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Managing online learning burnout via investigating the role of loneliness during COVID-19

BMC Psychology volume 13, Article number: 151 (2025) Cite this article

Abstract

Background

Learning burnout, which is a negative state of learning and seriously reduces learning engagement, has become more prevalent in online instruction. Especially during the COVID-19, loneliness during the online learning has attracted a great deal of attention from researchers, who have found that it exacerbates the risk of social media addiction and increase rates of depression. However, the relationship between loneliness and academic burnout in online formal learning has not been well explored in previous studies. Therefore, the present study explores whether and to what extent loneliness suffered by students in online formal learning triggers academic burnout, and whether there are factors that buffer the effects of loneliness on academic burnout, thus filling the gap in the current research.

Methods

Using self-reported data from 618 college students from central China through convenience sampling, this paper used hierarchical linear regression to test the direct effect of loneliness on learning burnout. Three moderation was conducted to reveal the role that 3 interesting factors (academic buoyancy, social presence and online learning duration) play in the relationship between loneliness and online learning burnout.

Results

We found that loneliness triggered burnout in online learning, while social presence relieve burnout. However, academic buoyancy, social presence and online learning duration cannot buffer against the online learning burnout caused by loneliness.

Conclusions

Loneliness in online learning should not be ignored, as it can cause different degrees of learning burnout, no matter whether learners have high or low academic buoyancy and social presence. Future research should continue to investigate how to alleviate loneliness in online learning.

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Introduction

Due to its flexibility and convenience in time and space, online learning is growing rapidly and becoming increasingly popular. However, full online formal learning remains a big challenge (see [92, 93] for review). During the COVID-19 pandemic, more than one billion students were affected by school closures and had to study at home [88]. Great changes have taken place in school education, shifting from the original face-to-face model to full online formal learning [13, 88, 90]. While embracing online learning as a means of continuing academic activities has played a unique role, this rapid change has posed great challenges to both students and teachers [13, 88, 90].

Online formal learning is completely different from the in-person school setting. Teaching content and information are distributed through the internet, which requires students to use technology effectively and to communicate virtually in an isolated environment while resisting distractions [1, 25, 90]. Without direct and immediate help from teachers, students find it hard to keep up with their studies, as well as to stay motivated and engaged in online learning [13, 31, 88]. In addition, a large-scale survey [86] reported the following problems in the implementation of online learning during the COVID-19 pandemic: (1) students experienced difficulties in understanding materials; (2) online lectures were perceived as boring; (3) online learning equipment was insufficient and the connectivity between students and teachers was poor; and (4) students’ enthusiasm for learning decreased. Those difficulties alongside the unique combination of social isolation, prolonged online learning, and employment pressure led to great anxiety and stress, intensified students’ mental health problems [13, 81]. In short, it is extremely challenging to learn online during the pandemic as the rates of students experiencing academic burnout are constantly rising [44].

Learning burnout refers to a negative state where students lose interest in learning, as well as have a detached attitude toward academic activities, and experience a decline in personal accomplishments [28, 66, 80]. Learning burnout not only was found to affect students’ engagement [75], but also was found to lead to depression in learners’ later life [78, 79]. More and more attentions are recently paid to college, students among which could also be affected by burnout [96]. The learning burnout of college students is the negative attitude and behavior of being tired of study due to the pressure of study or lack of interest in study [47, 91]. Thus, it is a necessity to identify the factors that cause learning burnout to better tailor strategies to alleviate it.

Loneliness during the epidemic has attracted a great deal of attention from researchers, who have found that it exacerbates the risk of social media addiction [48] and increase rates of depression [49]. Furthermore, loneliness represents an important issue among young individuals and university students [89] and this finding has been reported across several countries [22, 36, 70]. Recent research suggests an increasing trend in loneliness among university students [36]. However, the relationship between loneliness and academic burnout in online formal learning has not been well explored in previous studies. At the same time, loneliness as a job demand, the physical isolation and lack of face-to-face interaction in online learning can lead to feelings of loneliness among students, depleting their social resources and increasing the risk of learning burnout.

Therefore, based on the JD-R model, the present study explores whether and to what extent loneliness suffered by students in online formal learning triggers academic burnout, and whether there are factors that buffer the effects of loneliness on academic burnout, thus filling the gap in the current research.

Literature review

Online learning burnout and loneliness

Learning burnout is conceptualized as a 3-factor construct that includes emotional exhaustion, cynicism, and inefficiency [28, 57, 66, 67, 80]. Emotional exhaustion is a feeling of overstretching and depleting one’s cognitive, emotional, and physical resources,Cynicism is manifested as a negative, indifferent, and detached attitude toward academic activities; Ineffectiveness entails feelings of incompetence, low achievement, and a lack of accomplishment in the process of studying. In general, learning burnout has been found more and more common among university students [28, 67], including those who participated in online learning [44].

Many previous studies reported positive online learning experiences, in which students’ self-selection was satisfied [69, 90]. Nevertheless, it is also pointed out by many studies that students may experience poor readiness and adaptability to emergent (complete or partial) online learning [43, 90]. Browning et al. [11] found that students across seven U.S. universities felt anxiety, stress, isolation, and depression in online learning. More than that, the negative experience seems to be tightly correlated with learning burnout. Focusing on one Australian university, Keane et al. [38] reported that some students had a negative attitude toward online learning, lost their motivation to study, and were considering deferring or quitting their studies. Compared with face-to-face learning, online formal students with higher levels of burnout are more likely to have difficulties in adjusting and maintaining their motivation [31].

Learning burnout also was found to hinder learners’ engagement [26, 58], for example, Maroco [57] found that the role of students’ engagement plays in dropout intention and academic performance was weakened in the presence of burnout. Moreover, learning burnout was found to cause students’ long-term physical and mental health problems [76, 78, 79], which consequently produces a more profound influence on learners’ later lives than it appeals [76, 78, 79] found learning burnout led to depression in learners’ later life, and Bask et al. [8] found burnout caused dropping out of school. Therefore, in formal online learning, it is of great importance to explore the factors that may lead to learning burnout, which as a consequence proposes targeted intervention suggestions for educational practitioners and teachers.

The job demands-resources model (JD-R model) is a commonly used theoretical model in schools for exploring the influence that the learning environment may generate on learning burnout [21, 61, 76]. JD-R model divides influencing factors into two types: demands and resources [5, 6, 61]. Learning demands refer to the factors that are negatively associated with psychological or physical efforts [5, 6, 61]; but are positively linked with learning burnout. Learning resources are positive factors that help individuals achieve academic goals, alleviate burnout, as well as stimulate personal growth [5, 61]. In the context of online learning, the impacts of learning demand on learning burnout also have been studied. For example, Zhang et al. [95] found learning pressure contributed a lot to college students’ learning burnout. Zhao et al. [97] demonstrated that technostress is a significant variable in predicting learning burnout in a remote online learning environment. On the contrary, studies showed that learning resources played a positive role in alleviating learning burnout [92, 93] proposed that emotional support from teachers was negatively related to learning burnout. Zhang et al. [96] also suggested that social support alleviated learning burnout of college students. However, compared with traditional schooling, the factors that enhance or reduce burnout in formal online learning have not been thoroughly investigated.

In online learning, due to physical isolation and the lack of face-to-face interactions, learners must spend more physical or psychological effort to cope with loneliness [37, 46]. Loneliness is an unpleasant emotional experience caused by an individual’s dissatisfaction with interpersonal social life, or the lack of emotional connection with other people [9, 50]. Humans are inherently social beings and need to feel socially connected to others [19]. Loneliness has been found to be a more and more common phenomenon among university students in recent years [85], especially was found among online learners [37, 72, 90]. Online learners may suffer more from loneliness than those in face-to-face classes [2, 37] due to a lack of physical proximity, which provides the foundation for relational development [23]. Learning is also an activity that promotes knowledge construction through social interactions [37]. The loneliness perceived by students in online learning is found to be closely tied to their intention of dropping out, their low satisfaction, and their engagement in learning [23, 72].

JD-R model suggests that, in a school context, excessive demands cause emotional exhaustion [76]; And insufficient resources reduce motivation, which consequently results in physical or psychological alienation and inefficacy [6]. Loneliness in online learning entails a serious loss of organizational value and social resources [50]. Interestingly, conservation of resource theory (COR) has suggested that the loss of individual resources has a more significant impact on behavior than equivalently acquired resources [34]. The depletion model [17] proposes that a person has a fixed amount of mental and physical resources, and when energy is put on a particular target, less energy is shared by other targets. Grounded on the depletion model [17] and conservation of resource theory (COR), it is presumed that loneliness and studying simultaneously consume an individual’s learning resource. In other words, lonely learners need to put extra effort into completing learning tasks as part of the effort has to be used to cope with isolation, which may gradually increase the risk of burnout. Therefore, the following hypothesis is proposed in this study.

  • H1: Loneliness predicts learning burnout in exclusive online formal learning environments.

Variables moderate the impacts of loneliness on learning burnout

The influencing mechanism of environmental factors regarding learning burnout is complex [66]. Individuals differ in their perceptions of demands and resources [30, 61]. Although it is hypothesized that loneliness may lead to learning burnout, the effect is presumed to be moderated by an individual’s varied cognitive resources. JD-R model first proposed that there is an effort-driven energetic process and a motivational process in which resources and demands directly impact learning burnout generation [7, 21, 61], Secondly, the model proposed that individuals’ resource “buffers” against the loss of high demands on students, i.e., resources can mitigate the negative impact of demands on students [7, 21, 61]. For example, Makara-Studzińska et al. [53] found that the appearance of burnout was accompanied by high stress and low self-efficacy. The individual cognitive assessment results implied that a stressor triggers burnout only when he/she perceives that he/she is unable to cope with stress effectively.

With the development of JD-R model, personal resources have been incorporated into the JD-R model as they also influence one’ study-related performance [16, 61]. Academic buoyancy refers to the capacity that overcomes academic challenges typically in “everyday” life at school [59, 60]. Because it involves the ability to help students in coping with changes in the learning environment [63], Hoferichter et al. [35] suggested that academic buoyancy can be considered a personal resource. Same to learning resources, personal resources are also associated with positive learning outcomes [61].

Academic buoyancy is found an important attribute associated with students’ educational success. Students with high academic buoyancy tend to have greater learning engagement, adaptive coping strategies, and learning persistence [82]. Putwain et al. [98] suggested that students with high academic buoyancy can deal with negative emotions and academic stress. In a longitudinal study, Hoferichter et al. [35] found that academic buoyancy can slow the development of academic stress and anti-school attitudes in students. Most importantly, the role of academic buoyancy has been underlined as an important buffering variable against school burnout [77]. In addition to the direct impact that academic buoyancy generates on learning burnout (the focus of many studies), this study also intends to study its buffering effect against learning burnout. Therefore, hypothesis 2 is proposed in this study.

  • H2: Academic buoyancy buffers the effect of loneliness on learning burnout.

Social presence is another very important construct in the field of online learning [45]. Social presence mainly refers to the degree to which a person is perceived as a ‘real person’ in mediated communication [29]. In online learning, despite the lack of physical contact, learners use technology to communicate with teachers and peers through text, emoticons, voice, video, etc., which enhances their sense of social presence. Social presence not only improves students’ sense of immersion and reality [14], and reduces the possible loneliness in the online learning environment [41, 42], but also promotes the effect of students’ online learning [39]. Studies showed that helping online learners gain a sense of connection and communication with other learners is an effective method to resist loneliness. The enhanced social presence of others promoted lonely individuals’ positive feelings regarding media [41, 42], as well as their perception of the technologically formed bandages with other learners [40]. For example, Kehrwald [39] discovered that when people had familiar interactive partners in a media-supported environment, a more comfortable atmosphere was created, which as a consequence helped people “overcome feelings of loneliness or isolation” (p. 98). Kim et al. [42] held a similar view by finding that lonely individuals enjoyed media experiences when they felt the strong social presence of others, implying that social presence can improve lonely individuals’ media experiences. By adapting the theory in formal online learning, social presence is presumed to help lonely students gain a sense of social connection with others. However, its efficacy in learning burnout remains unknown. Therefore, hypothesis 3 is proposed in this study.

  • H3: Social presence moderates the relationship between loneliness and learning burnout.

A third moderating factor considered in this study is the duration of full participation in formal online learning. The duration of formal online learning that learners participate in varies from region to region. The time that students experience social isolation was found to affect their feelings of loneliness [18, 52]. It was shown in Loadeset al.’s study [52] that the duration of loneliness experiences was significantly correlated with health-related stress, i.e., the longer the learners experienced loneliness, the higher the subjective stress and mental health-related symptoms they reported. Evidence from Stepanikova et al.’ study [84] suggested that loneliness and life satisfaction varied with time spent online. In conclusion, the loneliness that influences mental health is tightly correlated with online duration. Therefore, it is speculated that the overtime online learning would also lead to learning burnout due to extreme loneliness which cannot be copied by learners. Therefore, hypothesis 4 is proposed in this study.

H4: The duration of online learning moderates the relationship between loneliness and learning burnout.

Methods

Sample and procedure

The data used in this study were obtained from convenience sampling through an online survey website named Wenjuan Star in Chinese. The participants were university students in cities in central China, including Changsha, Wuhan and Zhengzhou. The data was collected from May to June 2022. Students were encouraged to answer truthfully and were promised to keep their answers confidential. More than that, participants with qualified answers received ¥10 reward, which was ethical and steming from the principle of social beneficence [74].

We cleaned the collected data, and eliminated the questionnaires with duplicate answers, invalid answers or missing values. Finally, 15 invalid questionnaires were romved and obtaining 618 valid questionnaires, with a validity rate of 97.6%. The respondents were from pre-school education, electronic information engineering, psychology and other academic fields. All of them had experienced online learning. Table 1 outlines the background of participants. Among them, 264 (42.7%) were male and 354 (57.3%) were female; 578 (93.5%) were between 18 and 25 years old. In terms of grade, 53.7% (332) were freshmen, 26.1% (161) were sophomores, 13.6% (84) were juniors, and 15.5% (34) were seniors. In addition to online learning duration, no learner (0%) studied online for less than 7 days. 358 (57.9%) students had studied online for more than 7 days but less than one month, 103 (16.7%) students learned for 1–3 months, 71 (11.5%) students learned for one semester, and 86 (13.9%) students learned for more than one semester.

Table 1 Background of participants
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Measures

This study adopted four scales. ULS-8 Loneliness Scale [32] assess learners’ loneliness feeling with 8 items (2 reverse, 6 forward). High scores indicate high loneliness. Academic Buoyancy Scale [60] consists of 4 items assessing ability to cope with setbacks, rated on a 5-point Likert scale (1 = completely disagree; 5 = completely agree). Social Presence Scale (Arbaugh et al., 2008) contains 9 items gauging sense of presence in online learning Higher scores reflect stronger social presence. Maslach Burnout Inventory—Student Survey with the efficacy dimension reversed (MBI-SSi) [56] measures student burnout across three dimensions—exhaustion (5 items), cynicism (4 items), and inefficacy (6 items). Higher scores indicate higher burnout levels. All scales rated on a 5-point Likert scale (1 = completely disagree; 5 = completely agree).

Since all the scales were conducted in the Chinese context, we first formed a translation team consisting of all the authors of this study plus an English major teacher to translate the English version into Chinese. Follow the original meaning of the scale as much as possible during this process. Secondly, the order of sentences is modified by referring to the current Chinese-language version in order to better meet the reading habits of Chinese students. Then, about 10 college students were invited to make a prediction test of the scale, and subtle adjustments were made according to the expressions of items that were not easy to understand. Finally, the final version was formed (See Appendix for details).

To ensure the validity and reliability of the scales, we first performed factor analysis and corrected Item-Total Correlation. Detailed results are presented in Appendix . As can be seen from the appendix, the factor load of Item 3 and Item 6 in the loneliness scale is lower than 0.5, and Corrected item-total Correlation is also lower than 0.5, so it is deleted from the final scales, and other scale items are retained.

Then, we used composite reliability (CR) to evaluate structural reliability, and confirmatory factor analysis and average variance extracted (AVE) to evaluate convergent validity and discriminant validity (Hair et al.,2014). As shown in Table 2, Cronbach’s α was used for internal reliability analysis. All indicators were above 0.87, indicating good reliability, and the combined reliability (CR) of items exceed the recommended critical value of 0.7 [4]. This shows that the questions of each structure have good internal consistency. All AVE values of construct are greater than 0.5, which meets the recommended standard 0.5 [27], which indicates that the structural validity and convergent validity of the model are good. The discriminant validity was measured using the Fornell-Larcker criterion. As shown in Table 3, the square root of AVE is greater than the correlation coefficient between variables, so it meets the Fornell-Larcker criterion, which also indicates that the discriminant validity of the measurement model in this study is sufficient. All the results show that the measurement tool used in this study has good reliability, convergent validity and discriminant validity.

Table 2 Validity and reliability of measurement
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Table 3 The discriminant validity of the measurement model
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Common method bias

The results of Harman’s single-factor test show that 4 factors (academic buoyancy, social presence, loneliness, and online learning burnout) had eigenvalues greater than 1, of which the first factor explained a cumulative variation of 32.126% (less than 40%). This result indicates that there is no significant common methodological bias in this study.

Data analysis

Descriptive analysis and Correlation analysis was conducted first. Secondly, we used hierarchical linear regression to test the direct effect of loneliness on learning burnout. We entered the control variables into the model first, followed by the independent variables. Finally, to examine the moderating effect of the moderating variable, three distinct stratified linear regression models are constructed. The control variables, which include gender, age, and grade, are first entered into each model. Subsequently, the predictor variables are introduced. Following this, the moderating variable is added, and lastly, the interaction term between the predictor variable and the moderating variable is included. The presence of moderation is assessed by determining whether this interaction term is significant and by observing changes in the F-values after incorporating the interaction term. If moderation is found to exist, slope analysis is conducted to investigate the relationship between the predictor variable and the dependent variable at different levels of the moderating variable. We performed all analyses in SPSS (version 26).

Results

Descriptive analysis and correlation analysis

Table 4 outlines the results of descriptive analysis. As seen in Table 1, online learning students had average levels of loneliness and academic buoyancy. mean online learning burnout levels fell within the 2.3–3.4 range, showing the risk of burnout [54]. In addition, the absolute value of skewness for all variables is less than 1 and the absolute value of kurtosis is less than 3, indicating that the variables involved in the study tend to be normally distributed.

Table 4 Descriptive analysis
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The results of the Pearson’s correlation (as showm in Table 5) revealed that loneliness was positively and significantly correlated with online learning burnout (r = 0.494**), while academic buoyancy (r = -0.122**) and social presence (r = -0.108**) had significant and negative associations with burnout.

Table 5 A pearson correlation matrix among 8 parameters
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Linear regression models for predicting learning burnout

To fit loneliness, social presence, academic buoyancy, online learning duration, and learning burnout, linear regression models are built by using learning burnout as the target and other variables as predictors. Table 6 outlines the results of the hierarchical multiple regression model. It is found that the model passes the F test (F (7,610) = 42.623, p = 0.000), indicating that the model is significant. In addition, by testing the multicollinearity of the model, it is found that all VIF values in the model are less than 5, which means that there is no collinearity problem. Moreover, the D-W value is near 2, indicating that there is no autocorrelation in the model, and that the model is good. The findings suggest that by controlling the effects of the students’ demographic variables on online learning burnout, loneliness significantly and positively predicted online learning burnout (β = 0.584, t = 16.613, p = 0.000 < 0.01). Social presence (β = -0.225, t = -4.382, p = 0.000 < 0.01) was found to significantly and negatively influence learning burnout. However, academic buoyancy (β = -0.067, t = -1.951, p = 0.052 > 0.05) and online learning duration has no significant effect on learning burnout (β = -0.001, t = -0.034, p = 0.973 > 0.05).

Table 6 A hierarchical multiple regression model with 4 predictors
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Three hierarchical regression models

To explore the moderating effects of academic buoyancy, social presence and online learning duration on the relationship between loneliness and online learning burnout, three hierarchical regression models are built.

The results are presented in Table 7. Model 1 analyzes the effect of loneliness on online learning burnout without any moderating variables. Model 2 adds moderator variables based on Model 1, and Model 3 adds the interaction term of loneliness and academic buoyancy based on Model 2. The results show that the interaction term [loneliness* academic buoyancy] is not statistically significant (β = -0.018, t = -0.925, p > 0.05). In addition, when model 2 changes to model 3, there is no significant change in F value [F (1,611) = 0.856,p = 0.355]. This suggests that academic buoyancy cannot act as a buffer against loneliness, which promotes learning burnout in online learning.

Table 7 Academic buoyancy functions as a moderator for the effect of loneliness on learning burnout
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Next, we examine the moderating effect of social presence on the relationship between loneliness and learning burnout during online learning. Results illustrated in Table 8 revealed that the moderations [loneliness* social presence] were not significantly negatively related to learning burnout (β = -0.015, t = -0.815, p > 0.05).

Table 8 Social presence functions as a moderator for the effect of loneliness on learning burnout
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Finally, the moderating effect of online learning duration on the relationship between loneliness and learning burnout during online learning is evaluated. The results outlined in Table 9 suggests that the duration of online learning shows no contribution to improve model’s explanatory ability. The interaction term [loneliness* Online learning duration] was not statistically significant (β = 0.009, t = 0.361, p > 0.05). The results suggest that the effect of loneliness on learning burnout did not increase with the duration of online learning.

Table 9 Online learning duration functions as a moderator for the effect of loneliness on burnout
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Discussion

The main goal of the present research was to identify the factors that cause online learning burnout in order to better tailor strategies to alleviate it.

The results indicate that loneliness plays an important and unique role in online learning burnout. As Feelings of loneliness exacerbate students’ burnout in online learning, which was consistent with the results of previous studies [10, 33, 55], supported H1. Many previous studies already outlined that loneliness may lead to negative feelings about the learning experience (for review, see [37], and suggested that the perception may be higher in online than that in face-to-face learning environment [2], which even lead to mental health problems (for review, see [52]). This result adds to the JD-R theoretical model that loneliness in online learning does act as a demand, exacerbating learning burnout. It seems that lonely students experienced emotional distress [12], social anxiety and general anxiety [73] due to unsatisfactory social relationships, which as a consequence resulted in emotional exhaustion over time [15]. The required extra effort to cope with such an unpleasant learning experience triggered emotion-centered, passive coping strategies (such as rejection or withdrawal from the class) and cynicism [3]. It is also speculated that learners’ boredom and avoidance of learning were also generated from emotional exhaustion and cynicism [7]. Such causal-result linkage explains why loneliness is found in this study to be significantly correlated with learners’ learning burnout in a formal online learning context with a rather high coefficient.

Unexpectedly, our results found the duration of online formal learning has no significant relationship with learning burnout, and the relationship between loneliness and learning burnout is not moderated by the duration of online learning, rejected H4. Although a systematic review conducted by [52] summarized that the longer the learners experienced loneliness, the higher the subjective stress and mental health-related symptoms they reported, our study did not show consistent results. With the duration of online formal learning getting longer, the effect of loneliness on online learning burnout was not strengthened. These results align with Feldman et al. [24] and Hendryadi et al. [33], who found a stable effect over time on student loneliness, the level of burnout is consistent across time. This may be due to the measurement of online learning duration. This study divided the learning time into five groups, which were less than one week, more than a week but less than a month, 1 to 3 months, one semester, and more than one semester. In the present study, the shortest duration of online full formal learning has been more than one week, and learners have experienced complete physical isolation for more than one week, which is considered a long period of loneliness for learners, not much different from more than one semester. Therefore, in our study, online learning duration did not play a moderating role in the relationship between loneliness and online learning burnout. Follow-up studies that break down online learning time into smaller grains may come to a different conclusion.

The result also suggests that the effect of academic buoyancy is limited. The hypothesis that academic buoyancy plays a moderating role in the effect of loneliness on learning burnout could not be confirmed (rejected H2). Previous findings [59, 60] have shown that students with high academic buoyancy showed higher self-efficacy and self-regulation ability, therefore, it is presumed that academic buoyancy relieves learning burnout via acting as a personal resource [62, 64]. Students with high academically buoyant are expected to employ self-regulation strategies in adverse academic situations, optimize basic psychological needs [71], facilitate class- and school climate [35], and promote the mastery of goals [94]. However, this study’s result suggests that the effects of academic buoyancy are not strong enough to buffer the negative impacts from extreme loneliness caused by a pure virtual environment of formal learning. This result is in line with [35]’s study, in which they also found that buoyancy was most beneficial for students with low or average levels of stress, and for students with high stress, they did not benefit from buoyancy.

Finally, we would like to discuss the effect produced by the social presence on relieving learning burnout. It is interesting that the buffering effect of social presence between loneliness and online learning burnout was not confirmed (rejected H4). Although previous studies have shown that increasing students’ social presence in online learning can help them overcome loneliness [37, 42], our results suggest that even if learners truly perceive the existence of their peers and teachers and have a sense of connection, their loneliness will still lead to online learning burnout. Formal online learning inevitably integrates with internet communication tools, platforms, as well as virtual communicating communities [92, 93], which delivers teachers a false feeling of students’ social presence. However, as pointed out by (Longa et al., [20]), communication systems, such as videoconferencing, social media use and engagement with virtual reality activities, do not support sensory feedback and the sense of touch. Usual online communications have been shown to be not sufficient to prevent social isolation and loneliness [10, 87]. This result highlights the importance of designing the virtual climate and atmosphere for online formal learning to build affective touch in the instructional designs. Affective touch, a communication channel with a unique potential for social interaction and connections, is effective in reducing negative feelings of loneliness during virtual experiences.

Implications

Overall, this study validated the applicability of the Job Demands-Resources (JD-R) model in the context of online learning, with the findings providing support for some of the model’s hypotheses. Our results suggest that loneliness, as a demand, exacerbates learning burnout in online learning, while students’ academic buoyancy, social presence and online learning duration cannot act as buffers to mitigate this erosive effect. Thus, loneliness in online learning should not be ignored, as it can cause different degrees of learning burnout, no matter whether learners have high or low academic buoyancy. In addition, in the field of online learning, a large number of researchers have examined social presence. However, learners with a high sense of social presence still feel lonely [51]. Social presence did not buffer against the increase of online learning burnout caused by loneliness. Therefore, future research should continue to investigate how to alleviate loneliness in online learning.

Undoubtedly, the results of this study suggest that online learning burnout can be relieved by enhancing learners’ online social presence. Thus, instructional designs targeted at formal online learning should make focus on enhancing learners’ social presence during learning, which as a consequence reduces learners’ extreme loneliness and learning burnout. Firstly, creating a vivid and rapport online climate and atmosphere for learners is the optimal solution to stimulate participants’ social presence unconsciously, such as building a game-based learning context [41, 42], in which learners cooperatively implement learning tasks. Then, AI agents for loneliness is not sufficient to deal with students’ intense feelings of loneliness under a stress-accompanied formal learning context, although many studies hold that AI agents can provide people with human-similar social warmth [68] We would not recommend fully using AI agents as a replacement for authentic communication between human beings. The feasible solution is to enhance learners’ social presence passively, as we mentioned earlier that social presence not only refers to rapport feelings but also can be other feelings. For example, requiring each student to afford a team-leader role to complete the necessary communication works in turn, in which the team-leader should ask for other students’ homework, collect peer comments about another team’s performance, or transfer teachers’ notes to his/her peers one by one, etc. The task-driven communications entail the necessary bondages among students.

Conclusion, limitations and future research

Learning burnout is a prominent negative construct of inquiry in online learning, especially in the context of fully formal online learning. Harnessing JD-R theory, we demonstrated that loneliness triggers learning burnout, while social presence relieves burnout. However, the effect of academic buoyancy is limited. Moreover, academic buoyancy, social presence and online learning duration cannot buffer against the positive impact of loneliness on online learning burnout. Overall, the results highlight the important role of loneliness in online learning. Based on those findings, pedagogical suggestions are made for practitioners and educators.

Alongside some valuable and interesting findings, we must acknowledge some limitations of this study. First, we only used self-reported subjective data. Since loneliness is a subjective experience, self-report instruments may be better measurement tools. However, additional indicators, such as interview and log data from an online learning platform, could be used to triangulate outcomes with self-reported results. Second, this study is a cross-sectional one, which cannot reveal well the causal relationship between loneliness and learning burnout. Future studies should use a longitudinal design to further examine the causal link between the two. Third, we only focused on online learning among university/college students. Compared to college students, K-12 students show less readiness and adaptability to online learning [43, 90]. In addition, K-12 students are in school at a time when peer influence expands [43, 90]. These students have a stronger desire for social interactions and are more sensitive to social isolation [43, 90]. Thus, there is a need for more research about K-12 students. Fourth, online learning during COVID-19 differ from online learning in normal situations because students have free time to go out and meet people in person. In fully online formal learning during the epidemic, learners suffered from greater loneliness, and its association with burnout may be strengthened. Therefore, it is needed for follow-up studies to explore whether the loneliness felt by learners in online learning in normal situations will show the same relationship with burnout. Finally, future studies should explore other factors that impact students’ online learning burnout, such as the technical environment, teaching approaches and so on.

Data availability

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

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Funding

This work was supported by the National Natural Science Foundation of China Youth Project (62407032) and SCJG24C163.

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  1. College of Computer Science, Sichuan Normal University, 5 Jing’ an Rd, Chengdu Sichuan, 680000, China

    Hongxia Li & Juan Yang

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LHX completed the research design, data collection and analysis, and manuscript writing; YJ completed the design of the study and the revision of the manuscript.

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Appendix

Appendix

Loneliness scale

 

Item

Factor loading

Corrected Item-Total Correlation

1

I lack companionship

0.715

0.699

2

There is no one I can turn to

0.708

0.662

3

l am an outgoing person*

0.480

0.417

4

l feel left out

0.757

0.676

5

l feel isolation from others

0.798

0.722

6

l can find companionship when l want it*

0.303

0.254

7

l am unhappy being so withdrawn

0.705

0.658

8

People are around me but not with me

0.855

0.749

Social presence scale

 

Item

Factor loading

Corrected Item-Total Correlation

1

I felt emotionally drained by the online learning

0.833

0.838

2

I feel exhausted at the end of a day of online learning

0.898

0.889

3

I often feel exhausted in recent times

0.868

0.875

4

Taking online learning is stressful for me

0.896

0.899

5

I feel tired of taking online learning

0.928

0.906

6

I am increasingly skeptical of the significance of taking online learning

0.925

0.905

7

I don't think it matters whether I learn or not when I participate in online learning

0.913

0.901

8

I hate online learning.

0.880

0.876

9

I don't think I have enough patience when it comes to online learning

0.852

0.866

10

I can't effectively solve the problems that arise in online learning.

0.742

0.778

11

When taking online learning, I feel that I can't achieve the expected learning goals.

0.779

0.822

12

When taking online learning, I don't learn interesting things.

0.871

0.846

13

When taking online learning, I feel that I cannot complete my academic tasks.

0.930

0.911

14

Mastering what I learn is a little difficult for me when I take online learning.

0.917

0.902

15

I'm so bad at learning in online learning that I want to give up.

0.697

0.734

Social presence scale

 

Item

Factor loading

Corrected Item-Total Correlation

1

Getting to know other course participants gave me a sense of belonging in the course

0.769

0.792

2

I was able to form distinct impressions of some course participants.

0.733

0.760

3

Online or web-based communication is an excellent medium for social interaction.

0.741

0.768

4

Online or web-based communication is an excellent medium for social interaction.

0.825

0.828

5

I felt comfortable participating in course discussions.

0.910

0.876

6

I felt comfortable interacting with other course participants.

0.900

0.870

7

I felt comfortable disagreeing with other course participants while still maintaining a sense of trust.

0.862

0.819

8

felt that my point of view was acknowledged by other course participants.

0.930

0.887

9

online discussion helped me to develop a sense of collaboration.

0.878

0.840

Academic buoyancy scale

 

Item

Factor loading

Corrected Item-Total Correlation

1

I’m good at dealing with setbacks, such as a bad mark or negative feedback on my work

0.901

0.832

2

I don't let study stress get on top of me.

0.880

0.869

3

I think I'm good at dealing with schoolwork pressures.

0.925

0.890

4

I don't let a bad mark affect my confidence.

0.664

0.828

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Li, H., Yang, J. Managing online learning burnout via investigating the role of loneliness during COVID-19. BMC Psychol 13, 151 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40359-025-02419-3

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