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Research Article

Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination–a multi-country study

Roles Conceptualization, Methodology, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

Roles Conceptualization, Data curation, Methodology, Project administration, Writing – review & editing

Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Roles Conceptualization, Methodology, Writing – review & editing

Roles Data curation, Writing – review & editing

Affiliation Department of Mathematics, Faculty of Mathematics, University of Vienna, Vienna, Austria

Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Roles Conceptualization, Funding acquisition, Methodology

Affiliation Department of Psychology, Faculty of Education, Aleksandër Moisiu University, Durrës, Albania

Affiliation Department of Educational Sciences, Faculty of Philology and Education, Bedër University, Tirana, Albania

Affiliation Xiangya School of Nursing, Central South University, Changsha, China

Affiliations Xiangya School of Nursing, Central South University, Changsha, China, Department of Nursing Science, University of Turku, Turku, Finland

Affiliation Study of Nursing, University of Applied Sciences Bjelovar, Bjelovar, Croatia

Affiliation Baltic Film, Media and Arts School, Tallinn University, Tallinn, Estonia

Affiliation Faculty of Educational Sciences, University of Helsinki, Helsinki, Finland

Affiliation Department of Psychology, University of Bonn, Bonn, Germany

Affiliation Chair of Educational Psychology, Technische Universität Berlin, Berlin, Germany

Affiliation Department of Educational Studies, University of Potsdam, Potsdam, Germany

Affiliation Faculty of Education, University of Akureyri, Akureyri, Iceland

Affiliation Department of Global Education, Tsuru University, Tsuru, Japan

Affiliation Career Center, Osaka University, Osaka University, Suita, Japan

Affiliation Graduate School of Education, Osaka Kyoiku University, Kashiwara, Japan

Affiliation Department of Psychology, Faculty of Philosophy, University of Prishtina ’Hasan Prishtina’, Pristina, Kosovo

Affiliation Department of Social Work, Faculty of Philosophy, University of Pristina ’Hasan Prishtina’, Pristina, Kosovo

Affiliation Department of Psychology, Faculty of Social Sciences and Humanities, Klaipėda University, Klaipėda, Lithuania

Affiliation Geography Department, Junior College, University of Malta, Msida, Malta

Affiliation Institute of Family Studies, Faculty of Philosophy, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

Affiliation Institute of Psychology, Faculty of Social Science, University of Gdańsk, Gdańsk, Poland

Affiliation Faculty of Historical and Pedagogical Sciences, University of Wrocław, Wrocław, Poland

Affiliation Faculty of Educational Studies, Adam Mickiewicz University, Poznań, Poland

Affiliation CERNESIM Environmental Research Center, Alexandru Ioan Cuza University, Iași, România

Affiliation Social Sciences and Humanities Research Department, Institute for Interdisciplinary Research, Alexandru Ioan Cuza University of Iași, Iași, România

Affiliation Department of Informatics, Örebro University School of Business, Örebro University, Örebro, Sweden

Affiliation Faculty of Social Studies, Penn State University, State College, Pennsylvania, United States of America

•  [ ... ],

Affiliations Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria, Department for Teacher Education, Centre for Teacher Education, University of Vienna, Vienna, Austria

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• Elisabeth R. Pelikan, 
• Selma Korlat, 
• Julia Reiter, 
• Julia Holzer, 
• Martin Mayerhofer, 
• Barbara Schober, 
• Christiane Spiel, 
• Oriola Hamzallari, 
• Ana Uka, 

• Published: October 6, 2021
• https://doi.org/10.1371/journal.pone.0257346
• Peer Review
• Reader Comments

Due to the COVID-19 pandemic, higher educational institutions worldwide switched to emergency distance learning in early 2020. The less structured environment of distance learning forced students to regulate their learning and motivation more independently. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness affects intrinsic motivation, which in turn relates to more active or passive learning behavior. As the social context plays a major role for basic need satisfaction, distance learning may impair basic need satisfaction and thus intrinsic motivation and learning behavior. The aim of this study was to investigate the relationship between basic need satisfaction and procrastination and persistence in the context of emergency distance learning during the COVID-19 pandemic in a cross-sectional study. We also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these circumstances in different countries, we collected data in Europe, Asia and North America. A total of N = 15,462 participants from Albania, Austria, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, and the US answered questions regarding perceived competence, autonomy, social relatedness, intrinsic motivation, procrastination, persistence, and sociodemographic background. Our results support SDT’s claim of universality regarding the relation between basic psychological need fulfilment, intrinsic motivation, procrastination, and persistence. However, whereas perceived competence had the highest direct effect on procrastination and persistence, social relatedness was mainly influential via intrinsic motivation.

Citation: Pelikan ER, Korlat S, Reiter J, Holzer J, Mayerhofer M, Schober B, et al. (2021) Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination–a multi-country study. PLoS ONE 16(10): e0257346. https://doi.org/10.1371/journal.pone.0257346

Editor: Shah Md Atiqul Haq, Shahjalal University of Science and Technology, BANGLADESH

Received: March 30, 2021; Accepted: August 29, 2021; Published: October 6, 2021

Copyright: © 2021 Pelikan 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: Data is now publicly available: Pelikan ER, Korlat S, Reiter J, Lüftenegger M. Distance Learning in Higher Education During COVID-19: Basic Psychological Needs and Intrinsic Motivation 2021. doi: 10.17605/OSF.IO/8CZX3 .

Funding: This work was funded by the Vienna Science and Technology Fund (WWTF) [ https://www.wwtf.at/ ] and the MEGA Bildungsstiftung [ https://www.megabildung.at/ ] through project COV20-025, as well as the Academy of Finland [ https://www.aka.fi ] through project 308351, 336138, and 345117. BS is the grant recipient of COV20-025. KSA is the grant recipient of 308351, 336138, and 345117. Open access funding was provided by University of Vienna. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Introduction

In early 2020, countries across the world faced rising COVID-19 infection rates, and various physical and social distancing measures to contain the spread of the virus were adopted, including curfews and closures of businesses, schools, and universities. By the end of April 2020, roughly 1.3 billion learners were affected by the closure of educational institutions [ 1 ]. At universities, instruction was urgently switched to distance learning, bearing challenges for all actors involved, particularly for students [ 2 ]. Moreover, since distance teaching requires ample preparation time and situation-specific didactic adaptation to be successful, previously established concepts for and research findings on distance learning cannot be applied undifferentiated to the emergency distance learning situation at hand [ 3 ].

Generally, it has been shown that the less structured learning environment in distance learning requires students to regulate their learning and motivation more independently [ 4 ]. In distance learning in particular, high intrinsic motivation has proven to be decisive for learning success, whereas low intrinsic motivation may lead to maladaptive behavior like procrastination (delaying an intended course of action despite negative consequences) [ 5 , 6 ]. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness leads to higher intrinsic motivation [ 7 ], which in turn promotes adaptive patterns of learning behavior. On the other hand, dissatisfaction of these basic psychological needs can detrimentally affect intrinsic motivation. According to SDT, satisfaction of the basic psychological needs occurs in interaction with the social environment. The context in which learning takes place as well as the support of social interactions it encompasses play a major role for basic need satisfaction [ 7 , 8 ]. Distance learning, particularly when it occurs simultaneously with other physical and social distancing measures, may impair basic need satisfaction and, in consequence, intrinsic motivation and learning behavior.

The aim of this study was to investigate the relationship between basic need satisfaction and two important learning behaviors—procrastination (as a consequence of low or absent intrinsic motivation) and persistence (as the volitional implementation of motivation)—in the context of emergency distance learning during the COVID-19 pandemic. In line with SDT [ 7 ] and previous studies (e.g., [ 9 ]), we also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these specific circumstances, we collected data in 17 countries in Europe, Asia, and North America.

The fundamental role of basic psychological needs for intrinsic motivation and learning behavior

SDT [ 7 ] provides a broad framework for understanding human motivation, proposing that the three basic psychological needs for autonomy, competence, and social relatedness must be satisfied for optimal functioning and intrinsic motivation. The need for autonomy refers to an internal perceived locus of control and a sense of agency. In an academic context, students who learn autonomously feel that they have an active choice in shaping their learning process. The need for competence refers to the feeling of being effective in one’s actions. In addition, students who perceive themselves as competent feel that they can successfully meet challenges and accomplish the tasks they are given. Finally, the need for social relatedness refers to feeling connected to and accepted by others. SDT proposes that the satisfaction of each of these three basic needs uniquely contributes to intrinsic motivation, a claim that has been proved in numerous studies and in various learning contexts. For example, Martinek and colleagues [ 10 ] found that autonomy satisfaction was positively whereas autonomy frustration was negatively related to intrinsic motivation in a sample of university students during COVID-19. The same held true for competence satisfaction and dissatisfaction. A recent study compared secondary school students who perceived themselves as highly competent in dealing with their school-related tasks during pandemic-induced distance learning to those who perceived themselves as low in competence [ 11 ]. Students with high perceived competence not only reported higher intrinsic motivation but also implemented more self-regulated learning strategies (such as goal setting, planning, time management and metacognitive strategies) and procrastinated less than students who perceived themselves as low in competence. Of the three basic psychological needs, the findings on the influence of social relatedness on intrinsic motivation have been most ambiguous. While in some studies, social relatedness enhanced intrinsic motivation (e.g., [ 12 ]), others could not establish a clear connection (e.g., [ 13 ]).

Intrinsic motivation, in turn, is regarded as particularly important for learning behavior and success (e.g., [ 6 , 14 ]). For example, students with higher intrinsic motivation tend to engage more in learning activities [ 9 , 15 ], show higher persistence [ 16 ] and procrastinate less [ 6 , 17 , 18 ]. Notably, intrinsic motivation is considered to be particularly important in distance learning, where students have to regulate their learning themselves. Distance-learning students not only have to consciously decide to engage in learning behavior but also persist despite manifold distractions and less external regulation [ 4 ].

Previous research also indicates that the satisfaction of each basic need uniquely contributes to the regulation of learning behavior [ 19 ]. Indeed, studies have shown a positive relationship between persistence and the three basic needs (autonomy [ 20 ]; competence [ 21 ]; social relatedness [ 22 ]). Furthermore, all three basic psychological needs have been found to be related to procrastination. In previous research with undergraduate students, autonomy-supportive teaching behavior was positively related to satisfaction of the needs for autonomy and competence, both of which led to less procrastination [ 23 ]. A qualitative study by Klingsieck and colleagues [ 18 ] supports the findings of previous studies on the relations of perceived competence and autonomy with procrastination, but additionally suggests a lack of social relatedness as a contributing factor to procrastination. Haghbin and colleagues [ 24 ] likewise found that people with low perceived competence avoided challenging tasks and procrastinated.

SDT has been applied in research across various contexts, including work (e.g., [ 25 ]), health (e.g., [ 26 ]), everyday life (e.g., [ 27 ]) and education (e.g., [ 15 , 28 ]). Moreover, the pivotal role of the three basic psychological needs for learning outcomes and functioning has been shown across multiple countries, including collectivistic as well as individualistic cultures (e.g., [ 29 , 30 ]), leading to the conclusion that satisfaction of the three basic needs is a fundamental and universal determinant of human motivation and consequently learning success [ 31 ].

Self-determination theory in a distance learning setting during COVID-19

As Chen and Jang [ 28 ] observed, SDT lends itself particularly well to investigating distance learning, as the three basic needs for autonomy, competence and social relatedness all relate to important aspects of distance learning. For example, distance learning usually offers students greater freedom in deciding where and when they want to learn [ 32 ]. This may provide students with a sense of agency over their learning, leading to increased perceived autonomy. At the same time, it requires students to regulate their motivation and learning more independently [ 4 ]. In the unique context of distance learning during COVID-19, it should be noted that students could not choose whether and to what extent to engage in distance learning, but had to comply with external stipulations, which in turn may have had a negative effect on perceived autonomy. Furthermore, distance learning may also influence perceived competence, as this is in part developed by receiving explicit or implicit feedback from teachers and peers [ 33 ]. Implicit feedback in particular may be harder to receive in a distance learning setting, where informal discussions and social cues are largely absent. The lack of face-to-face contact may also impede social relatedness between students and their peers as well as students and their teachers. Well-established communication practices are crucial for distance learning success (see [ 34 ] for an overview). However, providing a nurturing social context requires additional effort and guidance from teachers, which in turn necessitates sufficient skills and preparation on their part [ 34 , 35 ]. Moreover, the sudden switch to distance learning due to COVID-19 did not leave teachers and students time to gradually adjust to the new learning situation [ 36 ]. As intrinsic motivation is considered particularly relevant in the context of distance education [ 28 , 37 ], applying the SDT framework to the novel situation of pandemic-induced distance learning may lead to important insights that allow for informed recommendations for teachers and educational institutions about how to proceed in the context of continued distance teaching and learning.

In summary, the COVID-19 situation is a completely new environment, and basic need satisfaction during learning under pandemic-induced conditions has not been explored before. Considering that closures of educational institutions have affected billions of students worldwide and have been strongly debated in some countries, it seems particularly relevant to gain insights into which factors consistently influence conducive or maladaptive learning behavior in these circumstances in a wide range of countries and contextual settings.

Therefore, the overall goal of this study is to investigate the well-established relationship between the three basic needs for autonomy, competence, and social relatedness with intrinsic motivation in the new and specific situation of pandemic-induced distance learning. Firstly, we examine the relationship between each of the basic needs with intrinsic motivation. We expect that perceived satisfaction of the basic needs for autonomy (H1a), competence (H1b) and social relatedness (H1c) would be positively related to intrinsic motivation. In our second research question, we furthermore extend SDT’s predictions regarding two important aspects of learning behavior–procrastination (as a consequence of low or absent intrinsic motivation) and persistence (as the implementation of the volitional part of motivation) and hypothesize that each basic need will be positively related to persistence and negatively related to procrastination, both directly (procrastination: H2a –c; persistence: H3a –c) and mediated by intrinsic motivation (procrastination: H4a –c; persistence: H5a –c). We also proposed that perceived autonomy, competence, and social relatedness would have a direct negative relation with procrastination (H6a –c) and a direct positive relation with persistence (H7a –c). Finally, we investigate SDT’s claim of universality, and assume that the aforementioned relationships will emerge across countries we therefore expect a similar pattern of results in all observed countries (H8a –c). As previous studies have indicated that gender [ 4 , 17 , 38 ] and age [ 39 , 40 ]. May influence intrinsic motivation, persistence, and procrastination, we included participants’ gender and age as control variables.

Study design

Due to the circumstances, we opted for a cross-sectional study design across multiple countries, conducted as an online survey. We decided for an online-design due to the pandemic-related restrictions on physical contact with potential survey participants as well as due to the potential to reach a larger audience. As we were interested in the current situation in schools than in long-term development, and we were particularly interested in a large-scale section of the population in multiple countries, we decided on a cross-sectional design. In addition, a multi-country design is particularly interesting in a pandemic setting: During this global health crisis, educational institutions in all countries face the same challenge (to provide distance learning in a way that allows students to succeed) but do so within different frameworks depending on the specific measures each country has implemented. This provides a unique basis for comparing the effects of need fulfillment on students’ learning behavior cross-nationally, thus testing the universality of SDT.

Sample and procedure

The study was carried out across 17 countries, with central coordination taking place in Austria. It was approved and supported by the Austrian Federal Ministry of Education, Science and Research and conducted online. International cooperation partners were recruited from previously established research networks (e.g., European Family Support Network [COST Action 18123]; Transnational Collaboration on Bullying, Migration and Integration at School Level [COST Action 18115]; International Panel on Social), resulting in data collection in 16 countries (Albania, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, USA) in addition to Austria. Data collection was carried out between April and August 2020. During this period, all participating countries were in some degree of pandemic-induced lockdown, which resulted in universities temporarily switching to distance learning. The online questionnaires were distributed among university students via online surveys by the research groups in each respective country. No restrictions were placed on participation other than being enrolled at a university in the sampling country. Participants were informed about the goals of the study, expected time it would take to fill out the questionnaire, voluntariness of participation and anonymity of the acquired data. All research partners ensured that all ethical and legal requirements related to data collection in their country context were met.

Only data from students who gave their written consent to participate, had reached the age of majority (18 or older) and filled out all questions regarding the study’s main variables were included in the analyses (for details on data cleaning rules and exclusion criteria, see [ 41 ]). Additional information on data collection in the various countries is provided in S1 Table in S1 File .

The overall sample of N = 15,462 students was predominantly female (71.7%, 27.4% male and 0.7% diverse) and ranged from 18 to 71 years, with the average participant age being 24.41 years ( SD = 6.93, Mdn = 22.00). Sample descriptives per country are presented in Table 1 .

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https://doi.org/10.1371/journal.pone.0257346.t001

The variables analyzed here were part of a more extensive questionnaire; the complete questionnaire, as well as the analysis code and the data set, can be found at OSF [ 42 ] In order to take the unique situation into account, existing scales were adapted to the current pandemic context (e.g., adding “In the current home-learning situation …”), and supplemented with a small number of newly developed items. Subsequently, the survey was revised based on expert judgements from our research group and piloted with cognitive interview testing. The items were sent to the research partners in English and translated separately by each respective research team either using the translation-back-translation method or by at least two native-speaking experts. Subsequently, any differences were discussed, and a consolidated version was established.

To assure the reliability of the scales, we analyzed them using alpha coefficients separately for each country (see S2–S18 Tables in S1 File ). All items were answered on a rating scale from 1 (= strongly agree) to 5 (= strongly disagree) and students were instructed to answer with regard to the current situation (distance learning during the COVID-19 lockdown). Analyses were conducted with recoded items so that higher values reflected higher agreement with the statements.

Perceived autonomy was measured with two newly constructed items (“Currently, I can define my own areas of focus in my studies” and “Currently, I can perform tasks in the way that best suits me”; average α = .78, ranging from .62 to .86).

Perceived competence was measured with three items, which were constructed based on the Work-related Basic Need Satisfaction Scale (W-BNS; [ 25 ]) and transferred to the learning context (“Currently, I am dealing well with the demands of my studies”, “Currently, I have no doubts about whether I am capable of doing well in my studies” and “Currently, I am managing to make progress in studying for university”; average α = .83, ranging from .74 to .91).

Perceived social relatedness was assessed with three items, based on the W-BNS [ 43 ], (“Currently, I feel connected with my fellow students”, “Currently, I feel supported by my fellow students”) and the German Basic Psychological Need Satisfaction and Frustration Scale [ 44 ]; “Currently, I feel connected with the people who are important to me (family, friends)”; average α = .73, ranging from .64 to .88).

Intrinsic motivation was measured with three items which were slightly adapted from the Scales for the Measurement of Motivational Regulation for Learning in University Students (SMR-LS; [ 45 ]; “Currently, doing work for university is really fun”, “Currently, I am really enjoying studying and doing work for university” and “Currently, I find studying for university really exciting”; average α = .91, ranging from .83 to .94).

Procrastination was measured with three items adapted from the Procrastination Questionnaire for Students (Prokrastinationsfragebogen für Studierende; PFS; [ 46 ]): “In the current home-learning situation, I postpone tasks until the last minute”, “In the current home-learning situation, I often do not manage to start a task when I set out to do so”, and “In the current home-learning situation, I only start working on a task when I really need to”; average α = .88, ranging from .74 to .91).

Persistence was measured with three items adapted from the EPOCH measure [ 47 ]: “In the current home-learning situation, I finish whatever task I begin”, “In the current home-learning situation, I keep at my tasks until I am done with them” and “In the current home-learning situation, once I make a plan to study, I stick to it”; average α = .81, ranging from .74 to .88).

Data analysis.

Data analyses were conducted using IBM SPSS version 26.0 and Mplus version 8.4. First, we tested for measurement invariance between countries prior to any substantial analyses. We conducted a multigroup confirmatory factor analysis (CFAs) for all scales individually to test for configural, metric, and scalar invariance [ 48 , 49 ] (see S19 Table in S1 File ). We used maximum likelihood parameter estimates with robust standard errors (MLR) to deal with the non-normality of the data. CFI and RMSEA were used as indicators for absolute goodness of model fit. In line with Hu and Bentler [ 50 ], the following cutoff scores were considered to reflect excellent and adequate fit to the data, respectively: (a) CFI > 0.95 and CFI > 0.90; (b) RMSEA < .06 and RMSEA < .08. Relative model fit was assessed by comparing BICs of the nested models, with smaller BIC values indicating a better trade-off between model fit and model complexity [ 51 ]. Configural invariance indicates a factor structure that is universally applicable to all subgroups in the analysis, metric invariance implies that participants across all groups attribute the same meaning to the latent constructs measured, and scalar invariance indicates that participants across groups attribute the same meaning to the levels of the individual items [ 51 ]. Consequently, the extent to which the results can be interpreted depends on the level of measurement invariance that can be established.

For the main analyses, three latent multiple group mediation models were computed, each including one of the basic psychological needs as a predictor, intrinsic motivation as the mediator and procrastination and persistence as the outcomes. These three models served to test the hypothesis that perceived autonomy, competence and social relatedness are related to levels of procrastination and persistence, both directly and mediated through intrinsic motivation. We used bootstrapping in order to provide analyses robust to non-normal distribution variations, specifying 5,000 bootstrap iterations [ 52 ]. Results were estimated using the maximum likelihood (ML) method. Bias-corrected bootstrap confidence intervals are reported.

Finally, in an exploratory step, we investigated the international applicability of the direct and mediated effects. To this end, an additional set of latent mediation models was computed where the path estimates were fixed in order to create an average model across all countries. This was prompted by the consistent patterns of results across countries we observed in the multigroup analyses. Model fit indices of these average models were compared to those of the multigroup models in order to establish the similarity of path coefficients between countries.

Statistical prerequisites

Table 2 provides overall descriptive statistics and correlations for all variables (see S2–S18 Tables in S1 File for descriptive statistics for the individual countries).

https://doi.org/10.1371/journal.pone.0257346.t002

Metric measurement variance, but not scalar measurement invariance could be established for a simple model including the three individual items and no inter-correlations between perceived competence, perceived social relatedness, intrinsic motivation, and procrastination. For these four variables, the metric invariance model had a good absolute fit, whereas the scalar model did not, due to too high RMSEA; moreover, the relative fit was best for the metric model compared to both the configural and scalar model (see S18 Table in S1 File ). Metric, but not scalar invariance could also be established for persistence after modelling residual correlations between items 1 and 2 and items 2 and 3 of the scale. This was necessary due to the similar wording of the items (see “Measures” section for item wordings). Consequently, the same residual correlations were incorporated into all mediation models.

Finally, as the perceived autonomy scale consisted of only two items, it had to be fitted in a model with a correlating factor in order to compute measurement invariance. Both perceived competence and perceived social relatedness were correlated with perceived autonomy ( r = .59** and r = .31**, respectively; see Table 2 ). Therefore, we fit two models combining perceived autonomy with each of these factors; in both cases, metric measurement invariance was established (see S19 Table in S1 File ).

In summary, these results suggest that the meaning of all constructs we aimed to measure was understood similarly by participants across different countries. Consequently, we were able to fit the same mediation model in all countries and compare the resulting path coefficients.

Both gender and age were statistically significantly correlated with perceived competence, perceived social relatedness, intrinsic motivation, procrastination, and persistence (see S20–S22 Tables in S1 File ).

Mediation analyses

Autonomy hypothesis..

We hypothesized that higher perceived autonomy would relate to less procrastination and more persistence, both directly and indirectly (mediated through intrinsic learning motivation). Indeed, perceived autonomy was related negatively to procrastination (H6a) in most countries. Confidence intervals did not include zero in 10 out of 17 countries, all effect estimates were negative and standardized effect estimates ranged from b stand = - .02 to -.46 (see Fig 1 ). Furthermore, perceived autonomy was directly positively related to persistence in most countries. Specifically, for the direct effect of perceived autonomy on persistence (H7a), all but one country (USA, b stand = -.02; p = .621; CI [-.13, .08]) exhibited distinctly positive effect estimates ranging from b stand = .18 to .72 and confidence intervals that did not include zero.

Countries are ordered by sample size from top (highest) to bottom (lowest).

https://doi.org/10.1371/journal.pone.0257346.g001

In terms of indirect effects of perceived autonomy on procrastination mediated by intrinsic motivation (H7a), confidence intervals did not include zero in 8 out of 17 countries and effect estimates were mostly negative, ranging from b stand = -.33 to .03. Indirect effects of perceived autonomy on persistence (mediated by intrinsic motivation; H5a) were distinctly positive and confidence intervals did not include zero in 12 out of 17 countries. The indirect effect estimates and confidence intervals for all remaining countries were consistently positive, with the standardized effect estimates ranging from b stand = .13 to .39, indicating a robust, positive mediated effect of autonomy on persistence. Fig 2 displays the unstandardized path coefficients and their two-sided 5% confidence intervals for the indirect effects of perceived autonomy on procrastination via intrinsic motivation (left) and of perceived autonomy on persistence via intrinsic motivation (right).

https://doi.org/10.1371/journal.pone.0257346.g002

Unstandardized and standardized path coefficients, standard errors, p-values and bias-corrected bootstrapped confidence intervals for the direct and indirect effects of perceived autonomy on procrastination and persistence for each country are provided in S23–S26 Tables in S1 File , respectively.

Competence hypothesis. Secondly, we hypothesized that higher perceived competence would relate to less procrastination and more persistence both directly and indirectly, mediated through intrinsic learning motivation. Direct effects on procrastination (H6b) were negative in most countries and confidence intervals did not include zero in 10 out of 17 countries (see Fig 3 ).

https://doi.org/10.1371/journal.pone.0257346.g003

Standardized effect estimates ranged from b stand = -.02 to -.60, with 10 out of 17 countries exhibiting at least a medium-sized effect. Correspondingly, effect estimates for the direct effects on persistence were positive everywhere except the USA and confidence intervals did not include zero in 14 out of 17 countries (see Fig 3 ). Standardized effect estimates ranged from b stand = -.05 to .64 with 14 out of 17 countries displaying an at least medium-sized positive effect.

The pattern of results for the indirect effects of perceived competence on procrastination mediated by learning motivation (H4b) is illustrated in Fig 4 : Effect estimates were negative with the exception of China and the USA. Confidence intervals did not include zero in 7 out of 17 countries. Standardized effect estimates range between b stand = .06 and -.46. Indirect effects of perceived competence on persistence were positive everywhere except for two countries and confidence intervals did not include zero in 7 out of 17 countries (see Fig 4 ). Standardized effect estimates varied between b stand = -.07 and .46 (see S23–S26 Tables in S1 File for unstandardized and standardized path coefficients).

https://doi.org/10.1371/journal.pone.0257346.g004

Social relatedness hypothesis.

Finally, we hypothesized that stronger perceived social relatedness would be both directly and indirectly (mediated through intrinsic learning motivation) related to less procrastination and more persistence. The pattern of results was more ambiguous here than for perceived autonomy and perceived competence. Direct effect estimates on procrastination (H6c) were negative in 12 countries; however, the confidence intervals included zero in 12 out of 17 countries (see Fig 5 ). Standardized effect estimates ranged from b stand = -.01 to b stand = .33. The direct relation between perceived social relatedness and persistence (H7c) yielded 14 negative and three positive effect estimates. Confidence intervals did not include zero in 7 out of 17 countries (see Fig 5 ), with standardized effect estimates ranging from b stand = -.01 to b stand = .31.

https://doi.org/10.1371/journal.pone.0257346.g005

In terms of indirect effects of perceived social relatedness being related to procrastination mediated by intrinsic motivation (H4c), the pattern of results was consistent: All effect estimates except those for the USA were clearly negative, and confidence intervals did not include zero in 15 out of 17 countries (see Fig 6 ). Standardized effect estimates ranged between b stand = .00 and b stand = -.46. Indirect paths of perceived social relatedness on persistence showed positive effect estimates and standardized effect estimates ranging from b stand = .00 to .44 and confidence intervals not including zero in 16 out of 17 countries (see Fig 6 ; see S23–S26 Tables in S1 File for unstandardized and standardized path coefficients).

https://doi.org/10.1371/journal.pone.0257346.g006

Meta-analytic approach

Due to the overall similarity of the results across many countries, we decided to compute, in an additional, exploratory step, the same models with path estimates fixed across countries. This resulted in three models with average path estimates across the entire sample. Standardized path coefficients for the direct and indirect effects of the basic psychological needs on procrastination and persistence are presented in S27 and S28 Tables in S1 File , respectively. We compared the model fits of these three average models to those of the multigroup mediation models: If the fit of the average model is better than that of the multigroup model, it indicates that the individual countries are similar enough to be combined into one model. The amount of explained variance per model, outcome variable and country are provided in S29 Table in S1 File for procrastination and S30 Table in S1 File for persistence.

Perceived autonomy.

Relative model fit was better for the perceived autonomy model with fixed paths (BIC = 432,707.89) compared to the multigroup model (BIC = 432,799.01). Absolute model fit was equally good in the multigroup model (RMSEA = 0.05, CFI = 0.98, TLI = 0.97) and in the fixed path model (RMSEA = 0.05, CFI = 0.97, TLI = 0.97). Consequently, the general model in Fig 7 describes the data from all 17 countries equally well. The average amount of explained variance, however, is slightly higher in the multigroup model, with 19.9% of the variance in procrastination and 33.7% of the variance in persistence explained, as compared to 18.3% and 27.6% in the fixed path model. The amount of variance explained increased substantially in some countries when fixing the paths: in the multigroup model, explained variance ranges from 2.2% to 44.4% for procrastination and from 0.9% to 69.9% for persistence, compared to 13.0% - 27.7% and 18.2% to 63.2% in the fixed path model. Notably, the amount of variance explained did not change much in the three countries with the largest samples, Austria, Sweden, and Finland; countries with much smaller samples and larger confidence intervals were more affected.

*** p = < .001.

https://doi.org/10.1371/journal.pone.0257346.g007

Overall, perceived autonomy had significant direct and indirect effects on both procrastination and persistence; higher perceived autonomy was related to less procrastination directly ( b unstand = -.27, SE = .02, p = < .001) and mediated by learning motivation ( b unstand = -.20, SE = .01, p = < .001) and to more persistence directly ( b unstand = .24, SE = .01, p = < .001) and mediated by learning motivation ( b unstand = .12, SE = .01, p = < .001). Direct effects for the autonomy model are shown in Fig 7 ; for the indirect effects see Table 3 .

https://doi.org/10.1371/journal.pone.0257346.t003

Effects of age and gender varied across countries (see S20 Table in S1 File ).

Perceived competence.

For the perceived competence model, relative fit decreased when fixing the path coefficient estimates (BIC = 465,830.44 to BIC = 466,020.70). The absolute fit indices were also better for the multigroup model (RMSEA = 0.05, CFI = 0.97, TLI = 0.96) than for the fixed path model (RMSEA = 0.06, CFI = 0.96, TLI = 0.96). Hence, multigroup modelling describes the data across all countries somewhat better than a fixed path model as depicted in Fig 8 . Correspondingly, the fixed path model explained less variance on average than did the multigroup model, with 23.2% instead of 24.3% of the variance in procrastination and 32.9% instead of 37.3% of the variance in persistence explained. Explained variance ranged from 1.0% to 51.9% for procrastination in the multigroup model, as compared to 13.9% - 34.4% in the fixed path model. The amount of variance in persistence explained ranged from 1.0% to 58.1% in the multigroup model and from 23.5% to 55.9% in the fixed path model (see S29 and S30 Tables in S1 File ).

https://doi.org/10.1371/journal.pone.0257346.g008

Overall, higher perceived competence was related to less procrastination ( b unstand = -.44, SE = .02, p = < .001) and to higher persistence ( b unstand = .32, SE = .01, p = < .001). These effects were partly mediated by intrinsic learning motivation ( b unstand = -.11, SE = .01, p = < .001, and b unstand = .07, SE = .01, p = < .001, respectively; see Table 3 ). Effects of gender and age varied between countries, see S21 Table in S1 File .

Perceived social relatedness.

Finally, the perceived social relatedness model with fixed paths had a relatively better model fit (BIC = 479,428.46) than the multigroup model (BIC = 479,604.61). Likewise, the absolute model fit was similar in the model with path coefficients fixed across countries (RMSEA = 0.05, CFI = 0.97, TLI = 0.96) and the multigroup model (RMSEA = 0.05, CFI = 0.97, TLI = 0.97). The multigroup model explained 17.6% of the variance in procrastination and 26.3% of the variance in persistence, as compared to 15.2% and 21.6%, respectively in the fixed path model. Explained variance for procrastination ranged between 0.5% and 48.1% in the multigroup model, and from 9.0% to 23.0% in the fixed path model. Similarly, the multigroup model explained between 1.0% and 56.5% of the variance in persistence across countries, while the fixed path model explained between 15.6% and 48.3% (see S29 and S30 Tables in S1 File ).

Hence, the fixed path model depicted in Fig 9 is well-suited for describing data across all 17 countries. Higher perceived social relatedness is related to less procrastination both directly ( b unstand = -.06, SE = .01, p = < .001) and indirectly through learning motivation ( b unstand = -.12, SE = .01, p = < .001). Likewise, it is related to higher persistence both directly ( b unstand = .07, SE = .01, p = < .001) and indirectly through learning motivation ( b unstand = .08, SE = .00, p = < .001; see Table 3 ). Effects of gender and age are shown in S22 Table in S1 File .

https://doi.org/10.1371/journal.pone.0257346.g009

The aim of this study was to extend current research on the association between the basic psychological needs for autonomy, competence, and social relatedness with intrinsic motivation and two important aspects of learning behavior—procrastination and persistence—in the new and unique situation of pandemic-induced distance learning. We also investigated SDT’s [ 7 ] postulate that the relation between basic psychological need satisfaction and active (persistence) as well as passive (procrastination) learning behavior is mediated by intrinsic motivation. To test the theory’s underlying claim of universality, we collected data from N = 15,462 students across 17 countries in Europe, Asia, and North America.

Confirming our hypothesis, we found that the three basic psychological needs were consistently and positively related to intrinsic motivation in all countries except for the USA (H1a - c). This consistent result is in line with self-determination theory [ 7 ] and other previous studies (e.g., 9), which have found that satisfaction of the three basic needs for autonomy, competence and social relatedness is related to higher intrinsic motivation. Notably, the association with intrinsic motivation was stronger for perceived autonomy and perceived competence than for perceived social relatedness. This also has been found in previous studies [ 4 , 9 , 28 ]. Pandemic-induced distance learning, where physical and subsequential social contact in all areas of life was severely constricted, might further exacerbate this discrepancy, as instructors may have not been able to establish adequate communication structures due to the rapid switch to distance learning [ 36 , 53 ]. As hypothesized, intrinsic motivation was in general negatively related to procrastination (H2a - c) and positively related to persistence (H3a - c), indicating that students who are intrinsically motivated are less prone to procrastination and more persistent when studying. This again underlines the importance of intrinsic motivation for adaptive learning behavior, even and particularly in a distance learning setting, where students are more prone to disengage from classes [ 34 ].

The mediating effect of intrinsic motivation on procrastination and persistence

Direct effects of the basic needs on the outcomes were consistently more ambiguous (with smaller effect estimates and larger confidence intervals, including zero in more countries) than indirect effects mediated by intrinsic motivation. This difference was particularly pronounced for perceived social relatedness, where a clear negative direct effect on procrastination (H6c) could be observed only in the three countries with the largest sample size (Austria, Sweden, Finland) and Romania, whereas the confidence interval in most countries included zero. Moreover, in Estonia there was even a clear positive effect. The unexpected effect in the Estonian sample may be attributed to the fact that this country collected data only from international exchange students. Since the lockdown in Estonia was declared only a few weeks after the start of the semester, international exchange students had only a very short period of time to establish contacts with fellow students on site. Accordingly, there was probably little integration into university structures and social contacts were maintained more on a personal level with contacts from the home country. Thus, such students’ fulfillment of this basic need might have required more time and effort, leading to higher procrastination and less persistence in learning.

A diametrically opposite pattern was observed for persistence (H7c), where some direct effects of social relatedness were unexpectedly negative or close to zero. We therefore conclude that evidence for a direct negative relationship between social relatedness and procrastination and a direct positive relationship between social relatedness and persistence is lacking. This could be due to the specificity of the COVID-19 situation and resulting lockdowns, in which maintaining social contact took students’ focus off learning. In line with SDT, however, indirect effects of perceived social relatedness on procrastination (H4c) and persistence (H5c) mediated via intrinsic motivation were much more visible and in the expected directions. We conclude that, while the direct relation between perceived social relatedness and procrastination is ambiguous, there is strong evidence that the relationship between social relatedness and the measured learning behaviors is mediated by intrinsic motivation. Our results strongly underscore SDT’s assumption that close social relations promote intrinsic motivation, which in turn has a positive effect on learning behavior (e.g., [ 6 , 14 ]). The effects for perceived competence exhibited a somewhat clearer and hypothesis-conforming pattern. All direct effects of perceived competence on procrastination (H6b) were in the expected negative direction, albeit with confidence intervals spanning zero in 7 out of 17 countries. Direct effects of perceived competence on persistence (H7b) were consistently positive with the exception of the USA, where we observed a very small and non-significant negative effect. Indirect effects of perceived competence on procrastination (H4b) and persistence (H5b) as mediated by intrinsic motivation were mostly consistent with our expectations as well. Considering this overall pattern of results, we conclude that there is strong evidence that perceived competence is negatively associated with procrastination and positively associated with persistence. Furthermore, our results also support SDT’s postulate that the relationship between perceived competence and the measured learning behaviors is mediated by intrinsic motivation.

It is notable that the estimated direct effects of perceived competence on procrastination and persistence were higher than the indirect effects in most countries we investigated. Although SDT proposes that perceived competence leads to higher intrinsic motivation, Deci and Ryan [ 8 ] also argue that it affects all types of motivation and regulation, including less autonomous forms such as introjected and identified motivation, indicating that if the need for competence is not satisfied, all types of motivation are negatively affected. This may result in a general amotivation and lack of action. In our study, we only investigated intrinsic motivation as a mediator. For future research, it might be advantageous to further differentiate between different types of externally and internally controlled behavior. Furthermore, perceived competence increases when tasks are experienced as optimally challenging [ 7 , 54 ]. However, in order for instructors to provide the optimal level of difficulty and support needed, frequent communication with students is essential. Considering that data collection for the present study took place at a time of great uncertainty, when many countries had only transitioned to distance learning a few weeks prior, it is reasonable to assume that both structural support as well as communication and feedback mechanisms had not yet matured to a degree that would favor individualized and competency-based work.

However, our findings corroborate those from earlier studies insofar as they underline the associations between perceived competence and positive learning behavior (e.g., [ 19 ]), that is, lower procrastination [ 18 ] and higher persistence (e.g., [ 21 ]), even in an exceptional situation like pandemic-induced distance learning.

Turning to perceived autonomy, although the confidence intervals for the direct effects of perceived autonomy on procrastination (H6a) did span zero in most countries with smaller sample sizes, all effect estimates indicated a negative relation with procrastination. We expected these relationships from previous studies [ 18 , 23 ]; however, the effect might have been even more pronounced in the relatively autonomous learning situation of distance learning, where students usually have increased autonomy in deciding when, where, and how to learn. While this bears the risk of procrastination, it also comes with the opportunity to consciously delay less pressing tasks in favor of other, more important or urgent tasks (also called strategic delay ) [ 5 ], resulting in lower procrastination. In future studies, it might be beneficial to differentiate between passive forms of procrastination and active strategic delay in order to obtain more detailed information on the mechanisms behind this relationship. Direct effects of autonomy on persistence (H7a) were consistently positive. Students who are free to choose their preferred time and place to study may engage more with their studies and therefore be more persistent.

Indirect effects of perceived autonomy on procrastination mediated by intrinsic motivation (H4a) were negative in all but two countries (China and the USA), which is generally consistent with our hypothesis and in line with previous research (e.g., [ 23 ]). Additionally, we found a positive indirect effect of autonomy on persistence (H5a), indicating that autonomy and intrinsic motivation play a crucial role in students’ persistence in a distance learning setting. Based on our results, we conclude that perceived autonomy is negatively related to procrastination and positively related to persistence, and that this relationship is mediated by intrinsic motivation. It is worth noting that, unlike with perceived competence, the direct and indirect effects of perceived autonomy on the outcomes procrastination and persistence were similarly strong, suggesting that perceived autonomy is important not only as a driver of intrinsic motivation but also at a more direct level. It is important to make the best possible use of the opportunity for greater autonomy that distance learning offers. However, autonomy is not to be equated with a lack of structure; instead, learners should be given the opportunity to make their own decisions within certain framework conditions.

The applicability of self-determination theory across countries

Overall, the results of our mediation analysis for the separate countries support the claim posited by SDT that basic need satisfaction is essential for intrinsic motivation and learning across different countries and settings. In an exploratory analysis, we tested a fixed path model including all countries at once, in order to test whether a simplified general model would yield a similar amount of explained variance. For perceived autonomy and social relatedness, the model fit increased, whereas for perceived competence it decreased slightly compared to the multigroup model. However, all fixed path models exhibited adequate model fit. Considering that the circumstances in which distance learning took place in different countries varied to some degree (see also Limitations), these findings are a strong indicator for the universality of SDT.

Study strengths and limitations

Although the current study has several strengths, including a large sample size and data from multiple countries, three limitations must be considered. First, it must be noted that sample sizes varied widely across the 17 countries in our study, with one country above 6,000 (Austria), two above 1,000 (Finland and Sweden) and the rest ranging between 104 and 905. Random sampling effects are more problematic in smaller samples; hence, this large variation weakens our ability to conduct cross-country comparisons. At the same time, small sample sizes weaken the interpretability of results within each country; thus, our results for Austria, Finland and Sweden are considerably more robust than for the remaining fourteen countries. Additionally, two participating countries collected specific subsamples: In China, participants were only recruited from one university, a nursing school. In Estonia, only international exchange students were invited to participate. Nevertheless, with the exception of the unexpected positive direct relationship between social relatedness and procrastination, all observed divergent effects were non-significant. Indeed, this adds to the support for SDT’s claims to universality regarding the relationship between perceived autonomy, competence, and social relatedness with intrinsic motivation: Results in the included countries were, despite their differing subsamples, in line with the overall trend of results, supporting the idea that SDT applies equally to different groups of learners.

Second, due to the large number of countries in our sample and the overall volatility of the situation, learning circumstances were not identical for all participants. Due to factors such as COVID-19 case counts and national governments’ political priorities, lockdown measures varied in their strictness across settings. Some universities were fully closed, some allowed on-site teaching for particular groups (e.g., students in the middle of a laboratory internship), and some switched to distance learning but held exams on site (see S1 Table in S1 File for further information). Therefore, learning conditions were not as comparable as in a strict experimental setting. On the other hand, this strengthens the ecological validity of our study. The fact that the pattern of results was similar across contexts with certain variation in learning conditions further supports the universal applicability of SDT.

Finally, due to the novelty of the COVID-19 situation, some of the measures were newly developed for this study. Due to the need to react swiftly and collect data on the constantly evolving situation, it was not possible to conduct a comprehensive validation study of the instruments. Nevertheless, we were able to confirm the validity of our instruments in several ways, including cognitive interview testing, CFAs, CR, and measurement invariance testing.

Conclusion and future directions

In general, our results further support previous research on the relation between basic psychological need fulfilment and intrinsic motivation, as proposed in self-determination theory. It also extends past findings by applying this well-established theory to the new and unique situation of pandemic-induced distance learning across 17 different countries. Moreover, it underlines the importance of perceived autonomy and competence for procrastination and persistence in this setting. However, various other directions for further research remain to be pursued. While our findings point to the relevance of social relatedness for intrinsic motivation in addition to perceived competence and autonomy, further research should explore the specific mechanisms necessary to promote social connectedness in distance learning. Furthermore, in our study, we investigated intrinsic motivation, as the most autonomous form of motivation. Future research might address different types of externally and internally regulated motivation in order to further differentiate our results regarding the relations between basic need satisfaction and motivation. Finally, a longitudinal study design could provide deeper insights into the trajectory of need satisfaction, intrinsic motivation and learning behavior during extended periods of social distancing and could provide insights into potential forms of support implemented by teachers and coping mechanisms developed by students.

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With Online Learning, ‘Let’s Take a Breath and See What Worked and Didn’t Work’

The massive expansion of online higher education created a worldwide laboratory to finally assess its value and its future.

By Jon Marcus

This article is part of our Learning special report about how the pandemic has continued to change how we approach education.

Kameshwari Shankar watched for years as college and university courses were increasingly taught online instead of face to face, but without a definitive way of understanding which students benefited the most from them, or what if anything they learned.

As an associate professor of economics at City College in New York, Dr. Shankar knew that one of the most important requirements of scientific research was often missing from studies of the effectiveness of online higher education: a control group.

Then came the Covid-19 pandemic, forcing almost everyone on earth online and creating a randomized trial on a planetary scale with a control group so big, it was a researcher’s wildest dream.

“The pandemic and the lockdown — that’s a great natural experiment,” said Dr. Shankar. A study she co-authored called it “a gold mine of evidence.”

Now the results of this experiment are starting to come in. They suggest that online higher education may work better than prepandemic research suggested, and that it is evolving decisively toward a combination of in-person and online, or “blended,” classes.

“For two years we’ve had sort of a petri dish of experimenting with learning online,” said Anant Agarwal, chief platform officer of the online program management company 2U and former CEO of edX, the online provider created by the Massachusetts Institute of Technology and Harvard and sold last year to 2U for $800 million. “Now people are sitting down and saying, ‘Let’s take a breath. Let’s see what worked and didn’t work.’ ”

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What did distance learning accomplish?

Millions of U.S. school children ended their academic year via remote learning. How did this unplanned experiment measure up?

Vol. 51, No. 6 Print version: page 54

• Schools and Classrooms

More than 56 million students attend public and private elementary, middle and high schools in the United States. Last March, the vast majority of them took part in an impromptu experiment when most schools locked their doors to protect against the novel coronavirus. Overnight, teachers were forced to figure out how to translate face-to-face lessons into remote-learning lesson plans.

As schools kick off the 2020–21 school year, there are many unknowns. Some form of distance learning remains likely—either entirely remote, in combination with scaled-back in-person instruction or as a future possibility if new waves of COVID-19 outbreaks emerge.

As educators and administrators plan for that uncertain future, they must also assess how students fared. The pandemic has presented many new challenges in addition to school closures, including the death of loved ones and economic hardship. “Students have been exposed to a tremendous range of experiences, ranging from traumatic to enriched,” says educational psychologist Sara Rimm-Kaufman, PhD, a professor of education at the University of Virginia.

While some students have thrived and learned during the pandemic, others are likely to have fallen behind. Regardless of ZIP code or family background, schools are, in theory, places where all students can receive education and support. But the coronavirus shutdown has emphasized (and widened) existing disparities in education.

“When kids come to a classroom, it’s easy to imagine they’re all the same. But we can’t expect the same outcomes from a kid learning on his own computer at his family’s vacation home and a child who doesn’t even have a table to sit at,” says Avi Kaplan, PhD, a professor of educational psychology at Temple University.

But the experience may yet have a silver lining, he adds. “We have a tendency to go back to what we thought was normal. But there’s an opportunity here to unlearn things that people knew were not working.”

The digital divide

When schools closed abruptly, teachers were forced to design remote-learning plans quickly. The plans they created were all over the map, says Helenrose Fives, PhD, a professor of educational foundations at Montclair State University and president of APA’s Div. 15 (Educational Psychology). In late March, Fives and colleagues began surveying teachers about their experiences with distance learning in New Jersey—a state with a staggering 584 school districts.

“It seems like every district is doing something different. The variability in how districts are approaching this is shocking,” she says.

Even within a single district, student experiences are wide-ranging. Teachers and parents have reported that some kids are thriving with fewer social distractions, or have been energized by their newfound independence. Yet many other children lack devices or reliable access to the internet. And while some families have parents who can oversee their children’s remote learning, many youths are caring for younger siblings while their parents work in essential jobs or living with the chaos of unemployment or homelessness.

“It’s a question of privilege,” says Michele Gregoire Gill, PhD, a professor of educational psychology at the University of Central Florida. “Some families are just in survival mode.”

The inequities are hard to overstate, Gill and other experts say. A survey of 1,500 U.S. families by advocacy group ParentsTogether released in late May found 83% of children in families in the highest income quartile were logging in to distance learning every day. Just 3.7% of those families reported their children were participating in distance learning once a week or less, compared with 38% of students from families in the lowest income quartile.

That missed instructional time is likely to be a serious setback for low-income students. Previous research has found that chronic absenteeism—usually defined as missing at least 10% of school days—affects reading levels, grade retention, graduation rates and dropout rates (Allison, M.A., et al., Pediatrics , Vol. 143, No. 2, 2019). Chronic absenteeism disproportionately affects kids living in poverty in the best of times, as Children’s National Hospital pediatrician Danielle Dooley, MD, and colleagues describe in an opinion piece on the effects of COVID-19 on low-income children ( JAMA Pediatrics , published online, 2020). Remote learning during COVID-19 is likely to widen that disparity, they say.

Students from low-income homes aren’t the only ones at risk of slipping through the cracks. Families who speak other languages, undocumented immigrants and students with special needs are also at risk of missing out on the services to which they’re entitled. Children with disabilities or special needs are legally entitled to special education services, including speech-language therapy, autism interventions, occupational therapy and psychological services. But many of those don’t translate easily to the remote platforms available. The ParentsTogether survey painted a grim picture for special education students, with 40% of parents reporting they weren’t receiving any support, and just 20% reporting their children were receiving all of the special education services they typically received in school.

Does remote learning work?

Students from disadvantaged backgrounds and those with special needs may face the biggest educational challenges. But some research indicates that all students could start the year far behind. Megan Kuhfeld, PhD, and Beth Tarasawa, PhD, of the Collaborative for Student Growth at the educational nonprofit organization NWEA, published a white paper analyzing past research on learning loss over summer break. They predict that overall, students in grades three through eight will return to school with roughly 70% of the learning gains in reading and less than 50% of the learning gains in math compared with a typical year ( The COVID-19 Slide: What Summer Learning Loss Can Tell Us About the Potential Impact of School Closures on Student Academic Achievement , Collaborative for Student Growth, 2020).

That’s not to say online learning itself isn’t effective. “Research generally shows that online learning can be as effective as in-person instruction, if you have a good setup,” Gill says. But what most schools were doing in the spring wasn’t true online learning, she adds. “Teachers didn’t have prepared online content, so they were trying to convert what they normally do to an online platform. It was emergency triage.”

“Remote learning is not the same as online learning,” agrees Aroutis Foster, PhD, a professor of learning technologies at Drexel University. True online learning happens on digital platforms designed for that purpose, often with personalized content for each student and options to use their choice of digital tools. “Online learning facilitates different types of learning preferences, provides learner flexibility and uses online quality metrics,” Foster says. But for many students, distance learning during COVID-19 included none of those features, and instead involved tuning in at a set time to listen to teachers lecture on Zoom or Google Meet.

What’s more, online learning programs that were working before coronavirus might not be as effective without teacher support and the structure of in-person learning. In a data tool called the Opportunity Insights Economic Tracker , economists at Brown University and Harvard University looked at how U.S. students were performing in an online math program before and after the coronavirus shutdown. As of May 31, total student progress in online math coursework decreased by 64.2% compared with January. In low-income ZIP codes, math progress fell 74.8%, compared with 36.1% in high-income ZIP codes.

Connecting lessons to children’s interests is especially important in remote settings where students don’t have the classroom structure to guide them.

Successful learning environments

With continued remote learning a distinct possibility, educators will be considering what went well during the spring of 2020, and what they can improve on. Educational psychology offers clues about what factors are important to creating successful learning environments. To stay motivated when learning at home, students need to feel competence, relatedness (a sense of belonging and connection with others) and autonomy, says Kaplan. According to self-determination theory (Ryan, R.M., & Deci, E.L., American Psychologist , Vol. 55, No. 1, 2000), those needs are vital for self-motivation and well-being in many domains, including education. In a practice brief for parents who are homeschooling during quarantine ( Homeschooling Under Quarantine , APA Div. 15, 2020), Kaplan and Debra A. Bell, PhD, describe how parents can support a child’s competence (emphasize improvement with realistic expectations), relatedness (consider a child’s needs, listen empathetically and provide emotional support) and autonomy (provide meaningful choices and allow a child to incorporate personal interests).

Tying lessons into children’s own interests may be especially important in remote settings, Foster says, when students don’t have the classroom structure and classmates’ behaviors to guide them. “Online settings require a lot of self-regulation, and we know novice learners don’t have a lot of that,” he says. “Peer influence is a huge deal in terms of learning, and there’s a lot of socially shared regulation happening in classrooms.”

The lack of social connections during the pandemic is significant, says Rimm-Kaufman. “One of the things that this shift has underscored is how much personal relationships matter for kids, including relationships with other students and with teachers.”

Feeling connected to a teacher can make a big difference in educational outcomes. The quality of teacher-student relationships has a significant effect on student engagement and, to a slightly lesser degree, on student achievement, according to a meta-analysis of 99 studies (Roorda, D.L., et al., Review of Educational Research , Vol. 81, No. 4, 2011). The influence of those relationships was particularly important for students from disadvantaged backgrounds and those with learning difficulties.

But meaningful teacher relationships may be harder to develop over the internet, says Fives. “So much of the motivation in a classroom comes from those quick interactions students have with teachers in the moment,” she says. “In a remote-learning setting, kids often have to wait for that feedback.”

What’s more, digital interactions can be highly taxing, Kaplan says. In person, teachers and students learn a lot from the mood of the classroom and subtle body language. In a video, it’s harder to discern those details. “Online, much of that information is missing, so our brains try to fill in the gaps. And that takes working memory,” Kaplan says. “At the same time, students might see their own image, which can raise their self-consciousness and is an added burden while trying to focus on learning.”

Learning new technology has also presented a challenge, Fives adds. “It’s not just writing an essay. It’s figuring out how to post it to the platform, how to log in to get the feedback from the teacher,” she says. Older students might have to learn different platforms for different classes, she adds. “Every teacher might be using different tools, and that puts a heavy cognitive load on students.”

Learning losses and teacher burnout

Given so many hurdles—known and unknown—educators will have to be flexible as the new academic year begins, Foster says. “It will be an atypical year, and there will absolutely be a lot of catching up.”

An important next step will be to figure out how best to assess students’ knowledge as they start the new year, Rimm-Kaufman says. “Some kids will come in having lost months of instruction, so educators will have to make broader assessments than they usually would, and find ways to adjust their instruction accordingly.”

That is a daunting task, though not an insurmountable one, says Francesca López, PhD, an educational psychologist and the Waterbury Chair of Secondary Education at Penn State University’s College of Education. “Teachers do remarkable work, and I don’t believe for a second this generation of students won’t catch up,” she says. “But we can’t allow everything to rest on teachers. Policies must change to ensure equity.”

In the short term, López adds, educators will have to attend to students’ emotional well-being to help them learn. Millions of families have experienced unemployment and financial hardship, and many children have lost loved ones to COVID-19. “This is a traumatic event, and we need to prioritize mental health,” López says. “We can’t focus on academics without considering the whole child.” (See companion article, “ Safeguarding Student Mental Health ”.)

Teacher mental health, too, is a top priority, experts say. At the end of March, Marc Brackett, PhD, founder of the Yale Center for Emotional Intelligence at Yale University, and colleagues surveyed more than 5,000 U.S. teachers, asking them to list the most frequent emotions they felt each day. The top three: anxiety, fear and worry. “We found [educators] are more anxious than ever before, and they’re struggling to manage their anxiety,” Brackett says. “The uncertainty and unpredictability about what the future of school will be is taking a toll on their wellness.”

Teachers aren’t just learning new platforms. They’re also worrying about student well-being more than ever before and having to figure out how to reach out to them from their own homes. Plus, says Rimm-Kaufman, “many schools emphasize teacher collaboration, and those efforts are strained when teachers aren’t in the same building with one another.” It’s unsurprising that many teachers experienced stress, burnout and self-doubt as they taught in such unprecedented circumstances in the spring, Fives adds. “Many really good teachers don’t feel like good teachers anymore. Their identity as a teacher is affected, and their self-efficacy is crashing.”

Investing and innovating

Administrators face an uphill battle as they find ways to support teachers and get students back on track. School budgets are vulnerable to shrinking state revenues due to the pandemic, and some school districts have already laid off employees. In May, school superintendents from 62 cities sent a letter to Congress asking for new federal education assistance. “Significant revenue shortfalls are looming for local school districts that will exacerbate the disruption students have already faced,” the letter warned.

Still, some experts are hopeful that this experience could be the shake-up that schools needed to improve education for all children. Educational disparities will be hard to ignore in the wake of the pandemic, Kaplan says. “Crises often sharpen our gaze and reveal aspects of our lives that were masked or ignored. This highlights the need for prioritizing equity at the policy level.”

“We’re shifting into the unknown,” López says. “Educational psychology has a robust history of learning theories. As this unfolds, we need to look to the research to see what we can learn, and how we can incorporate it into high-quality education.”

Further reading

Improving School Improvement Adelman, H., & Taylor, L., Center for Mental Health in Schools & Student/Learning Supports at UCLA

Low-Income Children and Coronavirus Disease 2019 (COVID-19) in the U.S. Dooley, D.G., et al., JAMA Pediatrics , 2020

School Reopening—The Pandemic Issue That Is Not Getting Its Due Christakis, D.A., JAMA Pediatrics , 2020

Impact of Online Learning in K–12: Effectiveness, Challenges, and Limitations for Online Instruction Ward-Jackson, J., & Yu, C., In Handbook of Research on Blended Learning Pedagogies and Professional Development in Higher Education , IGI Global, 2019

Recommended Reading

Online inequities

How many children weren’t engaging with remote learning (logging in once a week or less)?

• 3.7% of children in families making more than $100,000 per year
• 38% of children in families making less than $25,000 per year

Source: ParentsTogether

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Mini review article, distance learning in higher education during covid-19.

• 1 Department of Pedagogy of Higher Education, Kazan (Volga Region) Federal University, Kazan, Russia
• 2 Department of Jurisprudence, Bauman Moscow State Technical University, Moscow, Russia
• 3 Department of English for Professional Communication, Financial University under the Government of the Russian Federation, Moscow, Russia
• 4 Department of Foreign Languages, RUDN University, Moscow, Russia
• 5 Department of Medical and Social Assessment, Emergency, and Ambulatory Therapy, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

COVID-19’s pandemic has hastened the expansion of online learning across all levels of education. Countries have pushed to expand their use of distant education and make it mandatory in view of the danger of being unable to resume face-to-face education. The most frequently reported disadvantages are technological challenges and the resulting inability to open the system. Prior to the pandemic, interest in distance learning was burgeoning, as it was a unique style of instruction. The mini-review aims to ascertain students’ attitudes about distant learning during COVID-19. To accomplish the objective, articles were retrieved from the ERIC database. We utilize the search phrases “Distance learning” AND “University” AND “COVID.” We compiled a list of 139 articles. We chose papers with “full text” and “peer reviewed only” sections. Following the exclusion, 58 articles persisted. Then, using content analysis, publications relating to students’ perspectives on distance learning were identified. There were 27 articles in the final list. Students’ perspectives on distant education are classified into four categories: perception and attitudes, advantages of distance learning, disadvantages of distance learning, and challenges for distance learning. In all studies, due of pandemic constraints, online data gathering methods were selected. Surveys and questionnaires were utilized as data collection tools. When students are asked to compare face-to-face and online learning techniques, they assert that online learning has the potential to compensate for any limitations caused by pandemic conditions. Students’ perspectives and degrees of satisfaction range widely, from good to negative. Distance learning is advantageous since it allows for learning at any time and from any location. Distance education benefits both accomplishment and learning. Staying at home is safer and less stressful for students during pandemics. Distance education contributes to a variety of physical and psychological health concerns, including fear, anxiety, stress, and attention problems. Many schools lack enough infrastructure as a result of the pandemic’s rapid transition to online schooling. Future researchers can study what kind of online education methods could be used to eliminate student concerns.

Introduction

The pandemic of COVID-19 has accelerated the spread of online learning at all stages of education, from kindergarten to higher education. Prior to the epidemic, several colleges offered online education. However, as a result of the epidemic, several governments discontinued face-to-face schooling in favor of compulsory distance education.

The COVID-19 problem had a detrimental effect on the world’s educational system. As a result, educational institutions around the world developed a new technique for delivering instructional programs ( Graham et al., 2020 ; Akhmadieva et al., 2021 ; Gaba et al., 2021 ; Insorio and Macandog, 2022 ; Tal et al., 2022 ). Distance education has been the sole choice in the majority of countries throughout this period, and these countries have sought to increase their use of distance education and make it mandatory in light of the risk of not being able to restart face-to-face schooling ( Falode et al., 2020 ; Gonçalves et al., 2020 ; Tugun et al., 2020 ; Altun et al., 2021 ; Valeeva and Kalimullin, 2021 ; Zagkos et al., 2022 ).

What Is Distance Learning

Britannica defines distance learning as “form of education in which the main elements include physical separation of teachers and students during instruction and the use of various technologies to facilitate student-teacher and student-student communication” ( Simonson and Berg, 2016 ). The subject of distant learning has been studied extensively in the fields of pedagogics and psychology for quite some time ( Palatovska et al., 2021 ).

The primary distinction is that early in the history of distant education, the majority of interactions between professors and students were asynchronous. With the advent of the Internet, synchronous work prospects expanded to include anything from chat rooms to videoconferencing services. Additionally, asynchronous material exchange was substantially relocated to digital settings and communication channels ( Virtič et al., 2021 ).

Distance learning is a fundamentally different way to communication as well as a different learning framework. An instructor may not meet with pupils in live broadcasts at all in distance learning, but merely follow them in a chat if required ( Bozkurt and Sharma, 2020 ). Audio podcasts, films, numerous simulators, and online quizzes are just a few of the technological tools available for distance learning. The major aspect of distance learning, on the other hand, is the detailed tracking of a student’s performance, which helps to develop his or her own trajectory. While online learning attempts to replicate classroom learning methods, distant learning employs a computer game format, with new levels available only after the previous ones have been completed ( Bakhov et al., 2021 ).

In recent years, increased attention has been placed on eLearning in educational institutions because to the numerous benefits that have been discovered via study. These advantages include the absence of physical and temporal limits, the ease of accessing material and scheduling flexibility, as well as the cost-effectiveness of the solution. A number of other studies have demonstrated that eLearning is beneficial to both student gains and student performance. However, in order to achieve the optimum results from eLearning, students must be actively participating in the learning process — a notion that is commonly referred to as active learning — throughout the whole process ( Aldossary, 2021 ; Altun et al., 2021 ).

The most commonly mentioned negatives include technological difficulties and the inability to open the system as a result, low teaching quality, inability to teach applicable disciplines, and a lack of courses, contact, communication, and internet ( Altun et al., 2021 ). Also, misuse of technology, adaptation of successful technology-based training to effective teaching methods, and bad practices in managing the assessment and evaluation process of learning are all downsides of distance learning ( Debeş, 2021 ).

Distance Learning in a Pandemic Context

The epidemic forced schools, colleges, and institutions throughout the world to close their doors so that students might practice social isolation ( Toquero, 2020 ). Prior to the pandemic, demand for distance learning was nascent, as it was a novel mode of education, the benefits and quality of which were difficult to judge due to a dearth of statistics. But, in 2020, humanity faced a coronavirus pandemic, which accelerated the shift to distant learning to the point that it became the only viable mode of education and communication ( Viktoria and Aida, 2020 ). Due to the advancements in digital technology, educators and lecturers have been obliged to use E-learning platforms ( Benadla and Hadji, 2021 ).

In remote education settings for higher education, activities are often divided into synchronous course sessions and asynchronous activities and tasks. In synchronous courses, learners participate in interactive and targeted experiences that help them develop a fundamental grasp of technology-enhanced education, course design, and successful online instruction. Asynchronous activities and tasks, on the other hand, include tests, group work assignments, group discussion, feedback, and projects. Additionally, asynchronous activities and tasks are carried out via interactive video-based activities, facilitator meetings, live webinars, and keynote speakers ( Debeş, 2021 ).

According to Lamanauskas and Makarskaitė-Petkevičienė (2021) , ICT should be attractive for learners. Additionally, student satisfaction with ODL has a statistically significant effect on their future choices for online learning ( Virtič et al., 2021 ). According to Avsheniuk et al. (2021) , the majority of research is undertaken to categorize students’ views and attitudes about online learning, and studies examining students’ perspectives of online learning during the COVID-19 epidemic are uncommon and few. There is presently a dearth of research on the impact on students when schools are forced to close abruptly and indefinitely and transition to online learning communities ( Unger and Meiran, 2020 ). So that, the mini-review is aimed to examining the students’ views on using distance learning during COVID-19.

In order to perform the aim, the articles were searched through ERIC database. We use “Distance learning” AND “University” AND “COVID” as search terms. We obtained 139 articles. We selected “full text” and “Peer reviewed only” articles. After the exclusion, 58 articles endured. Then content analyses were used to determine articles related to students’ voices about distance learning. In the final list, there were 27 articles ( Table 1 ).

Table 1. Countries and data collection tools.

In the study, a qualitative approach and content analyses were preferred. Firstly, the findings related to students’ attitudes and opinions on distance learning were determined. The research team read selected sections independently. Researchers have come to a consensus on the themes of perception and attitudes, advantages of distance learning, disadvantages of distance learning, and challenges for distance learning. It was decided which study would be included in which theme/s. Finally, the findings were synthesized under themes.

Only 3 studies ( Lassoued et al., 2020 ; Viktoria and Aida, 2020 ; Todri et al., 2021 ) were conducted to cover more than one country. Other studies include only one country. Surveys and questionnaires were mostly used as measurement tools in the study. Due to pandemic restrictions, online data collection approaches were preferred in the data collection process.

Students’ views on distance learning are grouped under four themes. These themes are perception and attitudes, advantages of distance learning, disadvantages of distance learning, and challenges for distance learning.

Perception and Attitudes Toward Distance Learning

Students’ attitudes toward distance learning differ according to the studies. In some studies ( Mathew and Chung, 2020 ; Avsheniuk et al., 2021 ), it is stated that especially the students’ attitudes are positive, while in some studies ( Bozavlı, 2021 ; Yurdal et al., 2021 ) it is clearly stated that their attitudes are negative. In addition, there are also studies ( Akcil and Bastas, 2021 ) that indicate that students’ attitudes are at a moderate level. The transition to distance learning has been a source of anxiety for some students ( Unger and Meiran, 2020 ).

When the students’ satisfaction levels are analyzed, it is obvious from the research ( Gonçalves et al., 2020 ; Avsheniuk et al., 2021 ; Bakhov et al., 2021 ; Glebov et al., 2021 ; Todri et al., 2021 ) that the students’ satisfaction levels are high. In some studies, it is pronounced that the general satisfaction level of the participants is moderate ( Viktoria and Aida, 2020 ; Aldossary, 2021 ; Didenko et al., 2021 ) and low ( Taşkaya, 2021 ).

When students compare face-to-face and online learning methods, they state that online learning has opportunities to compensate for their deficiencies due to the pandemic conditions ( Abrosimova, 2020 ) and but they prefer face-to-face learning ( Gonçalves et al., 2020 ; Kaisar and Chowdhury, 2020 ; Bakhov et al., 2021 ). Distance learning is not sufficiently motivating ( Altun et al., 2021 ; Bozavlı, 2021 ), effective ( Beltekin and Kuyulu, 2020 ; Bozavlı, 2021 ), and does not have a contribution to students’ knowledge ( Taşkaya, 2021 ). Distance education cannot be used in place of face-to-face instruction ( Aldossary, 2021 ; Altun et al., 2021 ).

Advantages of Distance Learning

It is mostly cited advantages that distance learning has a positive effect on achievement and learning ( Gonçalves et al., 2020 ; Lin and Gao, 2020 ; Aldossary, 2021 ; Altun et al., 2021 ; Şahin, 2021 ). In addition, in distance learning, students can have more resources and reuse resources such as re-watching video ( Önöral and Kurtulmus-Yilmaz, 2020 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Martha et al., 2021 ).

Distance learning for the reason any time and everywhere learning ( Adnan and Anwar, 2020 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Todri et al., 2021 ). There is no need to spend money on transportation to and from the institution ( Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Nenakhova, 2021 ). Also, staying at home is safe during pandemics and less stressful for students ( Lamanauskas and Makarskaitė-Petkevičienė, 2021 ).

Challenges and Disadvantages of Distance Learning

Distance learning cannot guarantee effective learning, the persistence of learning, or success ( Altun et al., 2021 ; Benadla and Hadji, 2021 ). Students state that they have more works, tasks, and study loads in the distance learning process ( Mathew and Chung, 2020 ; Bakhov et al., 2021 ; Didenko et al., 2021 ; Nenakhova, 2021 ). Group working and socialization difficulties are experienced in distance learning ( Adnan and Anwar, 2020 ; Bozavlı, 2021 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ). The absence of communication and face-to-face interaction is seen a disadvantage ( Didenko et al., 2021 ; Nenakhova, 2021 ).

It is difficult to keep attention on the computer screen for a long time, so distance-learning negatively affects concentration ( Bakhov et al., 2021 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ). In addition, distance education prompts some physical and psychological health problems ( Kaisar and Chowdhury, 2020 ; Taşkaya, 2021 ).

Devices and internet connection, technical problems are mainly stated as challenges for distance learning ( Abrosimova, 2020 ; Adnan and Anwar, 2020 ; Mathew and Chung, 2020 ; Bakhov et al., 2021 ; Benadla and Hadji, 2021 ; Didenko et al., 2021 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Nenakhova, 2021 ; Taşkaya, 2021 ; Şahin, 2021 ). In addition, some students have difficulties in finding a quiet and suitable environment where they can follow distance education courses ( Taşkaya, 2021 ). It is a disadvantage that students have not the knowledge and skills to use the technological tools used in distance education ( Lassoued et al., 2020 ; Bakhov et al., 2021 ; Didenko et al., 2021 ).

The purpose of this study is to ascertain university students’ perceptions about distant education during COVID-19. The study’s findings are intended to give context for developers of distant curriculum and higher education institutions.

According to Toquero (2020) , academic institutions have an increased need to enhance their curricula, and the incorporation of innovative teaching methods and tactics should be a priority. COVID-19’s lockout has shown the reality of higher education’s current state: Progressive universities operating in the twenty-first century did not appear to be prepared to implement digital teaching and learning tools; existing online learning platforms were not universal solutions; teaching staff were not prepared to teach remotely; their understanding of online teaching was sometimes limited to sending handbooks, slides, sample tasks, and assignments to students via email and setting deadlines for submission of completed tasks ( Didenko et al., 2021 ).

It is a key factor that student satisfaction to identify the influencers that emerged in online higher education settings ( Parahoo et al., 2016 ). Also, there was a significant positive relationship between online learning, social presence and satisfaction with online courses ( Stankovska et al., 2021 ). According to the findings, the attitudes and satisfaction levels of the students differ according to the studies and vary in a wide range from positive to negative attitudes.

According to the study’s findings, students responded that while online learning is beneficial for compensating for deficiencies during the pandemic, they would prefer face-to-face education in the future. This is a significant outcome for institutions. It is not desirable for all students to take their courses entirely online. According to Samat et al. (2020) , the one-size-fits-all approach to ODL implementation is inapplicable since it not only impedes the flow of information delivery inside the virtual classroom, but it also has an impact on psychological well-being because users are prone to become disturbed.

In distance learning, students can have more resources and reuse resources such as re-watching videos. So, distance learning has a positive effect on achievement and learning. Alghamdi (2021) stated that over the last two decades, research on the influence of technology on students’ academic success has revealed a range of good and negative impacts and relationships, as well as zero effects and relationship.

The result also shows that distance education prompts some physical and psychological health problems. Due to the difficulty of maintaining focus on a computer screen for an extended period of time, remote education has a detrimental effect on concentration. There is some evidence that students are fearful of online learning in compared to more traditional, or in-person, in-class learning environments, as well as media representations of emergencies ( Müller-Seitz and Macpherson, 2014 ).

Unsatisfactory equipment and internet connection, technical difficulties, and a lack of expertise about remote learning technology are frequently cited as distance learning issues. Due to the pandemic’s quick move to online education, many schools have an insufficient infrastructure. Infrastructure deficiency is more evident in fields that require laboratory work such as engineering ( Andrzej, 2020 ) and medicine ( Yurdal et al., 2021 ).

Conclusion and Recommendation

To sum up, students’ opinions and levels of satisfaction vary significantly, ranging from positive to negative. Distance learning for the reason any time and everywhere learning. Distance learning has a positive effect on achievement and learning. Staying at home is safe during pandemics and less stressful for students. Distance education prompts some physical and psychological health problems such as fear, anxiety, stress, and losing concentration. Due to the pandemic’s quick move to online education, many schools have an insufficient infrastructure. Future researchers can investigate what distance education models can be that will eliminate the complaints of students. Students’ positive attitudes and levels of satisfaction with their distant education programs have an impact on their ability to profit from the program. Consequently, schools wishing to implement distant education should begin by developing a structure, content, and pedagogical approach that would improve the satisfaction of their students. According to the findings of the study, there is no universally applicable magic formula since student satisfaction differs depending on the country, course content, and external factors.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This manuscript has been supported by the Kazan Federal University Strategic Academic Leadership Program.

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 : ICT, distance learning, COVID-19, higher education, online learning

Citation: Masalimova AR, Khvatova MA, Chikileva LS, Zvyagintseva EP, Stepanova VV and Melnik MV (2022) Distance Learning in Higher Education During Covid-19. Front. Educ. 7:822958. doi: 10.3389/feduc.2022.822958

Received: 26 November 2021; Accepted: 14 February 2022; Published: 03 March 2022.

Reviewed by:

Copyright © 2022 Masalimova, Khvatova, Chikileva, Zvyagintseva, Stepanova and Melnik. 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: Alfiya R. Masalimova, [email protected]

† ORCID: Alfiya R. Masalimova, orcid.org/0000-0003-3711-2527 ; Maria A. Khvatova, orcid.org/0000-0002-2156-8805 ; Lyudmila S. Chikileva, orcid.org/0000-0002-4737-9041 ; Elena P. Zvyagintseva, orcid.org/0000-0001-7078-0805 ; Valentina V. Stepanova, orcid.org/0000-0003-0495-0962 ; Mariya V. Melnik, orcid.org/0000-0001-8800-4628

This article is part of the Research Topic

The State of E-Learning in Higher Education in the Era of the Pandemic: How do we move Forward?

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The impact of online interactive teaching on university students’ deep learning—the perspective of self-determination.

1. Introduction

2. literature review, 2.1. deep learning, 2.2. online interactive teaching, 2.3. self-determination theory, 3. research hypotheses and theoretical model, 3.1. the impact of online interactive teaching and learning on deep learning, 3.2. self-determination theory and deep learning, 3.3. the mediating role of perceived competence and perceived autonomy, 3.4. the mediating role of intrinsic motivation, 3.5. mediation in the technological environment, 4. methodology, 4.1. questionnaire design and participants, 4.2. data collection and analysis, 5.1. descriptive statistics, 5.2. measurement model checking, 5.3. structural modeling, 6. discussion and conclusions, 6.1. discussion, 6.2. theoretical contribution, 6.3. practical implications, 6.4. conclusions, 7. research limitations and future recommendations, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Zhou, Q.; Zhang, H.; Li, F. The Impact of Online Interactive Teaching on University Students’ Deep Learning—The Perspective of Self-Determination. Educ. Sci. 2024 , 14 , 664. https://doi.org/10.3390/educsci14060664

Zhou Q, Zhang H, Li F. The Impact of Online Interactive Teaching on University Students’ Deep Learning—The Perspective of Self-Determination. Education Sciences . 2024; 14(6):664. https://doi.org/10.3390/educsci14060664

Zhou, Qingyi, Hongfeng Zhang, and Fanbo Li. 2024. "The Impact of Online Interactive Teaching on University Students’ Deep Learning—The Perspective of Self-Determination" Education Sciences 14, no. 6: 664. https://doi.org/10.3390/educsci14060664

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Exploring the Nexus of Distance Learning Satisfaction: Perspectives from Accounting Students in Serbian Public Universities During the Pandemic

• Open access
• Published: 18 June 2024

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• Aleksandra Fedajev 1 ,
• Dejan Jovanović 2 ,
• Marina Janković-Perić 3 &
• Magdalena Radulescu   ORCID: orcid.org/0000-0002-4050-8170 4 , 5 , 6  

Amid the COVID-19 pandemic, teachers globally transitioned to distance learning, presenting significant challenges, particularly in developing countries. In that sense, the study investigates the usefulness and satisfaction (US) of distance learning (DL) among accounting students in Serbian public universities during the COVID-19 pandemic. The research focuses on three key factors affecting perceived US derived from existing literature, including teaching process quality (TPQ), technical qualities of distance learning platforms (TQ), and compatibility with social and pandemic conditions (CSPC). The developed theoretical model is predominantly based on the user satisfaction model. Data from 373 surveyed students, gathered through a validated questionnaire, underwent analysis using the partial least squares structural equation model (PLS-SEM). Results reveal that CSPC and TPQ significantly impact the US, whereas TQ has a minor and statistically insignificant effect. The R 2 value indicates these three constructs explain a significant portion of the variance for the US, with moderate effects of CSPC and TPQ and a small effect of TQ, indicated by f 2 values. Moreover, the model is found to be significantly predictive, according to the Q 2 value. Besides theoretical implications, the study suggests important practical implications for university management, emphasizing regular student surveys, continuous improvement of DL systems, and effective and continuous teacher training as the vital measures for enhancing teaching quality. Improving TPQ and TQ also impacts the Serbian economy by fostering workforce development, enhancing digital transformation, stimulating telecommunication industry growth, driving digital information sector development, attracting investment and innovation, boosting global competitiveness, and promoting lifelong learning.

Avoid common mistakes on your manuscript.

Introduction

Educational institutions face significant challenges resulting from the dynamic technical, economic, social, and political trends that have intensified during the last two decades, including globalization, technological advancement, digital transformation, and the widespread adoption of information and communication technologies (ICT). Adapting to these new trends includes enabling the smooth transition from traditional learning to distance learning (DL) that aligns with the new educational paradigm (Arsenijević & Mesaroš-Živkov, 2021 ) and the necessity for the establishment of a knowledge economy (Carayannis et al., 2012 ; Carayannis & Campbell, 2021 ; Carayannis & Rakhmatullin, 2014 ). These trends have particularly impacted higher education as the main generator of professionally qualified and trained staff for the upcoming jobs emerging from Industry 4.0 (Radun, 2020 ). Besides needing to be a specialist in a certain field and possess the job-specific skills (Bolade & Sindakis, 2020 ), high-educated graduates need to possess advanced technical skills, which can be acquired faster in distance than traditional learning.

Besides improved adaptability to new global trends, DL also addresses the issue of higher education accessibility in times of social, political, and health crises. It offers many potential benefits, not the least of which is the ability to get beyond traditional learning settings’ time and space limitations (Bates, 2005 ), evidenced during the COVID pandemic. Like in other countries worldwide, Serbian universities also introduced DL to overcome disruptions in education due to the adoption of anti-pandemic measures. “Lockdown, quarantine, stay home, stay safe” became the new paradigm to reduce the spread of the pandemic (Zhang et al., 2023 ). During the home confinement caused by COVID-19, universities closed and all teaching became virtual, prompting consideration of the question of the sustainability of the education system. (Faura-Martinez et al., 2021 ). A large number of faculties utilized the Moodle platform as a proven method of DL even before the pandemic (Eraković & Lazović, 2020 ) in combination with some web-based video conference tools, including Zoom, Microsoft Teams, and Google Meet.

DL during the pandemic was not a planned and designed procedure in advance, so from the standpoint of researchers in the field of online learning, it would not be accurate to categorize it as an example of distance education. The phrase “emergency remote teaching,” proposed by Hodges et al. ( 2020 ), is probably best for this circumstance since it relates to a temporary solution and alternative education delivery method in an online environment. However, the fact that lessons were delivered online and students were educated in this way is essential because it provided a valuable experience that can help higher education institutions learn from it and better prepare for future similar circumstances. Namely, DL should not be simply abandoned after the pandemic, and professors should not return back to their previous practices of teaching exclusively in the classroom without making sure that the lessons learned from 2020 were not lost for upcoming safety and public health crises (Barbour et al., 2020 ). It can be stated that the COVID-19 pandemic has prompted an investigation into the factors that influence the benefits of DL (Jo, 2023 ) to prepare efficient DL systems for eventual future crises.

In addition, it should be emphasized that today’s generation comprises digital natives (Al-htaybat et al., 2018 ), positioning them at the forefront of emerging technologies. Consequently, DL addresses the requirements of younger generations and presents a significant opportunity for aligning with the expectations of modern students (Rajeh et al., 2021 ). From an economic perspective, adopting advanced DL ensures equitable access to education for everyone. This is achieved by substantially lowering the expenses associated with studying, making it more accessible to individuals residing in smaller towns. In the traditional education model, the expenses related to commuting and lodging in some large cities can pose significant barriers to pursuing education (Stecuła & Wolniak, 2022 ).

Before the pandemic, the application of technology in accounting classes had been sporadic and primarily depended on the lecturers’ individual preferences. The pandemic era has reversed the burden of demonstrating that increased technological integration in education is required, given the state of the market. Earlier, professors advocated for more technology in the classroom did so by convincing management and colleagues of its benefits. Now, professors who prefer not to do so must explain why (Fogarty, 2020 ).

Due to the extensive use of DL before the pandemic, developed economies were better prepared than developing ones (Abbas, 2016 ; Mohammadi, 2015 , Ebner et.al., 2020 ), and research on this issue is more prominent. The scientific literature in developing countries is very modest (Bhuasiri et al., 2012 ; Van Vu et al., 2022 ). Intensifying research in developing countries can add more insight to this crucial subject, providing a rewarding topic for additional study and advancements in practice, which was one of the reasons for selecting the Serbian case for this research. Serbia is considered as one of the developing countries with a perspective educational sector. Serbia has one of the best educational systems in the region, and its education sector contributes to the country’s overall economic progress. It is also worth noting that, despite being a developing country, the level of ICT usage in the country is relatively high. According to data from the Statistical Office of the Republic of Serbia ( 2021 ), 76.7% of households have a computer, and 81.5% have an internet connection, with 91.7% of them having broadband internet. In this sense, it can be said that this country has favourable conditions for the future development of distance learning.

Many universities adopted open-source learning management systems to supplement their teaching efforts. These systems served as additional platforms for instructing, gathering and presenting educational program data and disseminating pertinent information to students. During the COVID-19 pandemic, classes shifted online, allowing instructors to select their preferred platforms, while universities offered guidelines on suitable digital tools. The institution extended technical assistance, recognizing that most educators had to work from home and utilize their personal resources (as reported by Koruga et al., 2023 ).

Teachers had the flexibility to choose assessments, such as quizzes, assignments, and exams, that aligned with the course objectives (along with deadlines to ensure students stayed on track). While most faculties required teachers to conduct online classes according to a predetermined schedule at the beginning of the semester, some faculties allowed teachers to decide whether the course would be delivered in real-time (synchronous) or if students could engage at their own pace (asynchronous). However, in both scenarios, they were required to post learning materials. These materials included presentations, recorded class videos, text-based resources, multimedia content, and case studies designed to address practical problems, all by the subject’s specifications. To enhance the quality of content delivery, most teachers implemented mechanisms for obtaining ongoing student feedback in addition to their regular communication efforts. This feedback allowed for continuous improvements to be made during the course.

The focus on accounting education is provoked by some specificities of teaching this economic discipline. First, teaching accounting subjects assumes intensive interaction between the professor and the students due to their specialized nature compared to other economics subjects. The efficient DL in accounting assumes enabling students to work in small groups, be encouraged to participate in discussions with the assistance of a professor, use the learning-by-doing, complete tasks independently, follow recommendations for group work, collaborate on results interpretation, and use simulation and optimization to foster critical thinking (Gainor et al., 2014 ; Januszewski & Buchalska-Sugajska, 2022 ). Second, accounting has a considerable practical dimension (Ali, 2020 ; Rameez et al., 2020 ). ICT is required to prepare students to adapt to business practice needs and adhere to their professional tasks (Busulwa & Ebans, 2021 ). So, enhanced ICT implementation in accounting curricula is an important competitive advantage of modern economic faculties. Third, several authors (Arbaugh, 2014 ; Arbaugh & Rau, 2007 ) suggested that the effects of DL may differ depending on the academic discipline and whether the course is quantitative or qualitative. So, a more thorough investigation of implications in the accounting field, which features numerous quantitatively oriented courses, seems warranted. Consequently, the specificity of this work compared to previous research is the distinct focus on the consideration of the usefulness of the satisfaction of distance learning in accounting subjects. Especially in the post-pandemic period, research results can be a framework for further improvements in distance learning or digital improvement of traditional learning. Considering that there have been no similar studies in Serbia, especially concerning accounting subjects, this research highlights a completely new dimension of studying accounting disciplines that was not present in Serbia before COVID-19. It emphasizes the advantages, issues, and benefits of DL.

Although there are many studies on DL systems, the contribution of this study is reflected in the advancement of knowledge in the field of DL analysis in the following contexts:

Evaluating the key factors impacting the successful adoption of DL in the Republic of Serbia, which could exemplify other developing countries. Like other developing economies, the Republic of Serbia has a low adoption and usage rate for DL, so fostering DL research can boost its adoption in practice.

Exploring a previously unexplored link related to the impact of the compatibility of DL with social and pandemic conditions on students’ perceived usefulness and satisfaction. This unique exploration highlights the importance of considering broader socio-environmental factors when evaluating DL outcomes.

Emphasizing the importance of DL in accounting education. It highlights the need for graduates to possess advanced technical skills beyond traditional accounting practices. This finding has implications for the evolving role of accountants in the dynamic business environment.

The paper aims to evaluate the impact of the technical qualities of DL platforms, teaching process quality, and compatibility with social and pandemic conditions on students’ perceived satisfaction and usefulness of emergency DL. The remainder of the paper is organized as follows. The next section presents the theoretical background with the development of the hypotheses. The “Data and Methodology” describes the research data and methodology. The fourth section presents the results of the research with appropriate discussion. The final section concludes the paper.

Theoretical Background and Hypothesis Development

Theoretical background.

Prior studies have mostly concentrated on the factors that affect the adoption and use of DL in the absence of extraordinary conditions that could change these practices (Parahoo et al., 2016 ; Al-Rahmi et al., 2019 ; Gómez-Ramirez et al., 2019 ; Yadegaridehkordi et al., 2019 ). Amidst the sudden transition to online learning prompted by the COVID-19 outbreak, the significance and complexity of various factors influencing perceived satisfaction and usefulness have become more pronounced. Numerous research studies have delved into the realm of distance learning in the context of the global health crisis (Ali, 2020 ; Alismaiel, 2021 ; Holzer et al., 2021 ; Ranadewa et al., 2023 ; Simamora, 2020 ; Yandra et. al., 2021 ).

After the pandemic, the knowledge gap has become even wider, given the lack of consensus among researchers about which factors are the most important in such extraordinary conditions. In developing countries, there is an especially wide spectrum of factors. The most commonly indicated are lack of resources, limited accessibility, infrastructure unavailability, communication issues, and the important role of numerous social factors (Adedoyin & Soykan, 2023 ; Hazzam & Wilkins, 2023 ; Islam et al., 2023 ; Venter et al., 2022 ).

The geographical distribution of the research population is quite uneven, with Asian countries being the most analyzed ones (Alismaiel et al., 2022 ; Nordin & Nordin, 2020 ; Chen et al., 2020 ; Bao, 2020 ; Howshigan & Nadesan, 2021 ; Jiang et al., 2021 ; Kornpitack & Sawmong, 2022 ; Selvanathan et al., 2023 ), followed by African counters (Kaisara & Bwalya, 2021 ; Ouajdouni et al., 2022 ; Bossman & Agyei, 2022 ; Ouajdouni et al., 2022 : Ennam, 2024 ), and European countries (Cofini et al., 2022 ; Dragomir & Dumitru, 2023 ; Faura-Martinez et al., 2021 ; Nikou & Maslov, 2023 ; Stecuła & Wolniak, 2022 ). Also, the authors investigated factors that impacted satisfaction, usefulness, and learning outcomes in different areas of studies, like engineering (Pham, 2022 ), hospitality (Tavitiyaman et al., 2021 ), business (Alam et al., 2022 ; Fang et al., 2023 ), and economics (Dragomir & Dumitru, 2023 ). In order to take into account specificities of particular subjects, some papers are devoted to analysis of factors impacting learning on particular subjects and one of them is accounting (Alshurafat et al., 2021 ; Krasodomska et al., 2022 ; Lazim et al., 2021 ; Lux et al., 2023 ; Shabeeb et al., 2022 ; Terblanche et al., 2023 ; Tettamanzi et al., 2023 ; Viviers et al., 2023 ). However, most of these papers consider subjective factors of students’ US, like performance expectancy, effort expectancy, hedonic motivation, habit, attitude, stress, anxiety, student engagement, influence from family and other important people, and self-efficacy. The impact of these factors is usually evaluated using the technology acceptance model (TAM).

The factors considered in this paper are defined based on the user satisfaction model and the findings of Händel et al. ( 2020 ), Adedoyin and Soykan ( 2020 ), Terblanche et al. ( 2023 ), and Lux et al. ( 2023 ), and they are related to TQ, TPQ, and CSPC as external factors. Namely, the unprecedented circumstances of immediate distance teaching and learning witnessed during this crisis underscore the need to investigate how TQ, TPQ, and CSPC impact students’ perceived US in this unique setting. Furthermore, there is a lack of substantial empirical evidence in existing literature to substantiate these links in accounting education in developing countries, particularly within the context of a global pandemic. Establishing this connection empirically can aid educational decision-makers in preparing for potential future disruptions and shed light on strategies to enhance student satisfaction and usefulness, thereby maximizing the benefits of DL during such challenging times.

The mentioned papers dealing with students’ perception of DL employed a variety of methodologies, like paired t -tests and independent t -tests, correlation measures, principal component analysis (PCA), qualitative analysis of interview data, confirmatory factor analysis (CFA), back propagation (BP) neural network model, covariance-based structural equation model (CB-SEM), and partial least squares structural equation model (PLS-SEM).

The PLS-SEM method is the most commonly used methodology in this area, and the following justifications support the use of PLS-SEM in this research. First, the research model exhibits a relatively complex structure featuring several observable latent variables. Second, it is thought that the structural relationships within the model are still in the initial phases of theoretical development or expansion in these new pandemic conditions. Third, this research took advantage of the methodology’s benefits, which included fewer stringent requirements and less restrictive assumptions, that facilitated the creation and estimation of the model without imposing additional constraints (Hair et al., 2019 ; Hair et al., 2023 ; Richter et al., 2012 ; Sarstedt et al., 2021 ).

Hypothesis Development

Technical qualities of the dl platform.

The technical quality DL platform encompasses its ability to facilitate diverse learning methods, ensure reliable access to online resources, enable accurate self-assessment via assessment tools, and suit the educational context, notably in accounting education. Several studies have examined these attributes concerning perceived US. Shehzadi et al. ( 2021 ) found that high-quality ICT, e-services, and e-information enhance students’ satisfaction with e-learning. So, the satisfaction of students with e-learning and, consequently, the improvement of learning outcomes can be elevated by the quality of interaction and the system’s reliability, including platforms for video meetings (Favale et al., 2020 ).

Al-Fraihat et al. ( 2020 ) and Fogarty ( 2020 ) echo findings that the quality of technical systems, information, and support drives perceived usefulness. Fogarty notes that today’s students, accustomed to constant internet use, question the delayed implementation of online learning. DeLone and McLean’s ( 2003 ) model identifies two technical elements: information quality, regarding content accuracy and accessibility, and system quality, focusing on performance and user-friendliness. Positive correlations between information quality, perceived satisfaction, and usefulness are observed by Seddon and Kiew ( 1994 ), Seddon ( 1997 ), Eom et al. ( 2012 ), Lwoga ( 2012 ), and Alotaibi and Alshahrani ( 2022 ), reinforcing the importance of robust technical infrastructure in enhancing online learning experiences.

Several authors, including Seddon and Kiew ( 1994 ), Seddon ( 1997 ), Po-An Hsieh and Wang ( 2007 ), Liaw ( 2008 ), and Hassanzadeh et al. ( 2012 ), have observed a significant positive correlation between system quality and perceived US in their empirical studies. Alotaibi and Alshahrani ( 2022 ) conducted a comprehensive analysis of the impact of both information and system quality on perceived usefulness and satisfaction. They found that a well-structured learning platform with updated content positively influences student satisfaction by enabling efficient task completion. However, they noted that information quality does not affect students’ perceived usefulness, possibly because DL platform usage was mandatory during the pandemic, providing students with alternative avenues, such as direct teacher contact, for precise information. Moreover, they concluded that system quality did not affect perceived usefulness due to the compulsory use of DL platforms, implying that despite quality variations, students benefitted from their usage. According to previous research results, the following is hypothesized:

H1. There is a statistically significant positive relationship between the technical qualities of the learning platform and students’ usefulness and satisfaction.

Teaching Process Quality

Teaching process quality in distance learning (DL) encompasses various factors like fostering student communication, effective utilization of ICT by teachers, thorough preparation, easy communication, and enhancing student learning experiences. Interactions between students and teachers, attitudes towards online learning, and course design positively influence perceived satisfaction and usefulness (Barbour et al., 2020 ; Cidral et al., 2018 ; Hossain et al., 2019 ; Liaw & Huang, 2013 ; Shin & Cheon, 2019 ; Wei & Chou, 2020 ). Human resources are pivotal for educational organizations, as teacher productivity relies heavily on their educational and pedagogical capacities. It is crucial during emergencies when creating online lessons is more time-consuming than traditional ones (Almazova et al., 2020 ; Omebe, 2014 ). Educators tended to be unprepared for online delivery, and students had reduced access to digital technology and stable and reliable internet. This likely affected low socioeconomic and vulnerable student populations the most (Crawford & Cifuentes-Faura, 2022 ). This underscores the importance of well-prepared educators and effective communication channels in DL settings, emphasizing the human aspect of facilitating effective online education.

Koruga et al. ( 2023 ) and Kyerewaa et al. ( 2021 ) emphasized that educators in higher education faced time constraints during the initial stages of the COVID-19 crisis, necessitating creative approaches to online teaching challenges. Prior research stressed the importance of educators focusing on pedagogical aspects of technology, prioritizing effective interaction and communication skills in the online realm (Kyerewaa et al., 2021 ). The findings underscored the need for educators to enhance pedagogical skills related to technology rather than solely focusing on technical proficiency. Teachers relied on past online teaching experiences to adapt to new working conditions, addressing disparities in digital resource access (Laudari et al., 2021 ). Success in online education requires educators to actively navigate and understand digital spaces, utilizing various learning tools and platforms effectively.

Careful planning of DL classes involves determining the content and fostering interactions pivotal for student satisfaction and usefulness. Educators in distance education must digitize traditional materials (Tere et al., 2020 ) and engage in digital communication, notably in providing feedback on student interactions (Yengin et al., 2010 ). Effective teachers enhance student satisfaction and encourage using electronic learning platforms (Cidral et al., 2018 ; Lwoga, 2012 ). Their efforts in planning and facilitating DL classes play a crucial role in student engagement and the overall success of the learning process.

Recognizing learning as a social and cognitive activity, not just a question of passing across information (Barbour et al., 2020 ), Liaw and Huang ( 2013 ) revealed a positive and significant link between interactive learning environments and perceived US. Sun et al. ( 2008 ) found a positive and statistically significant relationship between educator quality (timely responses and positive attitudes toward e-learning) and satisfaction. According to the presented theoretical background, the following hypothesis is defined:

H2. There is a statistically significant positive relationship between the teaching process quality and students’ usefulness and satisfaction.

Compatibility with Social and Pandemic Conditions

The pandemic brought significant stress to university students, affecting their academic, personal, and relational lives, as well as raising concerns about disease transmission (Zurlo et al., 2020 ). Lockdown measures shifted interactions to online platforms, leading to feelings of boredom, anxiety, and frustration (Aristovnik et al., 2020 ; Elmer et al., 2020 ). Contagion anxiety heightened stress levels among students (Brooks et al., 2020 ), and personal risk perception influenced responses and mental health (Capone et al., 2020 ). Students with higher risk perception levels may view DL as a useful tool to mitigate contagion risk, impacting learning outcomes and satisfaction (Baber, 2020 ). Studies suggest DL environments can be adaptable, sociable, and personalized (González-Gómez et al., 2016 ; Westermann, 2014 ), offering flexibility and work-life balance (Herrador-Alcaide et al., 2019 ). These factors significantly shape students’ perceptions of the US amidst the pandemic’s challenges.

DL, crucial for student safety during COVID-19 (Saxena et al., 2021 ), addresses the potential cabin fever from extended home stays due to government regulations. Often emotionally sensitive and aware of virus risks, college students tend to adhere to social distancing guidelines (Jo, 2022 ). In a university context where face-to-face interactions are challenging, students increasingly recognize the significance of DL for both safety and continued education. Namely, students who prioritize social distancing benefit more from distance learning (DL), influenced by their perceived risk and behavioral intentions (Bae & Chang, 2021 ). Those who view COVID-19 as a serious threat are more likely to adhere to distancing measures. Cabin fever, common among students in quarantine, may exacerbate discomfort and perceptions of COVID-19 risks, impacting attitudes toward social distancing and DL engagement (Jo, 2023 ). Heightened risk perception influences students’ readiness to embrace DL amidst lockdown measures and social isolation.

Recent studies (Behzadnia & FatahModares, 2020 ; Cantarero et al., 2021 ; Martela & Sheldon, 2019 ) highlight a positive connection between student satisfaction and well-being. Learners’ mental well-being during COVID-19 is crucial for coping and focusing on learning (Ranadewa et al., 2023 ). Factors influencing DL include a lack of social support and isolation and impact satisfaction (Ghaderizefreh & Hoover, 2018 ). Student emotions, including anger, anxiety, and enthusiasm, further affect satisfaction and perceived DL usefulness. The asynchronous nature of DL, allowing flexibility in a pandemic with family responsibilities, technological constraints, and limited access, contributes to its utility. Interviews conducted by Zhao et al. ( 2021 ) suggest increased satisfaction may stem from a comfortable environment aligning with students’ habits, emphasizing the role of motivation and habits in the relationship between the learning environment, physical conditions, and satisfaction. Alismaiel et al. ( 2022 ) point up that the utilization of social media for collaborative learning and student engagement has a direct and positive impact on US.

Considering all factors, it can be asserted that DL ensures equal access for both students and instructors. Its flexibility allows participation from any location and at convenient times, reducing the workload on educators during the COVID-19 era. Educators need not rearrange schedules or provide extra hours, as resources are easily accessible through cloud-based systems like Google Drive (Basir et al., 2023 ). This adaptability contributes to a more accessible and efficient learning experience for both students and instructors. Thus, the research of previous literature proposed the following hypothesis:

H3. There is a statistically significant positive relationship between the compatibility with social and pandemic conditions and students’ usefulness and satisfaction.

The established hypotheses are tested by applying the PLS-SEM methodology to the appropriate data collected by surveying.

Data and Methodology

Research design.

The research utilized a quantitative study method to test and analyze the factors influencing students’ perceived (US) toward (DL) application in accounting during the pandemic. Four variables were considered in this study, comprising the independent variables TQ, TPQ, and CSPC, along with the dependent variable US. The data collected from the questionnaires were processed using the PLS-SEM methodology. The theoretical model served as the foundation for its application.

The PLS-SEM testing yields two models: the outer and inner models. The outer model, also known as the measurement model, assesses the reliability and validity of the indicators associated with variable constructs. Once the reliability and validity tests are successfully completed, the subsequent phase involves the structural analysis, commonly known as the inner model analysis. The inner model represents the structural relationships between constructs, illustrating how they influence each other and serving as the platform for testing hypotheses related to these relationships.

Sampling and Data Collection

The data for the analysis was collected from primary sources via online research (Google Forms) over 6 months, from November 2020 to May 2021. The target group was students from state economics faculties who enrolled or intended to enrol in the accounting, auditing and business finance modules. In line with the research objective, a quantitative research technique was used. Students were selected through stratified random sampling to ensure equal representation of students in terms of gender, age, and year of study. Only students actively engaged in DL during the COVID-19 pandemic were invited to participate in the research.

Additionally, the authors guided their choice of sampling technique based on similar studies conducted worldwide (Alshurafat et al., 2021 ; Binyamin et al., 2020 ; De, 2020 ; Herrador-Alcaide et al., 2019 ), as well as studies mentioned in the literature review of previous research. It is important to emphasize that this research has an advantage because it was carried out during a period of certain adaptation to the pandemic circumstances (at the beginning of the second and during the third semesters of the pandemic), which is important in such studies (Milheim, 2012 ). The advantage lies in the fact that students’ perceptions and impressions of adjusting to the new scenario have subsided, and the number of students who have taken part in DL has increased, so the findings are more constant and, thus, more reliable.

The initial questionnaire was designed according to the review of questionnaires from similar studies (Al-Fraihat et al., 2020 ; Cidral et al., 2018 ; Herrador-Alcaide et al., 2019 ), and it contained 45 questions. Since the COVID-19 situation was novel, authors modified already-existing items or created new ones to address the actual circumstances appropriately. The first seven questions focused on the general characteristics of the respondents. The remaining questions were divided into four groups: technological characteristics of the learning platform, teaching process quality, compatibility with social and pandemic conditions, and usefulness and satisfaction. A five-point Likert scale was employed to evaluate students’ opinions about presented statements (ranging from 1, meaning completely disagree, to 5, meaning completely agree). The initial questionnaire was piloted with a small group of students to ensure the reliability and validity of the considered items. After the pilot survey, the final questionnaire was defined by excluding questions that negatively affected the constructs’ reliability and validity. The 22 questions are retained for the empirical research (presented in Appendix in Table 7).

The questionnaire was sent to students through learning platforms. The total number of students who filled out the questionnaire was 373, which gives a response rate of 38.02%, bearing in mind that the number of students registered on learning platforms when conducting the research was 981. The stated research rate is fully in line with previously conducted research in this area, listed in the review of previous research. Table 1 presents the demographic information of the study sample (373 respondents).

The descriptive statistic from Table 1 shows that most of the surveyed respondents are University of Kragujevac students (33.8%), and 71.6% are female. As expected, most are aged 19–25 (85.8%), and 87.9% are at the bachelor level of studies. As the most commonly used device for DL, the laptop was used by 61.4% of them, 39.1% of respondents used the Moodle platform, and only 19.6% had experience with DL before the pandemic. At this point, it is important to emphasize that most students who had prior experience with DL before the pandemic primarily utilized the Moodle platform, which had been introduced at certain faculties before the pandemic. Using Moodle alongside traditional classroom learning before the pandemic likely enhanced students’ digital literacy by fostering familiarity with online platforms and developing specific digital skills. However, the differences in digital literacy among students were relatively low, considering that Moodle is used in the vast majority of universities in Serbia.

Theoretical Model Development

The DeLone and McLean information systems success model, the technology acceptance model (TAM), the user satisfaction model, and the e-learning quality models are the four most commonly used types of models for evaluating the success of DL used in previous literature (Sun et al., 2008 ; Ozkan & Koseler, 2009 ; Alismaiel et al., 2022). The theoretical model used in this study (Figure 1 ) is mainly based on the user satisfaction model. This approach suggests that satisfaction is a major indicator of information systems’ success, effectiveness, utilization, and acceptance (Harter & Hert, 1997 ; Seddon, 1997 ; Thong & Yap, 1996 ). The current model also incorporates the DeLone and McLean information systems success model’s constructs of system quality (divided into technical characteristics of the learning platform and the teaching process quality), perceived usefulness, and satisfaction. Technical characteristics include elements like system availability, the usability of system features, and system reliability, while teaching process quality incorporates elements like interactivity and communication components and diversity of learning styles. Besides satisfaction, this model also incorporates the concept of usefulness, which is taken from the TAM model. In Seddon’s ( 1997 ) model, usefulness was operationalized as a factor influencing user satisfaction and viewed as a generic perceptual indicator of user benefits. As the positive correlations between satisfaction and usefulness are supported empirically by the existing literature (Arbaugh, 2000 ; Limayem & Cheung, 2008 ; Seddon, 1997 ), they are used as a joint construct in this model. In this study, students’ perceived usefulness and satisfaction encompass the extent to which DL is perceived by students as facilitating more effective learning compared to traditional methods, enabling quick problem-solving and knowledge acquisition, supporting adequate exam preparation, being an attractive educational format in contrast to traditional methods, advocating for its permanent integration into the accounting education process, and generating satisfaction with the achieved results in accounting exams when utilizing DL. Similar to Sun et al.’s ( 2008 ) model, the environmental component is introduced to represent social, health, and psychological issues during the pandemic, and it is named compatibility with social and pandemic conditions.

Theoretical model

Methodology

PLS-SEM is a highly utilized approach in multivariate data analysis in social sciences. It is the preferred methodology, especially in cases where the study subject lacks a well-established theoretical foundation, particularly when there is a limited prior understanding of causal relationships (Tilahun et al., 2023 ). PLS-SEM operates by maximizing the explained variance of the endogenous latent variables, making it especially suitable for exploratory and predictive studies (Manley et al., 2021 ). Among the most commonly cited advantages of PLS-SEM methodology are also facts that this methodology enables overcoming small population constraints with models that include a large number of items and constructs permitting the use of non-normal data (Anand et al., 2023 ; Pramudita et al., 2023 ; Ramli et al., 2019 ; Yesuf et al., 2023 ). In this research, this methodology is implemented through the SmartPLS 4 software.

Results and Discussion

The procedure established by Hair Jr et al. ( 2016 ) and Sarstedt and Cheah ( 2019 ) is applied to access measurement and structural models.

Measurement Model Assessment

The convergent validity and construct reliability of the measurement model are accessed by item loadings (with appropriate p values), Cronbach’s alpha (CA), composite reliability (CR), and average variance extracted (AVE), which are presented in Table 2 (Hair Jr et al., 2016 ).

Item loadings must be greater than 0.6, indicating that structures have incorporated more than 50% of the variance (Benitez et al., 2020 ; Teng et al., 2021 ). The results in Table 2 indicate that the loadings for the items were significantly higher than the recommended 0.6 value and statistically significant ( p ≤ 0.01). CA and CR values also indicate pleasant internal consistency since they exceed the recommended value of 0.7 for all constructs (Nannally, 1978 ). Additionally, convergent validity is confirmed by AVE values higher than the suggested threshold of 0.5 (Hair Jr et al., 2016 ).

Besides convergent validity, the discriminant validity should also be checked before assessing the structural model. The discriminant validity is assessed by the heterotrait-monotrait ratio of correlations (HTMT) ratio (Table 3 ).

Table 3 indicates that all HTMT ratio values are lower than the suggested threshold of 0.9 (Franke & Sarstedt, 2019 ; Henseler et al., 2015 ), confirming the existence of discriminant validity of the model.

Structural Model Assessment

Since the validity and reliability of the constructs are empirically verified, the structural model can be evaluated. Variance inflation factor ( VIF ), coefficient of determination ( R 2 ), effect size ( f 2 ), and predictive relevance ( Q 2 ) are used to assess the structural model, as they are the most commonly used indicators in the previous literature (Alami & Idrissi, 2022 ; Mustofa et al., 2022 ). The VIF values for considered items are presented in Table 4 .

A VIF value greater than 5 suggests a potential collinearity issue. The derived VIF values from Table 4 are all within the acceptable threshold values ( VIF < 5). Thus, collinearity was not an issue with these data.

The next step in structural model assessment is the evaluation of path coefficients, obtained via the bootstrapping procedure in SmartPLS 4 software (5000 bootstrap samples were generated) and presented in Table 5 .

The results from Table 5 suggest that all path coefficients are positive, as established hypotheses suggested. However, the path coefficient indicating the relationship between the TQ of the learning platform and the US is low (0.022) and statistically insignificant ( p -value = 0.626), suggesting that hypothesis H1 should be rejected. This finding contradicts extensive literature emphasizing technical aspects in enhancing online learning experiences (Al-Fraihat et al., 2020 ; Alotaibi & Alshahrani, 2022 ; Delone & McLean, 2003 ; Eom et al., 2012 ; Favale et al., 2020 ; Fogarty, 2020 ; Lwoga, 2012 ; Seddon, 1997 ; Seddon & Kiew, 1994 ; Shehzadi et al., 2021 ). While the TQ encompasses features like diverse learning methods and reliable access, the study’s findings suggest these may not strongly influence the US. Despite prior research emphasizing the positive correlation between system quality and perceived usefulness, the current study highlights discrepancies (Hassanzadeh et al., 2012 ; Liaw, 2008 ; Po-An Hsieh & Wang, 2007 ).

Such a result can be explained by the fact that the students were obliged to use DL platforms since there was no other option. So, they used and benefited from it regardless of its qualities. Also, the DL platform may not be necessary for students to obtain reliable information. They can contact the teacher if they require more specific information (Alotaibi & Alshahrani, 2022 ). Additionally, it should be said that the reason for this result could be that a larger number of students were already familiar with the Moodle platform as an asynchronous platform, which improved their digital literacy to some extent.

Furthermore, these generations spend a lot of time using computers and smartphones, so using new technologies does not affect their satisfaction. Hence, in the digital transformation era, education must evolve to match the digital landscape, with high-quality digital platforms supporting DL and preparing students for the demands of the digital workforce, which is crucial for Serbia’s competitiveness. Serbia should bolster its competitiveness by investing in education and embracing digital advancements, signalling a commitment to innovation and a skilled labour pool. Such a strategy supports lifelong learning, recognizing the necessity for continuous skill development amid a swiftly evolving job market.

The path coefficients for the remaining links are statistically significant, so H2 and H3 are supported. Both of them range from 0.2 to 0.5, which is considered moderate, according to Cohen ( 1988 ). The path coefficient representing the link between TPQ and US is higher, amounting to 0.474, indicating the highest positive impact of this factor on students’ US among considered factors. Such a result aligns with insights from the literature review. TPQ, encompassing factors such as communication, effective use of ICT, preparation, and interaction, has been consistently associated with perceived US (Barbour et al., 2020 ; Cidral et al., 2018 ; Hossain et al., 2019 ; Liaw & Huang, 2013 ; Shin & Cheon, 2019 ; Wei & Chou, 2020 ). The significance of well-prepared educators and effective communication channels in DL, particularly during emergencies like the COVID-19 crisis, is underscored (Almazova et al., 2020 ; Omebe, 2014 ). The research results are partially consistent with the findings of Alismaiel et al. ( 2022 ), which indicate that both collaborative learning and engagement positively impacted peer and instructor interaction; both factors influenced online learning during the COVID-19 pandemic, leading to improved student satisfaction and academic performance. The findings emphasize the human aspect in facilitating effective online education, aligning with the notion that educators, facing time constraints and challenges during crises, must prioritize pedagogical aspects of technology, focusing on interaction and communication skills (Koruga et al., 2023 ; Kyerewaa et al., 2021 ).

A slightly lower path coefficient (0.409) is obtained for the link between CSPC and US. However, the positive and statistically significant path coefficient obtained for this link is corroborated by the literature review, highlighting the pandemic’s multifaceted impacts on university students. The COVID-19 pandemic significantly impacted university students, evidenced by heightened stress, anxiety, and risk perception (Aristovnik et al., 2020 ; Brooks et al., 2020 ; Capone et al., 2020 ; Elmer et al., 2020 ; Zurlo et al., 2020 ). This prompted a shift towards online platforms like distance learning (DL), known for their adaptability and personalized nature (González-Gómez et al., 2016 ; Herrador-Alcaide et al., 2019 ; Westermann, 2014 ). DL became crucial for safety and continued education amidst challenges with face-to-face interactions (Bae & Chang, 2021 ; Jo, 2022 ; Jo, 2023 ). Martela and Sheldon ( 2019 ), Behzadnia and FatahModares ( 2020 ), Cantarero et al. ( 2021 ), and Ranadewa et al. ( 2023 ) also emphasized the positive link between student satisfaction, well-being, and DL engagement, supporting mental health during the pandemic. Also, it points up that online learning during the COVID-19 pandemic mediates the relationship between interactivity and student satisfaction (Alismaiel et al. 2022 ).

Finally, the obtained R 2 , f 2 , and Q 2 evaluation should supplement the previous analysis. R 2 has been used to determine the explained variance of the latent dependent variables in relation to the overall variance. The cutoff R 2 values suggested by Chin ( 2010 ) are as follows: 0.190 weak, 0.333 moderate, and 0.670 substantial. The R 2 value of 0.681 indicates that the three evaluated constructs explained a substantial percentage (68.1%) of e-learning usefulness and satisfaction. The factors’ effect size ( f 2 ) should be evaluated considering that values from 0.02 to 0.149 are considered small, from 0.15 to 0.35 is considered medium, and higher than 0.35 is considered large, according to Cohen ( 1988 ). Considering these thresholds, it can be concluded that the relationship between the technical qualities of the learning platform and usefulness and satisfaction has a small effect size (0.01), while a medium effect size is recorded for the remaining two relationships. A larger effect size is obtained for the link between TPQ and US (0.277) than for the link between CSPC and US (0.260). The reason for the medium impact of the TPQ may be that teachers used different platforms for different durations and in different ways to deliver lessons. On the other hand, the strength of the CSPC influence can be clarified by the fact that the research itself was conducted in a period when students were already mostly used to the teaching being conducted online. Digital technologies have been present in the Serbian education system for some years, but the COVID-19 pandemic has intensified the pace of education digitalization and highlighted its potential, possibilities, and risks.

The predictive relevance is another aspect of the structural model that should be evaluated ( Q 2 ). According to Fornell and Cha ( 1994 ), there is predictive relevance if the cross-validated redundancy value of the construct is greater than 0. The Q 2 value for usefulness and satisfaction is 0.672, indicating that the model is sufficiently predictive. To assess the model’s robustness, we employed the non-linearity criteria for robustness validation, as suggested by Sarstedt et al. ( 2020 ). This approach is commonly adopted by other researchers, including Ghasemy et al. ( 2021 ), Yusfiarto et al. ( 2022 ), Werimon et al. ( 2023 ), and Li et al. ( 2023 ). It is important to recognize that although theory often assumes a linear relationship between constructs, empirical observations reveal that linear relationships may not always hold true. From a statistical perspective, when the relationship between two constructs is nonlinear, the effect’s magnitude does not depend only on the exogenous construct’s change but also on its specific value (Hair et al., 2019 ). Consequently, this study incorporates a polynomial model by introducing a quadratic effect.

The outcomes of the quadratic effect, as presented in Table 6 , indicate that none of the path coefficients representing quadratic effects reach statistical significance. Therefore, we can infer that the lack of significance in these interactions supports the robustness of the linear effect, per the findings of Sarstedt et al. ( 2020 ).

The COVID pandemic forced teachers to leave their classrooms and give lectures from home via DL platforms, which was especially challenging for developing countries because it was about emergency distance education applications. In that sense, this research assessed the factors affecting Serbian students’ perceived usefulness and satisfaction with accounting DL during the pandemic. Based on the findings, the TPQ and CSPC had a medium and statistically significant impact on the US, while the impact of technical qualities was rather low and statistically insignificant. This research contributes to the existing literature in several ways. It addresses a critical gap by examining the perceptions of DL among accounting students in Serbian public universities during the COVID-19 pandemic, particularly focusing on the contexts of developing countries. By investigating the US of DL, the study sheds light on the challenges and opportunities students and educators face in transitioning to remote learning environments. This comprehensive approach helps understand the multifaceted nature of DL experiences during the pandemic.

Nevertheless, it can be concluded that faculties in Serbia have mostly readily organized classes in extraordinary conditions. However, the pandemic revealed the importance of skilled and trained teachers for organizing classes in this mode, especially during public health and safety crises when additional support and effort are required. It should be highlighted that just a few higher education institutions in Serbia have applied for accreditation of DL programs and courses, indicating that there is more than enough space for their implementation in the future.

Hopefully, the COVID-19 pandemic will soon be a memory of the past. When it is, universities should not just return to our pre-pandemic teaching and learning practices, abandoning distance instruction. There will most likely be future public health and safety crises. Facilities have been closed in recent years in response to natural disasters such as wildfires, storms, and earthquakes. As a result, teaching in a DL system — in both emergencies and more planned scenarios — must become part of a teacher’s set of qualifications and experience. Learning in such an environment is particularly important for accounting graduates whose spectra of skills are continuously increasing due to dynamic changes in the business environment. They must possess the advanced technical skills necessary to meet the requirements of future employers.

The shifting role of accountants has long been considered to be one of the primary causes of the changes in tertiary accounting education. They should evolve from their historical role as scorekeepers of past business changes to that of designers of the vital management data for directing the organization’s future in a dynamic environment. The ability to work in accounting software alone, which is used to track economic events and produce required reports, is insufficient. The ability to use the software for simulations, optimization, and further advanced analysis is required. Hence, university professors must select the best teaching approaches to generate accounting graduates with appropriate skills.

Theoretical Implications

The theoretical implications of this research are numerous. This first theoretical contribution is the development of a multi-dimensional, comprehensive model for measuring the performance of DL. The model was created after a thorough assessment of the literature, and it is mainly based on the user satisfaction model, which was not sufficiently exploited in previous literature. Second, a previously unexplored link (between compatibility with social and pandemic conditions and perceived usefulness and satisfaction) was examined. Third, this paper is one of the rare papers on DL effectiveness that indicates the greater importance of teaching process quality over the technical qualities of the learning platform. Last but not least, this paper contributes to the modest body of literature on DL effectiveness in developing countries (especially in this part of the world) and accounting education.

Managerial or Policy Implication

The research findings shed light on crucial challenges and recommendations that should be considered to improve perceived US and better utilize the benefits of DL. The study offers practitioners in this area the following practical contributions. First, the study’ findings emphasize the importance of surveying students regularly. As a result, these systems must be continuously improved to solve any issues or limitations. Second, according to the study’s findings, perceived usefulness and satisfaction are substantially affected by the quality of the teaching process. As a result, effective, extensive, and continuous teacher training for using the DL system is a critical success factor. It will help teachers obtain a better understanding and confidence in utilizing the DL system and raise their awareness of all the features of this learning system. Third, universities should motivate teachers to increase the number of online accounting courses and raise students’ awareness about the benefits of attending online courses regularly rather than only in emergencies.

The importance of improving TPQ and TQ, and generally DL development, can be viewed in the context of their broader impact on Serbia’s real economy, telecommunication industry, and digital information sector. There are several ways to articulate this significance. A well-educated workforce is a cornerstone of economic growth, with improved education quality yielding a more skilled and knowledgeable labour force vital for various industries, reducing unemployment rates, and fostering economic stability. The telecommunication industry is pivotal in delivering DL services, prompting increased demand for reliable internet connectivity and telecommunications services as online education surges. A robust digital information sector hinges on a skilled workforce and solid digital infrastructure, with enhancements in TPQ and TQ fuelling sector growth by producing graduates proficient in digital tools. Commitment to high-quality education, including DL, attracts investment and innovation, positioning Serbia favourably in the global marketplace and enhancing its global competitiveness.

Limitations and Ideas for Future Research

Despite the highlighted contributions, this research has certain limitations. First, the proposed model explained 68.1% of perceived usefulness and satisfaction, suggesting that other factors not considered in the model account for approximately 31.9% of the variance in DL perceived satisfaction and usefulness. As a result, there is still potential to research and incorporate more factors influencing perceived satisfaction and usefulness. Second, this study was based on the perceptions of students. Different groups of DL stakeholders, most notably teachers, could enrich the research with their diverse perspectives and provide a deeper knowledge of the challenges in DL delivery. Also, differences in students’ age and diploma types should be considered. These limitations provide researchers with the basis for future research. The new factors impacting perceived usefulness and satisfaction should be incorporated into future models. With ICT and DL practices continuously evolving, comprehensive studies analyzing how the DL quality factors change over time may yield further interesting results. Future research should also be focused on teachers’ perceptions as well. Such analysis should be accompanied by comparing the differences between teachers’ and students’ attitudes. Finally, the current database can be employed to analyze the differences in the impact of considered factors on US of students from different age groups and diploma types.

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Fedajev, A., Jovanović, D., Janković-Perić, M. et al. Exploring the Nexus of Distance Learning Satisfaction: Perspectives from Accounting Students in Serbian Public Universities During the Pandemic. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02138-x

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