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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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• Published: 15 June 2022

Distance education strategies to improve learning during the COVID-19 pandemic

Nature Human Behaviour volume  6 ,  pages 913–914 ( 2022 ) Cite this article

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A randomized controlled trial of approximately 4,500 households in Botswana during the COVID-19 pandemic was conducted to investigate the effectiveness of using low-tech learning interventions during school closures. A simple combination of phone tutoring and SMS messages substantially improved learning in primary school children in a cost-effective manner.

The problem

The COVID-19 pandemic placed enormous pressure on education systems worldwide. At the peak of the crisis, school closures forced over 1.6 billion learners out of classrooms, exacerbating a learning crisis that existed before the pandemic 1 . Widespread school closures are not unique to COVID-19 — teacher strikes, summer breaks, earthquakes, viruses such as influenza and Ebola, and extreme weather conditions all result in school closures. The cost of school closures has proven to be substantial, particularly for households of lower socioeconomic status 2 , 3 . Reducing learning loss requires outside-school interventions that can effectively deliver instructions to children. However, little evidence exists on how to implement cost-effective learning interventions during school disruptions that can reach as many families as possible.

The solution

The use of mobile phones provides a potential solution to deliver educational instruction when schooling is disrupted, with the advantage of being widely accessible and cost effective 4 . However, this ‘low-tech’ solution is less commonly used in education relative to ‘high-tech’ approaches that rely on internet-based instruction, despite only 15–60% of households in low- and middle-income countries having internet access. By contrast, it is estimated that 70–90% of households own at least one mobile phone, suggesting that the use of mobile phones has the potential to provide educational instruction in resource-constrained contexts at scale. To examine this possibility, we conducted a randomized controlled trial, with a sample of approximately 4,500 households across Botswana, testing two mobile phone-based methods as low-tech solutions to support parents when educating children during the COVID-19 pandemic. In one treatment arm, SMS messages provided a few basic numeracy ‘problems of the week’; a second treatment arm supplemented these weekly SMS messages with a live 15–20-minute phone call from a teacher to provide a walkthrough of numeracy problems.

We found that SMS messages alone had little effect on household engagement in education and learning. However, a combination of phone calls with SMS interventions resulted in a pronounced improvement, increasing learning by 0.12 standard deviations (Fig. 1 ) — or up to 0.89 standard deviations of learning per US $100 — which represents one of the most cost-effective learning interventions 5 . We further developed remote assessments, as a means to measure learning, and found that targeting instruction on the basis of the results of assessments improved learning gains in certain proficiencies, particularly for place value and fractions (Fig. 1 ). Finally, we found high parental engagement: parents became more confident and accurate in their beliefs about their child’s education. Overall, this study shows that instruction through mobile phones can provide an effective, scalable method for education delivery beyond traditional schooling approaches.

The graph shows the effects (in standard deviations) of multiple learning strategies relative to the control (no intervention) group. ‘Average level’ represents results from the Annual Status of Education Report (ASER) 0 to 4 scale corresponding to no operations (0), addition, subtraction, multiplication and division. ‘Place value’ and ‘fractions’ refer to two types of problem. Each group (such as ‘phone + SMS’) refers to randomized treatment groups pooled across the designated category. ‘Targeted’ refers to children in a subset that received additional targeted instruction on the basis of child-specific learning levels; ‘not targeted’ refers to children within a subgroup that did not receive targeted instruction. © 2022, Angrist, N. et al.

The implications

Our findings have immediate policy relevance as the COVID-19 pandemic continues to disrupt schooling. Even where schools have re-opened, instruction time has often been reduced owing to social distancing measures, such as double-shift systems in which half of the students attend school in the morning and the other half attend in the afternoon.

Providing additional educational instruction out of school is therefore a current priority. More broadly, our findings have implications for the role of simple, low-tech methods to support education during many forms of school disruption, including teacher strikes, summer holidays, public health crises, weather shocks, natural disasters, and in refugee and conflict settings. In moments in which schooling is disrupted, education systems require resilient approaches to continue to provide education.

Despite our trial including a very large sample size, our data are limited to a single context: the COVID-19 pandemic in Botswana. Future research might involve similar trials to assess how well a low-tech learning approach can be adapted across low- and middle-income countries. We are currently engaged in an active research agenda focused on education in emergencies, which includes a multicontext study testing the adaptability and scalability of remote mobile phone education across five countries: India, Kenya, Nepal, the Philippines and Uganda. Finally, it is important to note that our study evaluates only a subset of potential interventions; other low-tech methods of educational instruction, such as radio and TV, require further investigation.

Noam Angrist 1,2

1 University of Oxford, Oxford, UK.

2 Youth Impact, Gaborone, Botswana.

Expert opinion

“This is a timely and carefully executed and analysed study. The authors provide evidence of a promising, innovative, replicable, potentially scalable and cost-effective intervention to address the massive educational challenge posed by the COVID-19 pandemic. It is a valuable contribution to the literature, although it remains unclear whether the observed short-term gains persist or wane further into the future.” Juan E. Saavedra, University of Southern California, Los Angeles, CA, USA.

Behind the paper

We launched this study within a month of school closures in Botswana, providing some of the first experimental evidence on distance education during the COVID-19 pandemic. This rapid response was enabled by the depth and breadth of presence of Youth Impact in Botswana — an evidence-based nongovernmental organization that provides health and education programmes. Youth Impact provides education services to over 20% of primary schools in the country in partnership with the government, and had experience in running more than 20 rapid randomized trials prior to the pandemic. Our study demonstrates the power of real-time, rigorous evidence to identify effective solutions in a moment of enormous uncertainty and need. The results emerged quickly, were policy-relevant and have been followed by efforts in at least 5 countries reaching over 20,000 students, galvanizing a global and growing evidence base on effective approaches to education in emergencies. N.A.

From the editor

“The challenge of mitigating learning loss during the COVID-19 pandemic is crucial, and this paper by Angrist et al. stands out for its efforts to tackle this problem and test an intervention that could potentially be widely implemented.” Aisha Bradshaw, Senior Editor , Nature Human Behaviour .

Angrist, N., Djankov, S., Goldberg, P. K. & Patrinos, H. A. Measuring human capital using global learning data. Nature 592 , 403–408 (2021). This paper reports evidence of low levels of learning and limited educational progress across 164 countries, a phenomenon that the international education community has called ‘the learning crisis’ .

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Aker, J. C., Ksoll, C. & Lybbert, T. J. Can mobile phones improve learning? Evidence from a field experiment in Niger. Amer. Econ. J. Appl. Econ. 4 , 94–120 (2012). This paper demonstrates the potential of education provided via mobile phone for adults in a low-resource setting .

Kremer, M., Brannen, C. & Glennerster, R. The challenge of education and learning in the developing world. Science 340 , 297–300 (2013). This review article summarizes cost-effective and rigorously evaluated interventions in education .

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This is a summary of: Angrist, N., Bergman, P. & Matsheng, M. Experimental evidence on learning using low-tech when school is out. Nat. Hum. Behav . https://doi.org/10.1038/s41562-022-01381-z (2022)

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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.

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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?

Destiny Unbound: A Look at How Far from Home Students Go to College

• Published: 10 May 2024

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• Robert K. Toutkoushian   ORCID: orcid.org/0000-0001-7570-8546 1 ,
• Stephen Mayfield 2 &
• Samantha M. R. Jelks 3  

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One issue that has received little attention is how students factor distance from home into their decisions about college. In this study, we used data from the Education Longitudinal Survey of 2002 (ELS:02) to examine the distances between a student’s home and the colleges to which they applied, and how far from home they enrolled. We focused on how demand- and supply-side factors were related to the distances applied and enrolled. We tested the sensitivity of our findings to alternative ways of measuring the supply of postsecondary education within commuting distance, and identified factors associated with differences between a student’s application and enrollment distances. Finally, we used quantile regression analysis to determine if the associations between demand- and supply-side factors and distances applied and enrolled varied along the distance distributions.

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Data availability

The data used in this study can be obtained through a license with the National Center for Education Statistics.

Although it is a more recent survey, the High School Longitudinal Study of 2009 (HSLS:09) was not used because it only provides information on up to three institutions to which a student applied, and does not contain the student’s ZIP code during high school.

Applications and enrollments from students from the mainland to colleges in Hawaii and Alaska were included.

All sample sizes are rounded per NCES requirements. The data used in all tables have been weighted using the ELS variable F2F1WT to account for the complex sampling design used in the survey.

The student ZIP code was collected in the 2004 ELS first Follow-up Survey. We also used this ZIP code to infer the students’ county, which is in turn aggregated up to the commuting zone level. The ZIP codes for colleges were gathered from the 2004 IPEDS file. If no college ZIP code existed in IPEDS 2004, we used the ZIP codes as reported in IPEDS surveys for other years.

The straight-line distances were derived using Stata’s Geodist command. We acknowledge that there are other ways to measure distance, including the commuting distance between two points in miles and time. The computation of these alternate measures would require connecting the computer with the restricted-use data to the internet, which is prohibited by the licensing agreement with IES.

The clusters of counties for each commuting zone were created by the USDA Economic Research Service using “journey to work” data from the 2000 US Census.

Median family income was generated as a weighted average of the counties that comprise each commuting zone (i.e., median household income times the number of households in county). The unemployment rate per commuting zone was an average for the years 2004–2006.

We also estimated all of the models using standard errors clustered at the state level and found no appreciable differences in the significance levels for the variables.

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Acknowledgements

We are grateful to the Spencer Foundation for financial support for this project (grant #201900227). An earlier version of this paper was presented at the annual meeting of the Southern Economic Association. We would like to thank participants at the Southern Economic Association conference and Steve DesJardins for comments on an earlier draft of this study.

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Toutkoushian, R.K., Mayfield, S. & Jelks, S.M.R. Destiny Unbound: A Look at How Far from Home Students Go to College. Res High Educ (2024). https://doi.org/10.1007/s11162-024-09790-x

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Students’ perceptions on distance education: A multinational study

• Patricia Fidalgo 1 ,
• Joan Thormann 2 ,
• Oleksandr Kulyk 3 &
• José Alberto Lencastre 4  

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Many universities offer Distance Education (DE) courses and programs to address the diverse educational needs of students and to stay current with advancing technology. Some Institutions of Higher Education (IHE) that do not offer DE find it difficult to navigate through the steps that are needed to provide such courses and programs. Investigating learners’ perceptions, attitudes and willingness to try DE can provide guidance and recommendations for IHEs that are considering expanding use of DE formats. A survey was distributed to undergraduate students in Portugal, UAE and Ukraine. The results of this pilot study showed that in all three countries, students’ major concerns about such programs were time management, motivation, and English language skills. Although students were somewhat apprehensive many indicated they were interested in taking DE courses. Six recommendations informed by interpretation of students’ responses and the literature, are offered to assist institutions who want to offer DE as part of their educational strategy.

Introduction

The World Wide Web has made information access and distribution of educational content available to a large fraction of the world’s population and helped to move Distance Education (DE) to the digital era. DE has become increasingly common in many universities worldwide (Allen & Seaman, 2017 ). Nonetheless, there are still many universities that do not provide this opportunity because it is not part of their institutional culture. As DE becomes more prevalent, countries and Institutions of Higher Education (IHE) that do not provide DE courses will need to look at this option to retain and expand their student population. (Keegan, 1994 ; Nakamura, 2017 ).

In order to develop such programs, it is useful to determine if students are receptive to taking such online courses and are prepared to do so. This study addresses students’ perceptions and their interest in DE. In addition, it provides a comparative analysis across three countries whose IHEs do not have extensive offerings in DE. The results of this research provide some strategies to encourage and support students to take DE courses.

Literature review

A seminal article by Keegan ( 1980 ) presents key aspects of DE. Some of the elements are: physical separation of teacher and learner, learning occurs in the context of an educational institution, technical media are used, teacher and learner communicate, face to face meetings are possible, and an industrial model of providing education is used. More recently varying definitions of DE seem to be based on the perspective of various educators and to reflect the educational culture of each country and IHE. However, some common descriptors seem to be accepted by most stakeholders in the field. Distance education is an educational experience where instructors and learners are separated in time and space (Keegan, 2002 ) which means it can happen away from an academic institution and can lead to a degree or credential (Gunawardena, McIsaac, & Jonassen, 2008 ).

Although there are different types of DE, this research focuses on online learning. The following types of online learning will be investigated: synchronous, asynchronous, blended, massive online open courses (MOOC), and open schedule online courses. In synchronous instruction, teachers and learners meet (usually online) for a session at a predetermined time. According to Watts ( 2016 ) live streaming video and/or audio are used for synchronous interaction. Although videoconferencing allows participants to see each other this is not considered a face-to-face interaction because of the physical separation (Keegan, 1980 ).

Asynchronous instruction means that teachers and learners do not have synchronous sessions and that students have access to course content through the Internet at any time they want or need. Communication among the participants occurs mainly through email and online forums and is typically moderated by the instructor (Watts, 2016 ). According to Garrison ( 2000 ) “Asynchronous collaborative learning may well be the defining technology of the postindustrial era of distance education.” (p.12) Yet another type of DE is blended learning (BL). Garrison and Kanuka ( 2004 ) define BL as combining face-to-face classroom time with online learning experiences. Although it is not clear as to how much time is allocated to online in the blended model “the real test of blended learning is the effective integration of the two main components (face-to-face and Internet technology) such that we are not just adding on to the existing dominant approach or method.” (p.97) In the BL format different teaching strategies and instructional technology can be used to help individuals who have different learning styles, needs and interests (Tseng & Walsh Jr., 2016 ).

Another type of DE is MOOCs (Massive Online Open Courses). This format was first introduced in 2006 and offers distributed open online courses that are available without cost to a very large number of participants (Cormier, McAuley, Siemens, & Stewart, 2010 ). MOOCs origins can be traced to the Open Access Initiative in 2002 which advocates sharing knowledge freely through the Internet. By providing educational opportunities MOOCs can address the increasing demand for training and education (Zawacki-Richter & Naidu, 2016 ). Finally, in open schedule online courses students work asynchronously with all the materials being provided digitally. Although there are deadlines for submitting assignments, students working at their own pace have some independence as to when they do their coursework (Campus Explorer, 2019 ).

There are advantages and disadvantages in taking DE courses. Some of the advantages are self-paced study, time and space flexibility, time saving (no commute between home and school) and the fact that a distance learning course often costs less. Disadvantages include a sense of isolation, the struggle with staying motivated, lack of face-to-face interaction, difficulty in getting immediate feedback, the need for constant and reliable access to technology, and occasionally some difficulty with accreditation (De Paepe, Zhu, & Depryck, 2018 ; Lei & Gupta, 2010 ; Venter, 2003 ; Zuhairi, Wahyono, & Suratinah, 2006 ).

Most of the literature concerning student perception of DE courses, both blended and entirely online, involves students who have enrolled in online courses. Some articles address comparisons of perceptions between face-to-face and online students regarding DE (Daniels & Feather, 2002 ; Dobbs, del Carmen, & Waid-Lindberg, 2017 ; Hannay & Newvine, 2006 ; Lanier, 2006 ). Additional studies address adult and undergraduate students and cover many aspects of the online experience (Dobbs et al., 2017 ; Horspool & Lange, 2012 ; Seok, DaCosta, Kinsell, & Tung, 2010b , a ). However, little, if any research has been conducted that only addresses perceptions of students who live in countries in which few IHEs offer online courses.

In a study comparing online and face-to-face learning, Horspool and Lange ( 2012 ) found that students chose to take online courses to avoid travel time to class and scheduling problems. A majority of both face-to-face and online students did not experience technological issues. Both groups also found that communication with the instructor was adequate. Online students indicated that instructor response time to questions was prompt. By contrast online students perceived peer communication as occurring much less often. Course satisfaction was comparable for both formats (Horspool & Lange, 2012 ). Responses to another survey concerning online and traditional course formats found that students’ reasons for taking online courses included flexibility to accommodate work and family schedules, the ability to avoid commuting to the university and more online courses being available to them (Dobbs et al., 2017 ). Both online and traditional students agreed that traditional courses were easier, and they learned more in that format. They also concurred that online courses required more effort. Experienced online students indicated that the quality of their courses was good while traditional students who had never taken an online course felt that the quality of online courses was lower.

There is additional research that focuses on students including those enrolled in community colleges, MOOCs, blended learning as well as adult learners. Community college students’ and instructors’ perceptions of effectiveness of online courses were compared by Seok et al. ( 2010b , a ). The researchers focused on pedagogical characteristics (management, Universal Design for Learning, interaction, teaching design and content) and technical features (interface, navigation and support). In addition, responses were examined based on various aspects of the subjects’ demographics. Two surveys with 99 items were distributed electronically. One survey was for instructors and the other for students. In general, instructors and students indicated that teaching and learning online was effective. Female students responded more positively to most questions concerning effectiveness, and instructors also found it more positive (Seok et al., 2010b , a ).

Students who enrolled in a MOOC were motivated to take other courses in this format based on their perception that it was useful for achieving their goals. In addition, their motivation was high if the course was posted on a platform that was easy to use (Aharony & Bar-Ilan, 2016 ). This study also found that as students proceeded through the course, they gained confidence.

Blended learning was examined by Kurt and Yildirim ( 2018 ) to determine student satisfaction and what they considered to be important features of the blended format. The results indicated that the Turkish students who participated, almost unanimously felt that BL was beneficial and that their own role and the instructors’ role was central to their satisfaction. The authors stated, “the prominent components in the process have been identified as face-to-face lessons, the features of online course materials, LMS used, design-specific activities, process-based measurement and evaluation, student-student interaction and out-of-class sharing respectively.” (p. 439) DE has a growth potential and offers the opportunity to reach many people (Fidalgo, 2012 ), hence it can be used as a technique for mass education (Perraton, 2008 ). According to Perraton ( 2008 ) DE can be adapted to the needs of current and previous generations who did not complete their education. DE can also reach individuals who live in remote locations and do not have the means to attend school.

Methodology

Study goals.

The goal of this pilot study is to examine what undergraduate students’ perceptions are concerning DE and their willingness to enroll in this type of course. This study focuses on three countries that do not offer extensive DE accredited programs. By comparing three countries with similar DE profiles, commonalties and differences that are relevant and useful can be found. When the IHEs from these countries decide or have the conditions to move towards DE, the results of this study may help them adapt this format to their particular context and students’ needs. Results may also help IHEs plan their strategy for offering online courses to current and future students and attract prospective students who otherwise would not be able to enroll in the face-to-face courses that are available.

Research questions

Have undergraduate students taken an online course previously?

What are undergraduate students’ perceptions of distance education?

What are the reasons for undergraduate students to enroll/not enroll is distance education courses?

What preparation do undergraduate students feel they need to have before taking distance education courses?

What is the undergraduate students’ receptivity towards enrolling in distance education courses?

What types of distance education would undergraduate students be interested in taking?

This research was conducted at IHEs in three countries (Portugal, Ukraine and UAE). A description of each country’s sociodemographic and technological use provides a context for this study.

Portugal, a country located at the western end of the European continent, has a resident population of just over 10 million people (Instituto Nacional de Estatistica, 2019 ). Data collected by Instituto Nacional de Estatistica in 2019 indicated that almost 81% of households in Portugal had Internet access at home. According to the Portuguese National Statistical Institute ( 2019 ), the rate of Internet use by the adult population is about 76%. Among this population, people who attend or have completed secondary and higher education have a higher percentage of Internet use (98%) (Instituto Nacional de Estatistica, 2019 ).

The most used devices to access the Internet are smartphones and laptops. Regarding computer tasks, the most frequent ones are copying and moving files and folders and transferring files from the computer to other devices (PORDATA - Base de Dados Portugal Contemporâneo, 2017 ).

Among Internet users, 80% use social networks, which is a higher percentage than the European Union (EU) average. Mobile Internet access (outside the home and workplace and on portable devices) is 84% and maintains a strong growth trend (Instituto Nacional de Estatistica, 2019 ).

Ukraine is one of the post-soviet countries located in Eastern Europe and it strives to be integrated in economic and political structures of the EU. The current population of the country is 42 million. Despite the low incomes of many Ukrainians, modern technological devices are widespread among the population. The State Statistics Service of Ukraine ( 2019 ) reported that there were 26 million Internet subscribers in the country in the beginning of 2019. However, Ukrainians do not have a high level of digital literacy yet. According to the Digital Transformation Ministry of Ukraine (Communications Department of the Secretariat of the CMU, 2019 ), almost 38% of Ukrainian people aged from 18 to 70 have poor skills in computer literacy and 15.1% of the citizens have no computer skills.

According to the survey conducted by the Digital Transformation Ministry of Ukraine (The Cabinet of Ministers of Ukraine, 2019 ) 27.5% Ukrainian families have a tablet, and 30.6% have one smart phone, 26.4% have two smart phones, 16.5% have three smart phones and 10.8% have four and more smart phones. As for laptops, 42.7% Ukrainian families have a laptop and 45.6% have a desktop computer (The Cabinet of Ministers of Ukraine, 2019 ). The data from the ministry did not indicate if families have multiple devices, however the data shows that technological devices are widespread.

The United Arab Emirates (UAE) is a country located in the Persian Gulf that borders with Oman and Saudi Arabia. The UAE has a population of 9.77 million and is one of the richest countries in the world based on gross domestic product (GDP) per capita. The resident population consists of 11,5% Emiratis and the remaining residents are expats from countries such as India, Pakistan, Philippines, Egypt and others (Global Media Insight, 2020 ).

Regarding technology use, 91% of the residents use mobile Internetand over 98% of the households have Internet access (Knoema, 2018 ). Mobile devices such as smartphones are used to access the Internet mainly at home or at work (Federal Competitiveness and Statistics Authority, 2017 ).

In 2017 the most frequent Internet activities were: sending/receiving emails (61%), posting information or instant messaging (55%), getting information about goods or services (45%), reading or downloading online newspapers, magazines or electronic books (41%) and telephoning over the Internet/VOIP (33%). Downloading movies, images, music, watching TV or video, or listening to radio or music is also a frequent activity performed by 27% of the Internet users followed by Internet banking (25%) and purchasing or ordering good and services (22%) (Federal Competitiveness and Statistics Authority, 2017 ).

While these three countries were selected due to the location of the researchers and thus provided convenience samples, the three countries have a similar lack of DE offerings. Online surveys were emailed to students enrolled in a variety of undergraduate face-to-face courses during the fall semester of 2018. The students in Portugal and the UAE were enrolled in a teacher education program and the survey was emailed to two course sections in Portugal (73 students) and four course sections in the UAE (108 students). At the IHE in Ukraine, students were majoring in applied mathematics, philology, diagnostics, social work and philosophy, and surveys were emailed to 102 students who were enrolled in five course sections. In Portugal and Ukraine, the URL for the online survey was emailed by the instructor of all the course sections. In the UAE the instructor who emailed the URL for the survey taught two of the course sections. The students in the other two sections knew this instructor from taking courses with her previously. The students participating in this study were a convenience sample based on the disciplines taught by the researchers.

Data collection

An online survey with 10 closed questions about undergraduate students’ perception and receptivity towards enrolling in DE courses was developed by the researchers. Ary, Jacobs, Sorensen, and Walker ( 2010 ) compared traditional methods (i.e. face-to-face, paper and pencil) with web-based surveys and found the latter to be are more effective for gathering data from many participants. The questions designed by the researchers were informed by their experience/practice as well as in-depth literature review. The survey was created to respond to the research questions that guided this study. Response choices to the multiple-choice questions were based on issues and concerns related to DE. Students’ responses were collected towards the end of the first semester of the 2018/19 academic year.

The survey was developed to address research questions that assess undergraduate students’ perceptions of DE and students’ receptivity towards enrolling in DE courses (c.f. Appendix ). The use of surveys allows researchers to “obtain information about thoughts, feelings, attitudes, beliefs, values, perceptions, personality and behavioral intentions of research participants.” (Johnson & Christensen, 2014 , p. 192) The survey questions included multiple response formats: Likert scale, select more than one response and multiple choice. Surveys for Portugal were presented in Portuguese. In Ukraine the surveys were translated into Ukrainian. Since English is the language of instruction at the UAE institution, their survey was in English. The URL for the survey was emailed to students by their instructors and was available in an online Google Form. The survey took approximately 10 min to complete. The study consisted of a “self-selected” convenience sample.

Data analysis

Survey results were recorded in Google Forms and an Excel spreadsheet was used to collect students’ responses. Descriptive statistics of the responses to the survey are presented in graphs and tables with percentages of responses displayed. The descriptive statistics provide summaries about the sample’s answers to each of the questions as well as measures of variability (or spread) and central tendency.

Research approval and data management

The research proposal was submitted to the Research and Grants Committee and approved by the Institutional Review Board of the college in the UAE. No personal information (name, College ID number or any other type of information that allows the identification of students) was asked from the students in the surveys. The surveys were anonymous. Only the Principal Investigator (PI) had access to all the data collected. The data will be stored in the PI’s password protected computer for 5 years.

Fifty five of the 73 Portuguese students who received the survey responded and 98 of the 108 UAE students responded. In the Ukraine 102 students were sent surveys and 70 responded. Below are participants’ responses to questions concerning age, gender, as well as level of confidence using the computer and the Internet.

Students’ age range was from 17 to 50 years old. Most students’ age ranges were between 17 and 29 years. Survey responses indicated that 7% of the students in the UAE were male and 93% female, in the Ukraine 43% were male and 57% female and in Portugal 9% male, and 91% female.

Participants were asked about their level of confidence using a computer and the Internet. Results are presented in Table  1 .

The use of participants from three countries allows the study of trends and to determine differences and/or similarities of perceptions about DE. Although the students were enrolled in courses in diverse content areas, they were all undergraduates, almost all under 30 years old, and most were confident using the computer and Internet. These demographic similarities provided a relatively cohesive group for this study while allowing a comparison across countries.

A range of questions were asked about students’ attitudes towards and experience with DE. To determine the participants’ experience with DE two questions were asked.

The data indicates that out of 223 students who responded to the survey, a total of 63 students have taken DE courses. Half of the Ukraine students, about one quarter of the UAE students and only 5% of students in the group from Portugal had taken DE courses (Fig.  1 ). As shown in Fig.  2 , of the students who have had previous experience in DE, most Ukraine students have taken one or two online courses, most UAE students have taken one course and a few Portuguese students have taken one course.

Students that have taken distance education courses

Number of distance education courses taken

More than half of Portuguese students, about two thirds of the Ukraine students and a little over one third of UAE students had a Very favorable or Favorable attitude towards DE. Approximately one third of Portuguese and Ukraine students were Neutral/Unable to judge their attitude. A little less than half of UAE students also indicated this. A small percentage of Portuguese, and one fifth of UAE students indicated their attitude was Very unfavorable or Unfavorable and no Ukraine students reported this (Table 2 ).

More than one third of Portuguese students shared that managing class and study time, saving time by choosing study location and working at their own pace were reasons to enroll in DE. About two thirds of the students from Ukraine reported that working at their own pace and managing their study time were reasons to enroll. A little more than half of these students reported that reasons for enrolling in DE included managing class time, saving time by selecting study location and not having to travel to school as well as having more options for courses or colleges to attend. Almost half of the UAE students had similar reasons for enrolling in a DE courses including managing class and study time, saving time by choosing study location and working at their own pace. In addition, a little more than half of the UAE students also shared that having more options for courses or colleges to attend were reasons to enroll. The reasons that were selected the least by all three groups were that courses were less expensive and enrolling in a preferred program (Tables  3 and 4 ).

Students were given eleven options as to why they would not enroll in DE courses, which are displayed in Tables  5 and 6 . Two reasons that were chosen most often were difficulty staying motivated and preferring face-to-face classes. A small number of Ukraine students reported this as a reason to not enroll in DE courses. Difficulty getting immediate feedback was also a concern for UAE students. Close to one third in the three groups indicated that difficulty contacting the instructor and interacting with peers as well as missing campus life are reasons for not enrolling. About one tenth of Portuguese, one fifth of Ukraine and one fifth of the UAE students reported difficulty getting accreditation as a reason for not enrolling. Not knowing enough about DE was indicated by one tenth of Portuguese, one fifth of Ukraine and one fifth of the UAE students. Only a small number of all the students indicated three categories that are frequently cited in the literature as preventing students from enrolling, these include access to technology, feeling of isolation and too great an expense.

Tables  7 and 8 show student responses to a question regarding the preparation they think they would need before enrolling in a DE course. A little over one tenth of the Portuguese students indicated that they needed better computer equipment, writing skills and a dedicated study space. About one quarter of these students reported they need better skills in the following areas: time management, computer and English language skills, as well as needing to have learning goals and objectives. Having a better Internet connection and the need to develop a study plan was shared by approximately one third of these students. Finally, the highest rated prerequisite for these Portuguese students was to be more motivated.

Few of the Ukraine students felt that they needed better computer equipment or skills, a dedicated study space or a better Internet connection at home. Their concerns focused on their behaviors as students since half or a little more than half felt they needed to be more motivated, have learning objectives and goals, a study plan and better management skills. About one third of these students also reported that they needed better English language skills.

The UAE students were less confident than the Ukraine students about computer skills and needing better equipment and a better Internet connection at home. Almost half of these UAE students reported their need for a study plan and motivation as their most pressing needs. Better management and English language skills were recorded by about one third of the students. One quarter of the UAE students felt they needed better writing skills and a dedicated study space.

Table 9 shows students’ interest in enrolling in DE courses. Almost one quarter of the Ukraine students are Extremely interested in taking DE courses and almost half are Somewhat interested. This contrasts with the students from Portugal who indicated that only 5% are Extremely interested and almost a quarter Somewhat interested. The UAE students’ interest in enrolling fell in between the students from the two other countries. One fifth to almost one third of all three groups were Neutral/Unable to judge. About one tenth of students from Ukraine reported Not being very interested or Not at all interested which contrasts with the Portuguese and UAE students whose numbers were about one half and one quarter respectively.

Tables  10 and 11 show the types of DE that the students were interested in trying. Portuguese students favored Open schedule courses, followed by Blended learning and Synchronous. Few of these students were interested in MOOCs and Asynchronous. More than half of the students from Ukraine were interested in MOOCs and Blended learning followed by Open schedule. About one third of these students were interested in Synchronous and Asynchronous. UAE students most popular formats were Open schedule and Blended learning followed by Synchronous and Asynchronous. There was little interest in MOOCs by the UAE students. Few Portuguese and Ukraine students indicated that they would not take a DE course, however, almost a quarter of the UAE students indicated this.

Data indicates close to a 100% of the UAE residents use the Internet at home or on their mobile devices (Knoema, 2018 ). By contrast a smaller percentage of individuals use the Internet in Portugal and the Ukraine (Infographics, 2019 ). Internet use in each country does not seem to greatly impact UAE students’ opinions regarding DE.

Students’ perceptions of DE vary across the participants from the three countries. Portuguese and Ukrainian students rated DE more favorably than UAE students. Half of the Ukrainian students have experience with DE which might account for their favorable attitude. In contrast, in Portugal only a very small percentage of the students had experience. However, this does not seem to have negatively influenced their attitude towards DE. The interest level and engagement with new technologies by Portuguese students may help explain the favorable perception the participants had toward DE. A study by Costa, Faria, and Neto ( 2018 ) found that 90% of Portuguese students use new technologies and 69% of them use new technologies more than an hour and a half a day. Based on three European studies, Diário de Noticias ( 2011 ) stated that Portuguese students “appear at the forefront of those who best master information and communication technologies (ICT).” (para.1) Another factor influencing respondents might be that currently, and for the first time, the Portuguese government has passed a law that will regulate DE in the country. This new law will open the possibility for other IHEs to provide DE courses that lead to a degree.

Ukrainian students reported a high level of confidence in operating technological devices. The reason for this may be, in part, because of state educational requirements. Since the end of the 1990s, all Ukrainian students in secondary schools have at least one computer course as a mandatory element of their curriculum. This course covers a wide range of issues, which vary from information society theory to applied aspects of computer usage. Among the seven learning goals of this course three address digital literacy (Ministry of Education and Science of Ukraine, 2017 ). Ukrainian students who responded to the survey have taken computer courses for at least 5 years.

In the UAE, most DE courses and programs are not accredited by the Ministry of Education (United Arab Emirates Ministry of Education, 2016 ), which may account for UAE students lack of experience and their inability to judge this type of instruction.

It is worth analyzing the reasons why students enrolled or would enroll in DE courses. The reasons for taking DE courses, such as time management issues, are supported by studies concerning self-regulation and higher retention rates (Bradley, Browne, & Kelley, 2017 ; Peck, Stefaniak, & Shah, 2018 ). Students’ interest in having more control of their study time is also mentioned as one of the primary benefits of DE (Alahmari, 2017 ; Lei & Gupta, 2010 ). Regarding the reasons for not enrolling in DE courses, participants from the three countries mentioned difficulty contacting instructors and peers. Also, more than half of the students in Portugal and the UAE indicated they preferred face-to-face classes. Most students have spent their entire academic lives in traditional classes where interaction and immediate feedback from instructors and peers are more common. These concerns may be why students perceive they would lose a familiar type of interaction and have to engage with classroom participants in a new and different way (Carver & Kosloski Jr., 2015 ; Morris & Clark, 2018 ; Robinson & Hullinger, 2008 ; Summers, Waigandt, & Whittaker, 2005 ). It should be noted that the Portuguese and UAE students were enrolled in teacher education programs and are training to be face-to-face teachers. They may not understand the potential of DE format and are not preparing or expecting to use DE in their professional careers.

Difficulty being motivated was another reason chosen by the participants of the three countries to not enroll in DE courses. The lack of experience in this type of educational format may help explain student lack of confidence with their ability to study and stay on task. This response contrasts with the reasons reported for enrolling in DE courses such as controlling their study time. On one hand, participants like the prospect of having the ability to manage their own time. On the other hand, they are concerned they may lack the discipline they need to be successful.

Although the literature indicates that access to technology, isolation and expense are reasons frequently cited as preventing students from enrolling in DE courses (Lei & Gupta, 2010 ; Venter, 2003 ; Zuhairi et al., 2006 ), these reasons were selected by a very small percentage of the participants of this study. Access and affordability of technology has rapidly increased over the last decade which may help explain this inconsistency. Students may understand that DE courses are now less expensive than traditional university courses (Piletic, 2018 ) and they do not cite this as a reason for not enrolling. Relatively few students indicated they would feel isolated. Since this generation is in constant communication using technology (Diário de Notícias, 2011 ) they may not associate DE learning with isolation. However, it is interesting to note that there was a greater concern for interacting with instructors and peers than isolation.

The Ukrainian students are the most receptive to enrolling in DE courses. This is consistent with their positive perception of this type of learning. In addition, the previous experience of half of the participants may influence their interest as well as encourage their peers’ receptivity. UAE students do not have much experience and fewer than half are open to enrolling in DE courses. This may be due to their lack of experience and other concerns previously mentioned. Only one third of the Portuguese participants indicated their interest in enrolling in DE courses. This is in contrast with almost two thirds saying they had a favorable or very favorable attitude. The reasons for this inconsistency are not evident.

In terms of preparation needed to take DE courses, technical concerns were less of an issue for the participants of all three countries than skills and behaviors. Most participants’ answers focused on student skills including computer, English language and time management. Behaviors such as developing a study plan, having learning goals and objectives and being more motivated were also mentioned. The perceived need for better English language skills was expressed by about one third of the participants, none of whom have English as their native language. English speaking countries have been dominant in DE making English the most commonly used language in online learning (Sadykova & Dautermann, 2009 ). Regarding time management, half of the Ukrainian students expressed their need for improvement in contrast to approximately one third of the participants from the other countries. The difference among responses may be because the Ukrainian students are more self-reflective, or the others are more disciplined. Although both DE and face-to-face courses have deadlines for tasks and assessments, in the face-to-face courses, students meet in person with their instructors who may support and press them to do their work. Lack of in person contact may account for the participants feeling they need to improve these skills when taking DE courses (De Paepe et al., 2018 ). Students expressed concerns about lacking certain skills and having certain behaviors that would lead them to be reluctant to enroll in DE courses. The need for help and preparation are some of the concerns that participants reported. Perceived needs may account for the students’ apprehensions regarding taking DE courses. To promote this type of instruction, IHEs could address students’ concerns (Mahlangu, 2018 ).

Open schedule and blended learning courses were the two preferred formats stated by the participants. The reason that Open schedule is the most popular may be that it provides more freedom than other types of courses. Blended learning offers the familiar face-to-face instruction and some of the conveniences of DE which may be why participants are interested in this model.

Studies regarding the use of MOOCs in all three countries have been conducted indicating that researchers in these locations are aware that this course format is of potential interest to local students (Eppard & Reddy, 2017 ; Gallacher, 2014 ; Gonçalves, Chumbo, Torres, & Gonçalves, 2016 ; Sharov, Liapunova, & Sharova, 2019 ; Strutynska & Umryk, 2016 ). Ukrainian students selected MOOCs much more than students in the other countries. The reason for this may be that these students are more knowledgeable about MOOCs, because this type of course is usually at no cost and/or offered by prestigious IHEs (Cormier et al., 2010 ). However, this study did not ask why students were interested in MOOCs or other types of DE courses.

Limitations and future research

While this study offers useful information regarding undergraduate students’ perception and receptivity in taking DE courses, it has limited generalizability because of the size of the sample and the type of statistical analysis performed. Participants from two of the countries were enrolled in teacher education programs and were primarily female, thus future studies would benefit from including more students in diverse programs and a more equitable gender distribution.

Since many IHEs also offer programs for graduate students it would be useful to survey these students about their opinion and availability to enroll in DE courses. This would provide additional information for IHEs that are interested in developing DE programs.

There were some inconsistencies in the students’ responses such as Portuguese students’ interest in enrolling in DE courses not matching their favorable/ very favorable attitude towards DE. It would be helpful to conduct future research regarding this and other inconsistencies.

A study is currently being planned to collect data that will provide a larger and more diverse sample and include additional IHEs. This future research will potentially increase the available knowledge on how to provide DE for a greater number of students.

Conclusion and recommendations

Further development of DE courses and programs at IHEs in countries such as Portugal, UAE and Ukraine have good prospects. The students’ primary concerns regarding taking DE courses were similar among the three countries. These concerns included time management, motivation, and English language skills. However, this did not totally diminish participants interest in taking online courses especially for the Ukrainian students.

Based on this research, there are some obstacles that can be addressed to support the expansion of DE in the three countries that were studied and in other countries. The following recommendations may assist IHEs in promoting DE.

Recommendations for preparation within IHEs

IHEs can take proactive steps to prepare DE offerings, however, a one-size fit all model may not be appropriate for all countries and IHEs. Each institution needs to develop their own plan that meets the needs of their students and faculty. Data from this pilot study and the literature (Elbaum, McIntyre, & Smith, 2002 ; Hashim & Tasir, 2014 ; Hux et al., 2018 ) suggest that following steps might be taken:

Assess readiness to take DE courses through a survey and have students speak with counselors.

Provide pre-DE courses to build skills and behaviors based on students’ concerns.

Train instructors to develop and deliver DE courses that help to overcome obstacles such as motivation and time management.

Offer courses in a blended learning format to familiarize students with online learning which may provide a transitional model.

Recommendations for IHE outreach

This study shows that there is some student interest in enrolling in online courses. It is not sufficient for IHEs to make changes internally within their own institution. IHEs need to develop external strategies and actions that help advance the development of DE:

Promote DE in social media to target potential students and encourage them to take courses.

Urge government agencies to accredit DE courses and programs.

This pilot study provides some background information that may help IHEs to offer DE courses. Additional research about students’ preferences and needs regarding DE should be conducted. The sample size, IHEs included and participating countries could be expanded in order to gain a greater understanding.

Different cultural characteristics need to be taken into account in the development of online courses and programs. DE is being increasingly included by IHEs all around the world. To stay current, universities will need to find ways to offer DE to their current and prospective students.

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Patricia Fidalgo

Educational Technology Division, Lesley University, Cambridge, MA, USA

Joan Thormann

Philosophy Department, Oles Honchar Dnipro National University, Dnipropetrovs’ka oblast, Ukraine

Oleksandr Kulyk

Department of Curricular Studies and Educational Technology, University of Minho, Braga, Portugal

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Patricia Fidalgo: design of the work, data collection, analysis, interpretation of data, and draft of the work. Joan Thormann: design of the work, analysis, interpretation of data, and draft of the work. Oleksandr Kulyk: data collection, interpretation of data, and draft of the work. José Alberto Lencastre: data collection. The author(s) read and approved the final manuscript.

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Online Survey Questions

1. If the students have taken any distance education courses previously and if yes, how many;

2. What are the students’ perceptions of distance education;

3. What are the reasons students would enroll in distance education courses;

4. What are the reasons students would not enroll in a distance education course;

5. What preparation do students feel they need before taking distance education courses;

6. What is the level of students’ interest towards enrolling in distance education courses;

7. What types of distance education would students be interested in trying;

8. What is the students’ age;

9. What is the students’ gender;

10. How confident do students feel using a computer and the Internet.

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Fidalgo, P., Thormann, J., Kulyk, O. et al. Students’ perceptions on distance education: A multinational study. Int J Educ Technol High Educ 17 , 18 (2020). https://doi.org/10.1186/s41239-020-00194-2

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• http://orcid.org/0000-0002-5721-6051 Yanxian Chen 1 , 2 ,
• Andreas Mueller 3 ,
• http://orcid.org/0000-0002-4548-3574 Ian Morgan 4 ,
• Frank Larkin 5 ,
• http://orcid.org/0000-0002-1257-6635 Yan Wang 6 ,
• Junwen Zeng 7 ,
• Mingguang He 1 , 2 , 7
• 1 School of Optometry , The Hong Kong Polytechnic University , Hong Kong , Hong Kong SAR
• 2 Research Centre for SHARP Vision (RCSV) , The Hong Kong Polytechnic University , Hong Kong , Hong Kong SAR
• 3 Department of Noncommunicable Diseases , World Health Organization , Geneva , Switzerland
• 4 Research School of Biology , Australian National University , Canberra , Australian Capital Territory , Australia
• 5 Cornea and External Disease Department , Moorfields Eye Hospital NHS Foundation Trust , London , UK
• 6 Tianjin Eye Hospital & Eye Institute, Ophthalmology and Visual Development Key Laboratory, Tianjin Medical University , Tianjin , China
• 7 State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University , Guangzhou , Guangdong , China
• Correspondence to Professor Mingguang He, The Hong Kong Polytechnic University, Hong Kong, Hong Kong; mingguang.he{at}polyu.edu.hk

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• Optics and Refraction
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• Epidemiology

In August 2023, BJO and the Zhongshan Ophthalmic Center co-hosted a round table discussion on Best Practices in Myopia Control. This gathering provided a platform for the exchange of insights and discussion of evidence-based strategies to respond to the rapid increase in myopia prevalence. Participants from China, Hong Kong, the UK and Australia met in Guangzhou, China, to discuss the global status and challenges associated with myopia control (see the ‘Acknowledgements’ section for the list of panel members). The event included panel discussions on (1) prevention and public education for myopia control and (2) individualised myopia control. This report summarises the topics in myopia prevention and control discussed.

Prevention and public education for myopia control

Over the past 50 years, there has been a striking increase in the prevalence of myopia, raising questions about its causes and potential future impact. 1 Early data indicate that the advent of the COVID-19 pandemic and associated lockdowns has accelerated the myopia trend. 2 3 It is not clear whether a reduction in the time spent outdoors during lockdowns has led to longer-term behavioural changes favouring indoor lifestyles and hence impacted myopia prevalence.

Reduction of the impact of myopia centres on two crucial elements: education intensity and outdoor activities. Strategies that merely broadcast knowledge about myopia have shown limited effectiveness. Singapore’s efforts, including teacher training and promoting good eye care habits in schools, yielded marginal reductions in myopia prevalence. 5 Though the use of social media, such as WeChat messages, may result in some noteworthy reduction in myopia incidence, 8 9 this effect may be diminished by the effect of parental myopia. Compared with children with myopic parents, online family health education was more effective in children with non-myopic parents. A more compulsive strategy to increase time outdoors may be more effective. The Taiwanese school-focused strategy allocated 2 hours of supervised time, unlike in Singapore, during which children engaged in outdoor activities. It led to a significant decrease in the prevalence of myopia 4 and has generated an L-shaped decline after 10-year promotion of outdoor activities in kindergartens. 10

In China, safety concerns commonly arise in the implementation of outdoor activities during school hours. For instance, from 2016 to 2018 in Shanghai, an additional 40 min of outdoor activity classes was introduced to primary schools, but teachers expressed apprehensions about potential accidents when students were outdoors. However, experiences in Australia and Taiwan have shown that, with necessary safety measures such as teacher supervision and wearing hats, the risks associated with outdoor activities at school are minimal. An alternative solution considered for schools in China is to consolidate short 10 min breaks between classes into longer periods. Based on evidence from Taiwan, a clear implementation strategy led by the government coupled with an adapted education programme is likely to be a successful implementation of outdoor activities during school time.

Individualised myopia control

For children already affected by myopia, the primary focus is on controlling its progression. Myopia is a condition that physically alters the shape of the eye. The concept that every dioptre of myopia matters significantly holds true; even a one-diopre increase comes with a substantial 67% higher risk of myopic macular degeneration. 11 Consequently, reducing myopia by even one dioptre can be significantly beneficial. Myopia control encompasses a range of established interventions, including orthokeratology and low-dose atropine eye-drops. 12 The effect on myopia of atropine is recognised to be concentration-dependent and age-dependent, which itself exemplifies the individualisation of myopia prevention and control. A history of myopia in one or both parents is known to have a significant influence on development of myopia. Is ethnicity important? Comparatively few high-quality trials have been reported outside East Asia. One of the largest such trials from the USA, in which only 11% of children were East Asian, reported no benefit of atropine 0.01% drops in low to moderate myopia compared with placebo. 13

Additional emerging interventions, including defocus incorporated multiple segments spectacle lenses, 14 15 high-add power multifocal contact lenses, 16 spectacles with highly aspherical lenses 17 and repeated low-level red light (RLRL) therapy, 18 19 are showing promise. These diverse interventions offer hope for effective myopia control.

However, it is important to acknowledge that available interventions come with their own advantages and disadvantages. For instance, specially designed spectacles may have limitations related to wearing time and the patient’s age. 14 15 The use of orthokeratology demands comprehensive support from both clinics and parents. RLRL, although effective with a low rate of complications, carries the potential risk of retinal damage that necessitates close monitoring. Thus, success in myopia control lies in individualisation, recognising that different parents have varying requests, capabilities and expectations. For young children or those with highly myopic parents, a more robust intervention may be advisable, such as a higher concentration of atropine drops, a greater magnitude of myopic defocus or red-light therapy.

In regions with limited resources, strategies for myopia prevention and control must be tailored to factors such as affordability and accessibility. While 0.05% atropine has shown the potential to prevent myopia onset by 50%, 20 and RLRL therapy boasts a 54% reduction rate, 21 the cost–benefit analysis for large-scale implementation of these interventions in myopia prevention still requires further research. Spectacle lenses, being relatively more accessible, cost-effective and effective in slowing myopia progression, 15 present an alternative for low-income areas, although their effectiveness in reducing incident myopia also warrants further investigation.

In conclusion, the escalating prevalence of myopia represents a greater challenge than previously anticipated. However, there is recent clear evidence of effective myopia control on a national level in some countries taking proactive measures through interventions and educational reforms. While the effectiveness of myopia control interventions has been extensively explored, the focus must now shift towards individualised strategies in clinical practice to achieve better outcomes. Significant challenges persist, particularly concerning the large-scale implementation of myopia interventions in resource-constrained areas. These challenges remain an essential area for ongoing research and development.

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Acknowledgments

The following panelists participated in the ZOC-BJO Round Table Discussion on 'Best Practices in Myopia Control' (names in alphabetical order). Andreas Mueller, WHO; Chi-ho To, The Hong Kong Polytechnic University; Frank Larkin, Moorfields Eye Hospital (chairman); Ian Morgan, Australian National University, Zhongshan Ophthalmic Center, Sun Yat-sen University

Jason Yam, The Chinese University of Hong Kong; Junwen Zeng, Zhongshan Ophthalmic Center, Sun Yat-sen University; Kathryn Rose, University of Technology Sydney; Mingguang He, The Hong Kong Polytechnic University & Zhongshan Ophthalmic Center, Sun Yat-sen University; Ningli Wang, Beijing Tongren Hospital, Chinese Medical University; Stuart Keel, WHO; Xianggui He, Shanghai Eye Disease Prevention and Treatment Center; Xiangtian Zhou, Eye Hospital, Wenzhou Medical University; Xiao Yang, Zhongshan Ophthalmic Center, Sun Yat-sen University; Yan Wang, Tianjin Eye Hospital. We are grateful to Kangying Lai and Pai Zheng, BMJ China Office for their assistance in organisation of this round table.

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Contributors Drafting of manuscript: YC, JZ and MH. Review and revision of manuscript: AM, IM, FL and YW.

Funding The research was supported by PolyU - Rohto Centre of Research Excellence for Eye Care (Collaborative) (P0046333).

Competing interests MH is the director and shareholder in Eyerising and Eyerising International. The other authors have no proprietary interest in any aspect of this study.

Provenance and peer review Not commissioned; externally peer reviewed.

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