emphasis to the characteristic of an individual’s learning and the central role of the lecturer. Well-designed e-learning practices differ significantly from the emergency courses provided in response to the COVID-19 pandemic. According to Purcell & Charles [29] , higher educational institutes working to preserve education during the COVID-19 pandemic should understand the differences when assessing alternatives in distant teaching. In their research, it is suggested that everyone included in the sudden shift to eLearning had to understand that such disasters also create disturbances to the lives of students and faculty members outside their life on campus. Instructors and supervisors are advised to consider that students might not be able to attend courses straightaway and be more flexible over policies and the deadlines for students’ assignments [14] . Likewise, Zhao et al. [52] emphasized the importance of acknowledging students’ social classes and the availability of educational resources among students from lower socioeconomic backgrounds. Their study also addressed the negative impact of the pandemic on college students, such as increased stress, anxiety, and depression due to isolation, emergencies, and uncertainties. Moreover, the threat of COVID-19 has presented some unique challenges for all the parties included in higher education institutions, such as being asked to do unusual things concerning course delivery and learning. These challenges have not been seen before on this scale in their lifetime. Thus, and as suggested by Yin and Mahrous [49] , “workplace spirituality” has never been more critical; it’s necessary to build and ensure growth in a business environment by creating strong connections between employees and their organizations, as well as instilling meaning and a higher purpose in the workplace.

Abusaada and Elshater (2020) further imply the need for urban planners and designers to learn from the faced challenges to address social well-being during such times. The authors propose that computer simulations be used to envision open public places that adopt design rules that increase meditation possibilities, guide people toward diverse areas of interest and promote positive environments while respecting social distancing and precautionary measures. Similarly, Abusaada and Elshater [1] encourage optimistic thinking about the crisis’s difficulties and advocate for designers to refrain from solutions that increase monotony and boredom. Due to outdoor public spaces’ role in spreading the virus, the authors further advise designers to depend not just on standards, recommendations, and common methodologies to lower infection rates, but also on inventive solutions that suit users' physical distancing demands. Purcell & Charles [29] add that after the pandemic is over, the universities should highlight the strengths and weaknesses of the current eLearning process to be better prepared should similar events occur in the future and know what blends best with traditional methods of learning.

2. Literature review

A wide-ranging literature review covering eLearning and the perceptions of its users was conducted Table 1 . The review indicated that not many studies had hitherto covered the impact of the COVID-19 pandemic and the major challenge that it posed to educational structures since the systems had not anticipated nor been entirely ready for the sudden switch to online learning. According to Taylor et al. [40] , the learning transfer systems will improve and build up over a certain period, given that the precautions were first taken halfway through the academic semester. The vast, disrupting shift needed to suddenly transform all the ongoing courses into online courses in a matter of days required an elaborate plan and learning materials such as recorded instructional videos or electronically communicating groups. Moreover, other studies highlighted the psychological effect on university students in terms of fear, worry, or anxiety, whether because of the impact of COVID-19 on their education or their chances of future employment [44] . Anxiety disorders were exaggerated by social distancing during the quarantine period [20] . Universities and faculty members should not be perceived merely as carriers in delivering knowledge: they should also regulate what they teach and conduct it. Technology promotes the role of instructors from being simply information transmitters to dynamically functioning as co-creators of knowledge among their students. Onyema and Alsayed [25] , having discerned the negative impact of COVID-19 on education, recommended that all educational organizations, teachers, and students need to adopt technology and develop their digital skills in line with the developing universal trends and conditions in their field. Given the lack of literature covering the sudden transformation in response to a crisis such as the COVID-19 pandemic, it was necessary to identify the challenges accompanying this change, assess them and identify the parameters associated with online learning.

Literature reviews of eLearning and the impact of COVID on education.

2.1. Literature related to online learning

Several studies have analyzed the effects of online learning on teaching and shown how this sort of teaching emerges from the need for education at a distance. There are wide-ranging eLearning tools that facilitate online education, especially during epidemics such as the COVID-19 pandemic; these technologies and online platforms can reduce the gaps in education and reach everyone across the globe. Previous research has produced both positive and negative insights into online learning associated with students and teachers. One of the most valued aspects of e-learning based on past studies is its flexibility, whether in terms of time or location; this gives students more options to interact with lecturers or their peers. According to Wheatley & Greer [46] , the primary benefit of the eLearning process is that it saves time, lets instructors handle larger numbers of students without the worry of timing conflicts, and ultimately reduces the overhead costs of faculty. Romeu Fontanillas et al. [33] analyzed the students’ perception of E-assessment, and the results revealed a high level of satisfaction with the e-assessment activities of the course and an improvement of the learning process. A study by Alhefnawi [2] , investigated the effectiveness of online handouts (blackboard documents) and active lectures in improving students’ performance, specifically engineering undergraduates. While both presenting techniques had beneficial impacts, students preferred active lectures, which resulted in higher ideal responses. Another aspect revealed by Bisciglia & Monk Turner [5] is that eLearning obviates traveling time and expense, especially in fields where information frequently changes. Other researchers have added that, even in eLearning-based education, presence in a community can affect learners’ contentment and, thus, the motivation for eLearning [37] . Richardson et al. [31] found that social presence was a major forecaster of student fulfillment with LMS-based courses. Other studies, however, suggest that eLearning courses had drawbacks in terms of the lack of clear and specific face-to-face interaction between students and teachers or with other students; as a result, students might lose track of the learning process [48] , [50] , while more technical difficulties might cause distress to users. Moreover, considerable time is needed to adapt to new technology and the interruptions to communication that occur in the process; thus, it is always necessary to provide enough training to faculty members and students in using the technology to improve their satisfaction with online courses [15] . Other disadvantages mentioned in the literature include the inexperience of eLearning among teachers [50] , the lack of tutorial support (Li, 2009), and planning student courses without considering what is appropriate. These issues could offset the convenience offered by eLearning. Users might encounter problems of understanding course information that is technical, quantitative, or scientifically oriented, leading to a failure to deliver course outcomes efficiently. Students also regarded technical problems as critical obstacles to eLearning. For instance, Ibrahim et al. [16] conducted a similar study on online education, focused on architectural design and fundamental design courses. According to the findings of the survey, 94.4% and 48.8% of students experienced technical challenges in design classes and basic design courses, respectively. The encountered difficulties included low internet speed and troubles with their computer devices. These findings align with a relevant study conducted by Noori [24] regarding the pandemic’s impact on higher education in Afghanistan. Noori’s qualitative analysis revealed that nearly all students faced Internet and technical concerns including financial problems to attain improved Internet bundles and inadequate full-time power supply. Moreover, lack of immediate feedback plays a significant role in students’ negative perceptions of the online learning process [36] . The conditions of eLearning and working or learning from home could be very challenging to many instructors and students, particularly those who have difficulties in such areas as the accessibility, obtainability, and use of technology in learning besides affecting positive teacher-student relationships, which is one of the process goals of education [10] .

Instructors and students perceive the online process differently; thus, this paper approaches its methodology based on the assumption that different users have different attitudes to online learning. Although previous writers assumed that lecturers were concerned about interaction aspects, they welcomed the idea of replacing traditional courses with online teaching [17] . Shin, et al., [34] suggest that peer motivations may influence achievement by identifying processes, while instructor rationales emphasize students’ attention to content. The positives and negatives in the literature regarding the perceptions of the eLearning process among students and faculty members raised a research question, knowing that it had never been implemented on such a scale before, what were the perceptions of the sudden implementation of distance learning following COVID-19 in the case of the University of Sharjah? The studies in the above review were analyzed to identify the specific parameters surrounding the COVID-19 pandemic and the impact of online learning on teaching and learning.

3. Research questions

The analysis of the literature review covered the sudden introduction of distance learning in the case study due to the COVID 19 pandemic and the way that the users perceived it, which in turn led to the following questions:

• • What has been the impact of the pandemic on the users’ performance in the University in terms of learning, teaching, and assessment?
• • What are the similarities and differences of the pandemic impact on the University’s various colleges?
• • What is the perception of users about the future expectations of eLearning?

4. Research objective

The research aims to assess the impact of enforcing eLearning in consequence of the COVID 19 pandemic on facilitating the education process and examine the future implications of the eLearning process on the educational system to know whether or not new learning or teaching models can be introduced to the educational structures.

5. Research methodology

This study uses a quantitative research methodology which is defined as a form of educational study in which the scholar chooses what to assess; asks precise, well-constructed questions; gathers quantitative data from participants, evaluates the information using statistics; and evaluates in an unbiased, detached manner’ [7] . Several steps were taken to evaluate the impact of COVID-19 in the present study. In the literature review, different scholars mentioned several parameters that affect the perception of users of online learning in higher educational institutes. These parameters were used to create a survey that would allow students and faculty members to report their online experience at this time. An assessment of the methods used in previous papers concluded that surveys were the most viable way to collect data from the respondents [48] ; Li, 2009; [18] . In this study, the surveys took the form of a questionnaire, and the data thus gathered were statistically analyzed to test the research hypotheses. To provide an indicative overview and assess the users’ perspectives, the engineering college was selected as the study’s baseline case. Since this college is the largest at the university with numerous departments, the study seeks to examine it against other colleges to address the research questions. In the following sub-sections, the survey study design and the methodological processes used in this study are discussed.

5.1. Survey research design

According to Prickett and Rapley [28] and Author (2018), three prominent aspects should be considered in creating a survey: the survey’s design, its procedure, and the design of the sample. The survey was created using an online template through Google forms and disseminated to the sample of respondents. This study used a single survey to assess the perceptions of users, designed by selecting a list of parameters from previous studies based on the frequency with which they occurred in the studies. The parameters were correlated with the questions to be used; the purpose was to suggest a model for benchmarking eLearning in higher education institutes and the relationship between learning strategies in online education and academic performance [22] . The gathered parameters were used to create a set of questions about the perceptions of faculty members, graduate students, and undergraduate students on three main aspects of the online learning experience: Performance, Effectiveness, and Future Prospects. Then the survey and the non-parametric hypothesis testing were used to assess the impact of COVID-19 on teaching and learning. A pilot study sent to a limited number of faculty members and students was conducted to test the survey questions’ reliability, transparency, and rationality and get responses to the design and deal with any queries before distributing the survey to the entire university.

5.2. Design of the sample

The target sample of the survey included faculty members, graduate students, and undergraduate students. This university represents the higher educational institutes in the UAE; it has over 15,000 students and over 600 faculty members. The cut-off date of the survey was 14th July 2020, and the number of responses from all the colleges in the university totaled 1719. The least representative sample size was estimated using the modified sample size equation, Equation (1) [47] . This was based on the sample size formula of William Cochran [8] , where n is the sample size, N is the population size, e is the margin of error. In the present research, the total number of students and faculty members, N = 16,588, and the margin of error was chosen to be 3 %; thus, the minimum required sample size was 1,042; in the event, an even larger sample volunteered to respond. Fig. 1 represents the total number of respondents to the questionnaire, showing that the minimum sample size for each category was achieved successfully.

Sample of the survey distributed to students.

5.3. Methodological procedure

Fig. 2 illustrates the systematic methodological approach followed in this study; the identification of parameters was then related to the sudden implementation of eLearning to analyze the perceptions of their experience among the users and assess whether teaching and learning were affected.

Methodological procedures (source: The Authors).

5.4. Selecting the parameters

At this stage, a wide-ranging review was conducted of the literature that assessed the influence of online learning on education, and several parameters were chosen based on their frequency of occurrence in the literature (see Table 2 ). This review helped to identify different scholars’ choices of the most effective parameters, which were then visually represented in a pie chart in Fig. 3 . The parameters comprised motivation, attitudes, knowledge acquisition, accessibility, and adaptability, in addition to instructors’ support, assessment, and evaluation, technical issues, and difficulties, interactivity, and flexibility. These parameters were used to evaluate the perception of performance among the users. In the second section, the effectiveness of the techniques used was evaluated as Sullivan [39] suggests the effectiveness of the eLearning should meet the criteria of online education and provide learners with the understanding and knowledge that they would have gained from traditional class education. This could be ascertained through properly evaluating the methods and components of online learning education. The third section aimed to evaluate the future implications of distance learning and to judge whether a blended model could be an efficient solution in future years.

Parameters identified from the literature.

Parameters occurrence based on the percentage.

5.5. Assessing the impact of the COVID-19 pandemic and the sudden implementation of online learning on teaching and learning by validating the perceptions of the parameters

Not much literature has been found on the recent impact of COVID-19 on teaching and learning; hence it seemed helpful to assess the impact of the sudden implementation of online learning on the above parameters. The questionnaire created for this purpose asked the students and faculty members how they had perceived this impact. To facilitate data collection and avoid lengthy surveys and bias in the answers, the survey was presented in three sections, and all the questions could be answered using a 4-point Likert scale, where 1 signified strongly agree, and 4 signified strongly disagree. The respondents were given 25 questions that would reveal their perception of the online learning experience during the COVID-19 pandemic and their personal experiences. A 4-point Likert-scale questionnaire is an example of an even-point scale that eliminates the mid-point. It was chosen mainly because a scale without a “neutral” point seems to help eliminate the social desirability bias without changing the direction of people’s opinions. This is sometimes called a “forced choice” approach since it removes the neutral option [3] . A study conducted by Armstrong and Robert [4] suggests that the middle option becomes the only option when a respondent is undecided; therefore, it is debatable whether it is a neutral option. The research concludes that negligible variances were found between using and eliminating “undecided” as the middle option in a 5-point Likert scale. Another study [32] provides evidence that a social desirability bias resulting from respondents’ desire to satisfy the interviewer or not to seem to answer in a socially unacceptable way may be reduced by eliminating the mid-point, such as the “neutral” category, from Likert scales. Thus, the respondents were requested to evaluate their perception of every parameter listed according to a 4-point Likert scale. The linguistic term was then transformed to its corresponding numerical value in which “Strongly Agree”, “Agree”, “Disagree” and “Strongly Agree” correspond to 1, 2, 3, and 4, respectively.

Each set of answers was statistically analyzed, and the hypotheses on the consequences of this survey were tested to establish whether the difference between responses was statistically significant or not. The first step in the hypothesis testing was to use Pearson’s chi-square test (see Equation (2) ), which is a method used in statistics that calculates the difference between observed and expected data values. The test establishes a statistically significant variation between the anticipated frequencies and the noted frequencies in the categories of a cross-tabulation table which is an arrangement where data is categorized according to two distinct variables. The chi-square test was carried out in SPSS to obtain the p-vlue, as shown in the sample in Table 3 .

Chi-square test sample for a question result extracted from SPSS.

a. 0 cells (0.0%) have an expected count<5. The minimum expected count is 31.91.

where: X 2 is the Chi Square obtained, ∑ is the sum of, o is the observed value and e is the expected value [23] .

The P-value separates the significant answers from the insignificant ones to eliminate the null hypothesis; in other words, there is no significant difference between distinct populations, and any practical difference observed is due to sampling or investigational error. If the normality hypothesis is violated, the non-parametric test is used. According to Montgomery and Runger [19] , the main goal of this test is to examine whether the samples have matching population medians. With the p-value calculated, the current hypothesis was formulated as shown in Equation 3, and the test was applied at the 5% significance level (α).

H 0 : μ = 3n = 1,2…24

H 1 : μ < 3n = 1,2…24

α = 0.05.

Decision rule: Reject H₀ if P value < 0.05 (3)

Where μ i represents the median of the responses received for the i th parameter; I = 1… 24, and the terms H 0 and H 1 are the null and alternate hypothesis, respectively. The alternative hypothesis means that the median of responses is affirmative, suggesting more “strongly agree” and “agree” answers than answers in disagreement. Rejection of H₀ means that the i th response is significant, while acceptance of H₀ leads to rejecting the response to the question. The hypothesis for each question was tested to show the differences between students and faculty members and then tested to show the differences between different colleges.

6. Results and discussion

The survey shown in Table 8 was distributed to all 14 colleges in the University in June 2020. Responses were received from 229 full-time faculty members and 1486 students (194 postgraduates and 1296 undergraduates). The characteristics of the participants are summarized in Table 4 . The survey asked participants to provide authentic responses about their experiences with this learning approach. Therefore, the results of the study are based on 1719 responses, which represent more than 10% of all the students and faculty enrolled in the University of Sharjah.

Participants in the survey, according to their colleges.

Questionnaire questions with the P-values and chi-square tests results.

6.1. Statistical analysis

The data were descriptively analyzed using the Statistical Package for the Social Sciences (SPSS for Windows, Version 24.0). Chi-square tests were applied as suitable for testing the presence of statistical significances between numerous measures in this study sample (see Fig. 4 ). Following the test, the data results were compared against the second hypothesis test, where the results with median ≤ 2 were positive and the results with median ≥ 3 were negative. The three sections of the survey were analyzed independently; the medians of the answers were analyzed using the Mann-Whitney U test to compare students and faculty groups, while the medians of the answers were compared between different colleges using the Kruskal–Wallis one-way analysis of variance. A P-Value < 0.05 was considered statistically significant.

Spearman Correlation charts between the samples of faculty members and students.

6.1.1. The performance of the engineering college in comparison to other colleges

Generally, in comparing the perceptions of users from different colleges as in Table 5 , the results clearly show that the experience of the online learning implemented during the COVID-19 pandemic had different effects on different users. Given that the engineering college comprises the largest number of enrolled students, it is selected as a base case for comparison. The results revealed some similarities between the engineering college and other practical colleges that taught subjects such as medicine and fine arts. These colleges demonstrated low improvements in performance and productivity and a lack of motivation.

Results of the first section of the survey.

However, disparities are noticed with theoretical colleges such as those teaching Sharia, business, arts, sciences, humanities, and law. Several enhancements in performance have been observed, including better experiences among learners and teachers, more productivity, and improved motivation on both sides. Nevertheless, there was general agreement that the biggest advantage of online learning was its flexibility in terms of place and time, with a total of 77.2% positive feedback, supporting the theory of Veletsianos and Houlden [42] , who argue that there is a need for educational organizations to have a structure that offers users more flexibility, as seems to have been achieved in the case study. Moreover, as [11] suggested, this generation is digitally driven and has no problem using the new technologies. This matches the findings of the current study that the ease of use of technology was clearly and constructively perceived. Communication, interactivity, and knowledge acquisition were among the aspects in which differences were also identified. The engineering college and medicine and fine arts had tendencies of negative perceptions of the effect of online learning on interactivity and motivation. The experience was described as one with negative social and mental health ramifications; the answers from the different colleges and positions showed the total negative perceptions as 55.6%, and some users claimed to have been highly stressed during the eLearning period. These results match the findings of Cao et al. [6] , who maintained that 24.9% of college students were afflicted with anxiety experienced because of the COVID-19 outbreak. On the other hand, the theoretical colleges identified as positively engaging. Swan et al. (2000); Picciano [27] ; in their papers supported this finding because it was suggested that interactivity and communication were improved through online learning. Surprisingly, however, faculty members seemed to give positive feedback on their improved involvement in socializing, which defies the social presence parameter mentioned by Richardson and Swan [30] . Students had more positive perceptions of interactivity with instructors than faculty members had with students. This opposes the theory of Inman et al., [17] , Wheatley & Greer [46] that faculty members are more comfortable with distance learning since they can interact with a greater number of students at once and save time and effort. Overall, and as expected, the engineering college had multiple parallels with other practical colleges and apparent differences with the theoretical colleges, whose responses appear to be more positive on all aspects of performance from the faculty members and students.

6.1.2. The effectiveness of eLearning during the COVID-19 online pandemic

As indicated in Table 6 , in the second section of the survey, users were asked to evaluate how effectively the eLearning techniques and assessment methods were used during the pandemic. The students and faculty members were new to these tools. Remarkably there was a common agreement among colleges and between positions that all the users quickly adapted to the online learning experience, with a total of 80.3% positive responses, which contradicts the contention of Schramm, Wagner, & Werner (2000), that users need a long time to adapt to the new technology and face many communication interruptions in the process. The easy accessibility of the learning and teaching tools and the ability to communicate more quickly through the new online social platforms made the experience much more convenient. Nevertheless, technical difficulties appear to have been a significant issue that 79.7% of users faced. However, the university’s technical support and that of the instructor’' support managed to overcome these challenges and fill the gaps discussed by previous writers, such as Galusha (1997), who stressed that eLearnin’'s major drawbacks are the lack of instructors’ support. Regarding the assessment of students, their grades during the pandemic show a major effect, of which 71.6% agreed. However, this does not gainsay the fact that the outcome of the courses was successfully achieved, and the assessment methods used on students by the University of Sharjah provided them with fair, convenient, and timely feedback. This opposes the challenge of a lack of feedback mentioned by Strong [36] . While there were some negative perceptions of users to performance, most users agreed about all the responses on the effectiveness of the eLearning methods used so far during the pandemic with a median of 2, signifying “agree”.

Results of the second section of the survey.

6.1.3. Future implications

As indicated in Table 7 , in the third section, the participants were asked what they thought about teaching after the COVID-19 pandemic is over. Students and faculty members favored the implementation of eLearning tools in such contexts as online meetings on social platforms and communication methods with 70.8% positive feedback. However, medical colleges had negative feedback about incorporating eLearning assessment tools for learning/teaching in the near future; respondents preferred traditional exams and quizzes. The participants were also asked if traditional learning could be blended with the online learning techniques introduced during the pandemic; 75% of the respondents strongly approved this scenario, and, when asked about the preferred tool, they mainly chose social platforms, virtual class meetings, and interactive chat groups between faculty members and students. Some students suggested that online learning could be used in emergency cases such as pandemics, natural disasters, and conditions that prohibited students from attending classes, but few students indicated that quizzes, exams, and projects should all be online-based and formative.

Results of the third section of the survey.

In contrast, the engineering college, fine arts, and medicine rejected the idea of blending and preferred to keep all their classes traditional since they are difficult to replace at home. This is expected as students are often enrolled in a scientific laboratory and studio classes that are better equipped and prepared for students’ activities. Suppose this university plans to implement such a model. In that case, all colleges must be considered to ensure that all students have technology-mediated contact with both instructors and other students, improving the learning community (Lea and Nicoll, 2002).

7. Blended learning as a future solution

However, with 75% of the respondents agreeing that combining online and face-to-face techniques would be beneficial, a hybrid model could be the solution to the next phase. Russell et al. (2018) define hybrid learning as a model for delivering instruction that combines face-to-face classroom teaching with eLearning that would be better described as a new term, not a new idea. According to Welker & Berardino, [45] , Blended Learning is any combined use of online learning tools that supplements but are not a substitute for face-to-face learning. Their study found that implementing a hybrid model produced more work, according to faculty members, and lacked some traditional classroom dynamics. Students reported flexibility, accessibility, and objectivity as advantages but complained of uncertainty, reduced social communication, and additional work as drawbacks. Another study by Hannay and Newvine [13] examines why students chose online education and their perception of the quality and struggle of their courses compared to courses taught in the traditional ways. They suggest that eLearning alone is not enough. The study assimilated some of the finest features of distance learning into traditional courses to build a “hybrid” educational environment. The findings indicated that students favor online education, largely because it allows them to manage their commitments more conveniently. The case study university, for its part, has already started to incorporate online learning, having accepted a plan for a blended model that starts in the Fall Semester of 2020/2021. It aims to improve interactivity, connections between students and instructors, and active learning that will allow better engagement with the content of courses and considerable feedback on assessments. The governments must ensure the accessibility of consistent communication tools, high-quality digital academic involvement, and endorse technology-enabled learning for students to bridge the differences initiated in the education system due to the change process before and after the COVID-19 pandemic [21] . The university also plans to provide all its theoretical courses online and practical courses blended according to the lab/studio set up and the course requirements, thus providing a more flexible and resilient approach. However, the use of technology to aid on-campus courses helps the move from traditional educational structure to a blend-based model to be efficient (Lau, Yang and Dasgupta (2020); Open University (2020); Ross (2020; Sanger (2020), this approach still needs to be tested, and further research is required to analyze the blended-learning model.

8. Conclusion

In this research, the impact of online learning on teaching and learning during the COVID-19 pandemic was analyzed. Different parameters were extracted from a comprehensive literature review. These parameters were classified according to their relevance and used to create a survey of the perceptions of students and faculty members of different colleges, with the engineering college set as the base case, about their performance during the pandemic, the effectiveness of the eLearning techniques, and the future implications of this sudden introduction of complete online learning. The study findings reveal that the implementation of e-learning had both positive and negative impacts on the users. The primary benefit that has been determined is flexibility in place and time, with 77.2% of users providing positive feedback. On the contrary, the survey results showed that the sudden eLearning adoption had discouraging repercussions on user’s mental health and socialization, with 55.6% of the users agreeing that they had been affected negatively. Also, 71.6% of the users reported a decline in their academic performance and grades. As for the comparison between theoretical colleges and practical colleges, theoretical colleges appear to have a more positive perception of the extent of productivity, motivation, knowledge acquisition, and interactivity between users. All in all, the users advocated the idea of adopting online learning techniques in the future in conjunction with traditional classroom learning; 75% of the users preferred a blended model of face-to-face and e-learning techniques combined, rather than solely depending on either online learning or traditional learning.

This study was limited by the lack of previous writers analyzing the impact of the COVID-19 pandemic and by the location of the study. As far as future work can be suggested, the scope of the research could be extended by designing an educational model that combines the best of traditional and online learning and could serve as an educational scheme that could be generally implemented in cases of emergency or events such as a pandemic or a natural disaster of some kind.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Emad S. Mushtaha received his Master’s and Ph.D. degrees in 2003 and 2006, respectively, in Architectural Management and Environmental Design, from Hokkaido University, Japan. Dr. Mushtaha received a prestigious Postdoctoral-JSPS Fellowship in 2006 in Japan, then joined Ajman University in the UAE from 2007 to 2013 as an Assistant Professor of Architecture. He is currently working as an Associate Professor at the University of Sharjah (UoS), teaching architectural design studios and environmental courses. Furthermore, Dr. Mushtaha is an active member of the Sustainable Engineering Asset Management (SEAM) Research Group at UoS. He has actively participated in many conferences, workshops, symposiums and published more than 50 articles in different refereed journals and conferences. His current research interests are in the fields of Place Management and Sustainable Design, including lighting. On the community level, he is the leader of the Architecture and Built Environment Sustainability Circle (ABESC) at the Sustainability Office, UoS. Dr. Emad S. Mushtaha is the corresponding author and can be contacted at: [email protected].

Peer review under responsibility of Ain Shams University.

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Top 6 Questions People Ask About Online Learning

Since the invention of the internet, we have witnessed a huge change in the accessibility and flexibility of higher education. Not only can students earn their degrees at a distance and on their own schedule but they can also complete certifications and trade programs with more ease than ever before.

If you’re considering online classes as a means to achieving your goals, you likely have questions. Here are some of the most common ones, with answers!

What Is Online Learning?

So, just what is online learning? This term refers to education that takes place in a completely virtual environment using an internet connection and a computer or device to connect to the school. In the online "classroom," you can do all the same things that in-person students do, such as:

• Listening to lectures
• Answering questions from a professor
• Completing readings
• Turning in assignments
• Taking quizzes and tests
• Meeting as a group

Some schools, programs, or courses combine online learning with in-person learning experiences. This model is known as "hybrid education," wherein students participate online most of the time. However, when learning objectives call for hands-on experience (say, practicing skills for a health profession or laboratory experiments), they can head to campus.

That said, many programs allow their students to complete the entire curriculum virtually. Degrees such as a Bachelor of Science in Software Engineering, for example, may not call for in-person learning at all. You can always contact admissions or the specific department if you want to learn more about delivery format.

Why Online Learning Is Good for Students

Despite the widespread accessibility of remote education, some students remain skeptical about online classes. Are you really learning if there’s not a professor present at the front of a lecture hall? Can you really learn the skills you need without the in-person interaction between students and faculty?

Ease and Accessibility

While some people feel online education lacks the intimacy and immediacy of a "real" classroom, it offers an educational channel to students who might otherwise not have the time or resources to attend. Online access has made it possible for students to enroll and participate in online classes with greater ease, from nearly anywhere, in a way that fits their schedules.

Affordability

Online courses are usually more affordable as well. According to the Education Data Initiative , an online degree is $36,595 cheaper than an in-person degree when the cost of tuition and attendance are compared. The average cost of attending a private university is $129,800 for an in-person degree and only $60,593 for an online degree.

It’s also estimated that students who commute to college for in-person classes pay $1,360 per year in transportation costs that an online student wouldn’t have to pay. Add in factors such as cheaper meals at home and more time to work, and it’s not hard to see why many students opt for online learning.

Top Questions About Online Learning

Despite the benefits, you likely still have some questions about online learning. Let’s take a look at six of the most common.

1. Are You Able to Earn Your Degree Completely Online? Yes, many (but not all) schools do offer this as an option. We’re not just talking about certificates or minors, either.

For instance, you can earn a Master of Science in Electrical and Computer Engineering from U of M Online. If you complete the entire program virtually, you will pay in-state tuition costs from anywhere in the United States – a major bonus. A good school should offer you a searchable course catalog to compare options and view which have a required on-campus component.

2. How Long Does It Take to Earn a Degree Online? Most online programs mirror their in-person counterparts in terms of how long it takes to earn the degree. From certificates and minors to bachelor’s or master’s degrees, you’re looking at roughly the same timeline for equivalent programs. Some programs offer students the flexibility for part time options if that is needed to accommodate work and family responsibilities.

Some schools or programs may limit how quickly you can move through the material. However, given the freedom and flexibility of online learning, it’s possible you can complete more coursework in less time than you could on campus. Talk to your admissions officer or program coordinator about specifics.

When first researching your options, you can again turn to the searchable course catalog. On each degree page, you should find the recommended timeline clearly listed.

3. Is an Online Degree Viewed Differently Than a Traditional Degree? Among the most common and pressing questions for online learning is whether future employers view online degrees with skepticism. The answer is an emphatic "no." Most online programs appear on your transcript the same as on-campus programs would.

You may also wonder if an online program will impact your plans for a higher degree later. As long as your degree is from an accredited institution, it won’t harm your chances of acceptance.

4. What Are Some Benefits of Online Learning? When you choose to learn online, you can:

• Study more, due to the lack of commuting to, from, and around campus
• Potentially take more classes, again because of the time savings
• Get more immediate feedback from professors on assignments
• Leverage the online resources that come with your course portal
• Spend less money on your degree overall
• Continue working or caring for family while going to school

5. Do Instructors Offer Help and Support to Students? Instructors are required to give the same amount of time and energy to their online classes as they do to in-person groups. In fact, many professors are enthusiastic about virtual learning because it means they have more flexibility and don’t have to commute either.

6. Can Students Have Success and Excel in Online Learning? Lastly, can you learn new skills, attain knowledge, and become successful in online learning? Unequivocally, the answer is yes! Online degree programs still afford you tutoring and career resources as well as full access to academic resources such as the library .

Plus, you will have the ability to transfer credits either to or from the degree program, just as you would with an on-campus one. In other words, you will find yourself and your goals in no way hampered by taking the online approach.

Online Learning

In summary, online learning offers you a ton of freedom and savings. It allows you to complete your work anywhere, from the office to the living room to on the road. And you can rest assured that you’ll get the same level of professorial support as you would from an on-campus program, as well as a degree that’s worth just as much.

Learn More, Today

Ready to learn more? Reach out to U of M Online to ask questions or get information about specific programs today!

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• Open access
• Published: 25 June 2024

Understanding barriers and facilitators to palliative and end-of-life care research: a mixed method study of generalist and specialist health, social care, and research professionals

• Catherine Walshe 1   na1 ,
• Lesley Dunleavy 1   na1 ,
• Nancy Preston 1 ,
• Sheila Payne 1 ,
• John Ellershaw 2 ,
• Vanessa Taylor 3 ,
• Stephen Mason 2 ,
• Amara Callistus Nwosu 4 ,
• Amy Gadoud 4 ,
• Ruth Board 5 ,
• Brooke Swash 6 ,
• Seamus Coyle 7 ,
• Andrew Dickman 8 ,
• Andrea Partridge 5 ,
• Jaime Halvorsen 9 &
• Nick Hulbert-Williams 10  

BMC Palliative Care volume  23 , Article number:  159 ( 2024 ) Cite this article

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Palliative care provision should be driven by high quality research evidence. However, there are barriers to conducting research. Most research attention focuses on potential patient barriers; staff and organisational issues that affect research involvement are underexplored. The aim of this research is to understand professional and organisational facilitators and barriers to conducting palliative care research.

A mixed methods study, using an open cross-sectional online survey, followed by working groups using nominal group techniques. Participants were professionals interested in palliative care research, working as generalist/specialist palliative care providers, or palliative care research staff across areas of North West England. Recruitment was via local health organisations, personal networks, and social media in 2022. Data were examined using descriptive statistics and content analysis.

Participants (survey n  = 293, working groups n  = 20) were mainly from clinical settings (71%) with 45% nurses and 45% working more than 10 years in palliative care. 75% were not active in research but 73% indicated a desire to increase research involvement. Key barriers included lack of organisational research culture and capacity (including prioritisation and available time); research knowledge (including skills/expertise and funding opportunities); research infrastructure (including collaborative opportunities across multiple organisations and governance challenges); and patient and public perceptions of research (including vulnerabilities and burdens). Key facilitators included dedicated research staff, and active research groups, collaborations, and networking opportunities.

Conclusions

Professionals working in palliative care are keen to be research active, but lack time, skills, and support to build research capabilities and collaborations. A shift in organisational culture is needed to enhance palliative care research capacity and collaborative opportunities across clinical and research settings.

Peer Review reports

Palliative care provision should be informed by high quality research, so that clinical practice is underpinned by a robust evidence base. Improving the evidence base in palliative care is a ‘moral imperative’, with arguments highlighting that it is ethically important to offer effective treatments supported by an evidence base, and equally that futile treatments are avoided [ 1 ]. A principal focus of much of the research conducted to understand why developing the evidence base is difficult has focused on the specific challenges of recruiting patient and carer participants to palliative care research studies. Gatekeeping can be an issue, with staff concerned about overburdening vulnerable patients and carers, and feeling ill prepared to discuss research with potential participants [ 2 , 3 , 4 ]. This is despite evidence suggesting patients and families are willing to engage in research at the end of life [ 5 , 6 , 7 ]. Despite this readiness, there can be many reasons why patients and carers may not feel able to engage in research such as illness severity, symptom burden, misconceptions about palliative care, lack of cure and perceived therapeutic benefit, and study burden [ 8 , 9 , 10 ]. This can mean that many studies experience recruitment difficulties [ 11 , 12 ]. Facilitators that may address some of these complex structural, cultural and personal barriers include dedicated research staff on site [ 3 , 13 ], training on how to recruit to palliative care studies [ 14 , 15 ], and improving communication with patients and their families to promote research participation, and within staff teams to address gatekeeping.

Researchers outside palliative care have chosen to explore the professional and organisational facilitators and barriers to conducting research [ 16 , 17 ]. Less is known about the personal, professional, organisational, and structural barriers and facilitators to conducting palliative care research. Palliative care requires a multi-professional approach, and patients are cared for in a variety of settings, including hospitals, hospices, nursing homes and primary care. Palliative care research is historically under-funded in comparison to research that focuses on the prevention or cure of cancer and other life-limiting illnesses [ 18 , 19 ]. There may also be challenges with access to staff with the relevant research expertise, and complicated or undeveloped governance arrangements particularly in settings outside statutory provision [ 20 , 21 , 22 , 23 ]. Research may not be a strategic priority, especially for standalone voluntary organisations who largely rely on charitable funding to fund patient care [ 23 ]. Palliative care research can be time consuming and staff may see it is an ‘add on’ to their role and not part of the routine care they provide to patients [ 24 ]. Staff may feel that they lack the necessary knowledge, skills and expertise to be involved in palliative care research [ 4 , 25 ] and may have limited opportunity to participate or learn more, especially when balancing clinical pressures that have increased during the COVID 19 pandemic [ 26 ]. An organisational research culture improves outcomes for all patients, and not just those involved in the research [ 27 ]. The aim of this study therefore is to further understand professional and organisational facilitators and barriers to conducting all types of palliative care research.

Research question

What are the barriers and facilitators to conducting palliative and end-of-life care research across North West Coast England ?

A mixed method study following a convergent design [ 28 ] , incorporating a cross-sectional online survey and working groups using a nominal group technique [ 29 ]. The survey is reported according to the CHERRIES guidelines for e-surveys [ 30 ].

Both the survey and working groups were conducted across the UK NIHR North West Coast region of England (incorporating South Cumbria, Lancashire, Cheshire, and Merseyside). Currently, palliative care research activity within this area is low. In the UK, palliative care is provided by generalists, the patient’s usual care team, in the hospital, community or care home setting. Specialist inpatient, hospital, home and home nursing palliative services are provided by professionals specifically trained in palliative care, and they largely rely on charitable funding [ 31 , 32 ].

All those who had interest in the provision of, or research into, generalist or specialist palliative care across the region including across acute and community NHS Trusts, GP practices, voluntary hospices, other community and private providers of care, clinical research networks, and academic settings including Universities were invited to participate. The survey was accessed via an online link that included a screening question incorporating the inclusion criteria (see Table  1 ).

Survey: The survey used a convenience sampling approach and was designed to collect largely descriptive data and yield rich information across a range of respondents. Without a viable sampling frame of potential participants, no anticipated sample size could be reliably estimated. Working groups : Those who indicated an interest in taking part via their survey response, or who responded to additional calls for participation, were invited to participate, and then purposively selected to maximise variability across professional background, expertise, and geography.

Recruitment

Survey: Potential participants were recruited via several routes that included dissemination via collaborators in local NHS Trusts and Hospices and the North West Coast Clinical Research network to ensure primary care organisations were reached. Information about the survey was openly and widely disseminated through a project website, personal networks, and social media (Twitter, Facebook, and LinkedIn). No incentives for survey completion were offered. Dissemination included a link to the online survey, with screening questions at the start of the survey confirming eligibility, with clicking through to progress to the survey indicating consent. Potential participants were reassured that taking part was voluntary and that survey results would be aggregated and anonymised. It was explained that their data would be inputted into a secure online survey platform, and these data would be then stored in a secure institutional filestore at Lancaster University. (see additional file 1).

Working groups

Individuals who expressed an interest in taking part in further research after completing the survey were sent working group invitation packs. Additionally, collaborators in local NHS Trusts, Hospices and the North West Coast Clinical Research network circulated packs to eligible participants. Social media (Twitter, Facebook, and Instagram) was also used to advertise the working groups. Participants could take part in the working groups even if they had not completed the survey. Participants contacted the research team if they were interested in taking part and electronic consent was obtained prior to the working group.

Data collection

Survey: The open online survey was built using Qualtrics XM [ 33 ], and the full survey is included in additional file 1. Both closed and free-text questions were used, together with skip options dependent on given answers; 19 possible questions (some with multiple components) were asked across 5 blocks. Participants could navigate through the survey using forward and back buttons. The survey identified current and desired levels of palliative care research involvement, current research barriers, suggestions for sustainable solutions and research training needs. The survey was developed from the IPOS survey (a survey of the research barriers and training needs within the International Psycho-Oncology Society) [ 34 ] and literature on barriers and facilitators to palliative care research [ 3 ]. Survey development followed an iterative approach, with members and colleagues of the project steering committee reviewing survey questions to ensure the survey was appropriate. Participants could only complete the survey once. There was not a completeness check for respondents. The survey was open from 02/03/2022 to 08/06/2022.

Four online (via Microsoft Teams) working groups took place. The groups lasted two hours and were facilitated by LD and another member of the research team (from CW, AG, BS, RB). Nominal group technique was used as it is a method that elicits the views and opinions of a group of experts through the ranking of priorities related to a particular topic of interest. It combines both qualitative and quantitative data collection and involves a number of stages that include; introductions, silent generation of ideas, listing of ideas, discussion of ideas, ranking of top ten ideas, voting on top ten ideas, discussion of voting and conclusions [ 29 ]. Mentimeter [ 35 ] was used to facilitate the voting process and the working groups were recorded.

Data analysis

Survey: Data were downloaded from Qualtrics™ as.csv and.sav files for Excel and SPSS, hosted on Lancaster University secure OneDrive, and checked for potential duplicate entries (using IP, email address or organisation name to ensure only one entry per respondent), and to remove incomplete entries. Entries were judged as complete when participants had provided sufficient descriptive personal information alongside survey responses, even if answers to all available questions had not been given. Pseudonymised data were used for analysis. Descriptive analysis included the use of frequency counts, percentages, and rankings, with some collapsing of categories.

For the analysis of free-text comments, data were extracted into Microsoft Excel. Comments tended to be brief, expanding on answers to closed questions [ 36 , 37 ]. After initial familiarisation, a coding framework was inductively developed by LD and CW and applied to the free text data using a conventional content analysis technique [ 38 ]. Coding and theme development were driven by the content of the free-text comments.

Working groups, using nominal group technique

Each working group was initially analysed separately by LD using the group’s Mentimeter rankings as an initial a priori framework [ 39 ]. The working group recordings and transcripts were read and listened to, and the key issues were summarised within the a priori frameworks. The findings were then compared across the working groups by LD, SM, BS, and AP with input from the study’s Patient and Public Involvement group and finally the study steering committee, to identify key themes.

Four overarching groupings were inductively generated after completion of the working groups. Survey free text and working group findings were compared as part of the four theme development. Mentimeter rankings were allocated to the four groups along with the survey statements where there was strongest agreement about the barriers to research across all survey respondents (see Table 5. ).

Approval was granted by the East of England—Cambridge South Research Ethics Committee (Ref: 22/EE/0049) on the 24/02/2022. Organisational approval was obtained via the Health Research Authority and each participating site.

Survey response

The online survey received 495 visitors, of whom 8 declared they did not meet the inclusion criteria, 36 provided no data, and 158 did not proceed beyond the screening questions. Valid responses were received from 293 participants (59% of visitors), with 171 of the 293 (58%) recording 100% survey progress, and a mean progress of 82% (range 100% to 25%).

Characteristics of survey respondents

Full descriptive data from these respondents are found in Table 2 . The highest proportion of respondents worked in hospice settings, were nurses, and had worked in palliative care for over 10 years. Unexpectedly, there was a high number of paramedics who completed the survey ( n  = 17).

Characteristics of working group participants

Twenty palliative care providers/research staff participated in the working groups (see Table 4  for details).

Barriers and facilitators to participating in palliative care research (quantitative data)

Survey respondents were asked to indicate the strength of agreement with statements about facilitators or barriers to engagement and involvement with palliative care research. Working group participants inductively generated statements about barriers which were then ranked. In Table 5.  below we present the survey statements where there was strongest agreement across all survey respondents, together with the ranking of inductively generated statements from each of the working groups. Full survey data are found in additional file 2.

The top research barriers were conceptualised across four main areas: organisational culture and capacity (including prioritisation and time given to research); research knowledge (including research skills, how to obtain funding); research infrastructure and collaborations (including collaborative opportunities and governance arrangements), and patient and public perceptions of palliative care research (including vulnerabilities and burdens). Data on facilitators and training needs were collected in the online survey and are presented in Tables 6 and 7 .

Barriers to participating in palliative care research (qualitative data)

Additional data on the four areas of organisational culture and capacity, research knowledge, research infrastructure and collaborations, and patient and public perceptions of research were generated in both the free text comments from the survey and working group analysis. A narrative exploring each of these is presented in turn, illustrated with verbatim data extracts from the working groups and survey.

Organisational culture and capacity

This was the top barrier identified in the survey and most working groups. The focus was about whether research is prioritised within the organisation, including if people are enabled to conduct research in terms of protected time. Across the working groups and survey, participants explained how staff have no time to be involved in research because of clinical pressures and commitments. Staffing shortages, patient complexity, and the impact of COVID 19 have made the situation even more challenging for clinicians:

‘It's really difficult because everyone is so stretched that everybody's so busy sort of, you know, the AHP's [allied health professionals], the doctors, the nurses, everyone's very busy, sort of fighting fires that nobody's got time to move away from that at the moment’ (Hospice Doctor, working group 2)

‘The main barrier from my experience is not having protected time to spend in research activities. My case load is vast and give me no time to participate in research. This is disheartening to me as we need to constantly develop and not stagnate. Also, with palliative care we get one opportunity to make that difference so we need to be equipped with the best we can do.’ (Survey study ID 163, Hospital Doctor)

Organisational culture and external requirements also mitigate against engagement in palliative care research, where priority is given to meeting key performance indicators, which rarely include research engagement:

‘The clinical demands and their key performance indicators required by our service specifications and our trust, demand that you spend the majority of your time 90% if not more, undertaking clinical aspects of the role and that there isn't necessarily buy in [to research] I don't feel from the senior management within the organisation to support us’ (Palliative care nurse specialist, working group 1)

Research not being part of an organisations culture and ethos and therefore not seen as a strategic priority was an important barrier.

‘Even if someone said here's some funding, what do you want? We reel off a million answers, but research would probably be at the bottom just because there's other things that we need or want that we feel is probably more important than research. Whether that's right or wrong, I think it's just not. Not a priority. It's no one’s first thought.’ (Hospice nurse, Working group 2)

Participants highlighted the need for a ‘research champion’ within an organisation who would be responsible for leading, prioritising and raising the profile of research therefore making research less daunting for staff.

‘I think you're somebody who's motivated to drive a research agenda forward, I think makes a big difference in the organisation that you're in, whether that's hospital based or community Hospice and based because I think if you haven't got anybody who's keen and enthusiastic, you're not going to go anywhere. So you've got to have someone who's willing to take that on.’ (Hospital Doctor, Working group 4)

Research knowledge

Health and social care staff can have a limited understanding of research processes, and therefore may not have the necessary skills to conduct research. Whilst some basic knowledge was covered at pre- and post-registration undergraduate or postgraduate level, continuing to develop skills and knowledge could be challenging:

‘We're encouraging our staff to undertake further education or sort of masters level qualifications, and at that level it does require for the qualification a piece of research and a number of research questions to be undertaken, but it's how do we move beyond that?’ (Hospice manager/admin Working group 1)

You do the research project within the course to get through the course and then you know you like, breathe sigh of relief and then you don't go near research again.’ (Palliative care nurse specialist, Working group 1)

Research can feel distant and overwhelming, academic and jargon filled, without relevant pathways to support professional development:

‘I think from a perspective of peoples understanding and knowledge of research and where to get support and there's a lot of people shy away from it because they don't know where to start. They don't know where to go to. They don't know how to find the literature and they just feel like they're in a minefield of information they don't know which avenue to take.’ (Hospice nurse, Working group 4)

The need for mentorship, support, and guidance from more experienced research staff and how to access this support was clearly identified. Engaging junior staff was seen as important and training sessions/e-learning needed to be accessible, including tailored resources for palliative care, and level of involvement in research.

‘If people haven't done a lot of research and they want to be involved and it's sort of supporting that group of people if they haven't got links to people already or groups within their organization or network that they can link into, and they're really interested in it, it's getting those people involved and how to direct them?’ (Hospice nurse, Working group 4)

‘Need the support of an experienced researcher and also someone to help plan and develop the research, mentor and guide throughout research project and assist with analysis of results-/stats and writing up the project.’ (Survey study ID 39 specialist palliative care clinical manager)

Participants explained how there tended to be a lack of research expertise (e.g. knowledge of research processes) within hospices and how it was important to have someone with the right skill set in the setting/small organisation.

‘Having somebody with the right skill set to take something through ethics committee and everything I suppose, and you need to have that one person in every Hospice or in every setting who can do all that. It's a skill all of its own.’ (Manager/admin, Working group 2)

Research infrastructure and collaborations

Palliative care research was felt to have a weak infrastructure, with few studies in the National Institute for Health Research (NIHR) portfolio, limiting opportunities to be involved in research and access to research nurse support. Hospices had few financial resources to support research activity, and seemed reluctant to divert funds from direct patient care:

‘So, there's huge financial implications in terms of them [charitably funded hospices] providing sort of and delivering research … it was a massive competing pressure on money because you don't want to be impacting on the organisations finances and within the charity sector to the detriment of immediate patient care.’ (Hospice Doctor, working group 1 )

‘Releasing people to take part in research is just impossible for a Hospice with our current funding arrangements. Research feels like a "nice to have" aspect of Hospice work. Even though I know it would be valuable to our sector long-term to be research active, the climate we find ourselves in means research is way down the list of priorities for a charity receiving 30% (and diminishing) funding [from the NHS] to run a 24/7 service.’ (Survey Study ID 85, Hospice CEO)

The lack of or limited research infrastructure outside the hospital setting, particularly within standalone hospices, was raised as a barrier. The necessary structures to support research activity, such as governance arrangements, training, and adequate staffing levels, could often be lacking.

‘I think when you're working with within small groups you could be quite isolated with only having one research nurse who then is on their own, and I think the link I think that's probably an issue in terms of I guess the funding for that person. It can be an issue but also attracting somebody to a post which is going to feel quite isolating.’ (Hospital Doctor, Working group 4)

‘But the thought of actually undertaking some research ourselves. We're a million miles away from that in our hospice you know. We are trying to be involved in other bigger trials, but where to actually put through an ethical approval ourselves. We're nowhere near that here.’ (Hospice Doctor, Working group 2)

The importance of engaging nursing and allied health professionals in research and giving them the opportunity to be involved was raised. The four pillars of professional practice of the clinical nurse specialist and advanced practitioner roles includes research alongside clinical, education and leadership components [ 40 ]. However, research is not always recognised or developed. It was noted that organisations support training in Independent and Supplementary Prescribing, diagnostics, and advanced communication skills, so it was questioned why not research. Some short-term research positions may not provide opportunities for all staff, as posts may be linked to certain roles (e.g. medical, nursing) or require professional registrations, thus limiting opportunities for staff without these qualifications (e.g. healthcare assistants). The importance of recognising the role and expertise of non-clinical staff in research and its potential impact on care and services needs to be promoted.

Currently, there was not a strong sense that people or organisations were working collaboratively locally or regionally to facilitate research:

‘We don't work collaboratively, and we have a really big list of research projects that we'd like to do. We'd like to get started on. We don't have the capacity to do it, but actually other hospices or other professionals in palliative care might be working on it. But we just don't know because we don't talk to each other. Perhaps we just need to talk more?’ (Manager/admin Working group 2)

‘I think we're all busy, aren't we? So, the opportunity to meet, collaborate, share ideas doesn't to me seem like it's there. I could be wrong, but I think lack of existing collaboration, just perhaps due to how busy we all are individually, and rather than what I didn't mean, was competitiveness between hospices, yeah.’ (Hospice nurse, Working group 3)

‘From a researcher perspective, the barriers I face are around making the necessary connections with relevant practitioners interested and available to work on research projects. This is partly to do with few opportunities to meet people in informal environments where research priorities or interests can be discussed….(Survey study ID 43 researcher)

The need for some form of alliance or collaborative infrastructure was highlighted to pool research ideas, share information, collaborate on policies and governance issues. This was felt to need buy in from multiple organisations, potentially with a funded post to lead on research across voluntary hospices:

‘it's almost like we need some sort of alliance, isn't it? And that may well be where all this is headed and in terms of, you know, somewhere in the region somebody's putting a bid in for this research and who wants to jump on board to recruit in their area to get some opportunity for the expertise.’ (Palliative care nurse specialist, Working group 1)

‘And so maybe having some kind of umbrella group or network that… then everything kind of filters through it and information comes back out the other way so that that information is shared and you kind of know where to go. Maybe if you've got an idea to check that no one else is already doing it and to be in touch with the right people at the right time, I don't know if something around the kind of coordination of the whole thing.’ (Hospice manager/admin, Working group 2)

There were concerns raised that the palliative care research community involved a select group of individuals and could be elitist. It could be difficult for those sitting outside the elite to know how to be involved and included in any research activity:

‘I did reflect on initially when I got interested in research it was sort of seen as this area of expertise in which a select group were involved, and it was sort of how do we get into that Network.’ (Hospice nurse, Working group 4)

Patient and public perceptions of palliative care research

Concerns were also raised that patient and public perceptions of palliative care research may be an issue either because there were assumptions that research was not happening, or only in large/cancer settings, that people did not want to take part, or that the end of life is an inappropriate time to request participation.

‘Sometimes staff feel oversensitive. Almost oversensitive to not wanting to upset patients and relatives to recruit them in, or to ask the relevant questions that we need them to ask.’ (Hospice educator, Working group 2)

However, counter arguments were also recognised:

‘Anecdotally, we've had people tell us when they've taken part in studies that we've done, that they've enjoyed taking part that it's been beneficial for them, not because the research will impact them, but because of the process of...I guess the therapeutic aspect that's a side line to them taking part that they've enjoyed taking part and sharing. Their views and being able to put something back and to help other people.’ (Researcher, Working group 3)

The aim of this research is to understand professional and organisational facilitators and barriers to conducting palliative care research. Palliative care research was recognised as important and valuable, with three-quarters of those involved in this study wanting to increase their involvement in research, despite most not being currently research active. Several key barriers to palliative care research were identified including lack of organisational research culture and capacity (including prioritisation and available time); research knowledge (including skills/expertise and funding opportunities); research infrastructure and collaboration (including lack of collaborative opportunities across multiple organisations and governance challenges); and patient and public perceptions of research (including vulnerabilities and burdens). Key facilitators included dedicated research staff, and active research groups, collaborations, and networking opportunities.

What this research adds

A key finding is the apparent lack of progress in facilitating palliative care research over time, and the challenge for the sector is why change has been so slow. Previous palliative care research identifies a suite of remarkably similar barriers [ 23 , 41 , 42 , 43 , 44 ], albeit not necessarily unique to this specialty [ 45 , 46 ]. There needs to be a concerted and sustained focus on collaboration and sharing best practice, developing a research culture and facilitating research within and between palliative care providers, enhancing staff capacity and expertise, and providing guidance on research processes and procedures [ 23 , 41 , 43 , 44 ]. Our research further highlights the importance of organisational barriers, pointing to the need to prioritise organisational solutions.

Organisations have a critical role in building research culture and capacity [ 46 , 47 , 48 ]. It is imperative that organisations recognise and value research and incorporate research into the core business of the organisation. This means that research should be visible throughout, from mission statements to policies, business plans, and job descriptions. They should protect research time and resources, recognise talent, and reward positive research related behaviours [ 48 ]. This may be a particular challenge for those palliative care organisations that are charitably funded due to the uncertainty and volatility of their funding [ 49 , 50 ], and business models that may not account for research activity [ 51 ]. The focus is also set nationally, with the recently launched Hospice UK 2024–29 strategy having no overt mention of research [ 52 ].

A key finding is that for many the organisational lack of support for research translates into research not being seen as a core part of people’s jobs. Again, this is not unique to palliative care, with capacity to be engaged in research limited in time or job plans [ 53 ]. As an example an audit of clinical nurse specialist job descriptions found that 80% had an expectation of research engagement [ 40 ], however, in detailed studies of how such roles are enacted, research is typically absent [ 54 , 55 ]. Where research is mentioned, it was in the context of it being the least important aspect of the role, or that others (such as medical consultants) should be leading research [ 56 ]. However, whilst there is little contemporary data, previously the median time palliative care consultant doctors spent on research was zero hours [ 57 ]. A recent survey of UK palliative medicine consultants found that while 78% ( n  = 140/180) were interested in conducting research, 83% had no allocated time within their job plan [ 58 ]. Given the serious and significant workforce pressures and challenges currently facing many healthcare workers it is unlikely this position will change without both investment in, and prioritisation of, research time and roles. It may be that research time or engagement needs to explicitly form part of key performance indicators or other metrics to enable such prioritisation to occur.

Research should be important to palliative care provider organisations. It is known that a strong research culture and organisational research performance lowers mortality rates, increases patient and staff satisfaction, reduces staff turnover, and improves organisational efficiency [ 59 ]. Our research encompassed a variety of different organisations and settings, demonstrating that these barriers were remarkably similar wherever a person worked. Solutions may differ though depending on the size, funding, and specialism of the organisation. An independent voluntary funded hospice may have different solutions to a palliative care team working as part of a larger general hospital or community care provider.

The opportunity to collaborate between individuals and across organisations may be important, as in other specialities such as General Practice [ 60 ]. Evidence indicates that the creation of research cooperatives, collaborations and partnerships can be fruitful. There are palliative care examples from the UK [ 61 ], US [ 62 , 63 ], Australia [ 64 , 65 , 66 ], and Africa [ 67 ]. Some of these are large collaboratives, across multiple sites, facilitating multiple studies [ 68 ]. It is possible that such collaboratives mitigate the effect of the employing organisation for members, facilitating research in a way that sits above, and possibly either bypasses, negates, or gives the skills to overcome institutional and local organisational barriers. Joint approaches between universities and public and charitable providers of palliative care may help overcome structural issues such as indemnity, sponsorship and gaining research ethics committee approvals. However, funding to sustain some of these collaborations can be fragile or time limited. For example, in the UK, very welcome but time-limited funding to build palliative care research partnerships has been awarded, but it is too early to see the impact of this on the research landscape [ 69 ]. The benefits of such collaborations may also be on the wider research culture of the organisations that participate in such research. The initial impact of participating in a trial may be staff stress and workload, but this has found to be replaced by enthusiasm for the changes and benefits achieved [ 70 ].

Those who completed our survey had wide variability in levels of research experience and involvement. It is important to recognise when considering developing an organisational research culture that not all members of staff need the same level of skill and expertise, and not all organisations will be at the same level of engagement. Previous recommendations for hospices suggested a typology of engagement, through which hospices could progress if they wished, from research aware, to research engaged, to research leading [ 23 , 43 ]. Equally, individuals can have different levels of preparation, with recognition that generating and leading new research likely needs the higher levels of research preparation such as research focused PhDs, and that organisations that aspire to these levels need to invest in educating staff to these levels and supporting their continued research development.

Strengths and limitations of the research

A strength of this research was the breadth of response from across different sectors and professional backgrounds. There was a particularly strong response from nurses, and a reasonable proportion of those providing general palliative care. However, it was harder to recruit respondents who do not provide specialist palliative care (perhaps because they do not identify themselves as palliative care providers despite the high numbers of those with palliative care needs that they provide care for). Care home respondents were particularly poorly represented. We aimed to invite patients, family members and the public to a working group. Whilst we involved Patient and Public Involvement (PPI) study team members in planning this work and attempted to recruit the public to our working groups, challenges both in institutional permissions and recruitment meant that this planned aspect of the study did not go ahead. This work also represents the views of people from across a particular UK geography. Whilst this includes a large, diverse, population it may be that this does not represent wider views, although this is unlikely given the congruence with past and related research. This study also includes participants who were involved or would wish to be involved in palliative care research so the views of those who are not interested are not reflected in the findings.

Engagement in palliative care research appears stagnant, with this study revealing a range of barriers that appear unchanged from a decade or more ago. The challenge for palliative care is not to identify further the barriers and facilitators to research, but to invest time and funding to address the known barriers and enable the facilitators of research. It is likely that such investments will reap dividends in terms of staff satisfaction, organisational performance, and importantly the quality of care provided to patients and families.

Availability of data and materials

Data are stored in Lancaster University’s PURE repository, consent to share data was not given by participants.

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This project is funded by the NIHR Palliative and End of Life Care Research Partnerships Funding Committee [NIHR135334]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

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Catherine Walshe and Lesley Dunleavy are joint senior authors.

Authors and Affiliations

International Observatory On End-of-Life Care, Division of Health Research, Faculty of Health and Medicine, Lancaster University, Lancaster, UK

Catherine Walshe, Lesley Dunleavy, Nancy Preston & Sheila Payne

Liverpool University, Liverpool, UK

John Ellershaw & Stephen Mason

University of Huddersfield, Huddersfield, UK

Vanessa Taylor

Lancaster Medical School, Lancaster University, Lancaster, UK

Amara Callistus Nwosu & Amy Gadoud

Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK

Ruth Board & Andrea Partridge

Chester University, Chester, UK

Brooke Swash

The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK

Seamus Coyle

Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK

Andrew Dickman

NIHR Clinical Research Network North West Coast, Liverpool, UK

Jaime Halvorsen

Edge Hill University, Ormskirk, UK

Nick Hulbert-Williams

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Conceptualisation and funding acquisition: LD, NHW, NP, SP, JE, VT, SM, ACN, AG, RB, BS, SC, AD, AP, JH, CW; Investigation and analysis: LD, NHW, CW, AG, RB, BS; Writing – original draft – CW, LD; Writing – review and editing - LD, NHW, NP, SP, JE, VT, SM, ACN, AG, RB, BS, SC, AD, AP, JH, CW.

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Walshe, C., Dunleavy, L., Preston, N. et al. Understanding barriers and facilitators to palliative and end-of-life care research: a mixed method study of generalist and specialist health, social care, and research professionals. BMC Palliat Care 23 , 159 (2024). https://doi.org/10.1186/s12904-024-01488-2

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