The Effect of University Students’ Academic Self-Efficacy on Academic Burnout: The Chain Mediating Role of Intrinsic Motivation and Learning Engagement

Abstract

Academic burnout seriously affects the academic performance and mental health of college students. This study developed a multi-mediation model to investigate the relationship between academic self-efficacy and academic burnout. A total of 1431 undergraduate students (51.85% female) were recruited to participate in this study. And we used SPSS PROCESS developed by Hayes to examine the mediating role of academic self-efficacy and academic burnout. The results indicated that academic self-efficacy can negatively predict academic burnout. Moreover, intrinsic motivation and learning engagement have a significant mediating effect between academic self-efficacy and academic burnout. Finally, we constructed a model comprising four variables and found that it explained 47.62% of the variance in academic burnout. These findings can be conducive to a suitable intervention path to reduce academic burnout of Chinese college students, so as to develop effective instructional strategies to improve their learning performance.

Keywords

academic self-efficacy intrinsic motivation learning engagement burnout mediation

Introduction

During the long and arduous learning process, college students are prone to fatigue, depression, and frustration. If such emotions are not effectively resolved, academic burnout will occur. Research has found that about 40% of college students suffer from moderate or high levels of academic burnout (Chang et al., 2016). Academic burnout, as a severe psychological distress and one of the unfortunate consequences of inordinate amounts of stress and anxiety in academic settings, has been the subject of a substantial body of research within the past two decades. Academic burnout refers to students’ lack of confidence in coping with learning difficulties and negative psychology towards studying (Tuominen-Soini & Salmela-Aro, 2014). It is conceptualized as a tripartite syndrome marked by emotional exhaustion (tiredness due to academic challenges and demands), cynicism (having a pessimistic feeling and absence of interest in academics), and academic inefficacy (a sense of incompetency and lack of accomplishment as a student) (Karimi & Fallah, 2021). Academic burnout not only has serious consequences for students’ physical and psychological health (Farina et al., 2020; Herrmann et al., 2018) but is also associated with lower academic motivation, which can lead to decreased engagement, academic performance, and increased dropout rates (Fiorilli et al., 2017; Maroco et al., 2020; Zhang et al., 2007). How to prevent college students from academic burnout during the learning process is an urgent problem in higher education. Previous studies have mainly focused on the external environmental factors affecting academic burnout, and there is less discussion on college students’ psychological factors and their relationship. However, a large number of quantitative studies have shown that the three variables of academic self-efficacy, internal motivation, and learning engagement are closely related to college students’ academic burnout (Maricutoiu & Sulea, 2019; ÖZhan, 2021). Based on this, this study intends to take these three variables as antecedents of academic burnout to explore the influence mechanism of academic self-efficacy, internal motivation, and learning input on academic burnout, so as to provide a scientific basis for improving college students’ academic performance.

Literature Review

Academic Self-Efficacy

Bandura (1997) defines self-efficacy as “beliefs in one’s capacity to organize and successfully carry out the courses of action required to perform in a certain field,” which consists of four basic sources, namely, an individual’s performance experience, emotional state, indirect experience, and verbal persuasion. Self-efficacy in the academic domain is referred to in the literature as academic self-efficacy, which refers to people’s belief that they are capable of organizing and executing the activities necessary to achieve pre-planned educational expectations (Gutiérrez & Tomás, 2019). According to Bandura’s Social Cognitive Theory, self-efficacy is a key factor which influences both motivation and engagement. Several studies have shown that college students must have high academic self-efficacy in their studies (Adams et al., 2020), and that students with high self-efficacy are more willing to put more effort into the learning process and show long-term resistance and patience with difficulties than those with weak beliefs, thus showing higher levels of academic performance (Fernandez-Rio et al., 2017; Gebauer et al., 2020). In addition, academic self-efficacy has a significant negative correlation with academic burnout (ÖZhan, 2021). This is due to the fact that individuals spend a lot of energy in order to achieve their academic goals. If an individual judges that he is not able to cope with the challenges of his study, it will affect his efforts and concentration, which in turn can create pressure and affect his judgment of self-efficacy, leading to his depression and academic burnout.

Intrinsic Motivation

As a macro theory of human motivation and personality, self-determination theory is related to individuals’ inherent growth tendencies and innate psychological needs (competence, autonomy, and relatedness). In order to promote adequate social function and personal well-being, these needs must be met. According to Ryan and Deci (2000), the satisfaction of these needs will contribute to the development of intrinsic motivation. Intrinsic motivation is used to refer to the internal desire to do something or pursue a goal because it is satisfying and enjoyable in itself, rather than an extrinsic motivation. Driven by interest and enjoyment, people with intrinsic motivation are more likely to work hard and feel less emotional fatigue (Deci & Ryan, 1985). Compared with external motivation, intrinsic motivation is more effective in reducing negative psychological attitudes and behaviors, such as turnover intention and job burnout (Koeske & Koeske, 1989). There is a negative correlation between intrinsic motivation and academic burnout (Rehman et al., 2020). Individual intrinsic motivation will affect their learning goals, learning methods, and learning strategies and can promote students’ autonomous learning. Students with high intrinsic motivation will actively ask questions, seek advice, master learning strategies, participate in the classroom, and show less academic burnout (Alhadabi et al., 2019). Students with low intrinsic motivation lack interest in learning, break away from classroom activities, and feel meaningless in academic activities, resulting in academic burnout and low academic achievement (Aguayo et al., 2019). According to Ehrman (2003), if learning itself is considered to be purposeful, and learners find the task challenging and attractive, and have confidence in accomplishing the task, they will have intrinsic motivation to learn. Learners with intrinsic motivation feel satisfied and comfortable with the education they receive and experience less anxiety and academic burnout.

Learning Engagement

Schaufeli (2002) defined engagement as a positive, fulfilling state of mind related to work, including dedication, vigor, and absorption. Engagement was originally used in human resource management, and it is also applicable to college students. Learning engagement is defined as a continuous, fulfilling, and positive emotional and cognitive state related to learning or research, which means that individuals have high energy and psychological resilience (vitality) when learning, gain a sense of value and enthusiasm (dedication) from learning, and can concentrate and happily devote themselves to learning (concentration). Research shows that academic engagement is positively correlated with academic performance and negatively correlated with academic burnout. Engagement in learning can bring a series of positive effects. Individuals who are more engaged in learning are better at applying strategies and have a stronger sense of self-control, leading to higher satisfaction and academic performance, and reducing negative psychological symptoms (Liu et al., 2018). Akanni (2019) found that individuals with higher levels of academic burnout have difficulty engaging in learning even if they have higher IQs. A recent cross-sectional study found that during the epidemic of COVID-19, there was a significant negative correlation between academic engagement, psychological capital, and academic burnout. Academic engagement can effectively reduce academic burnout (Wang et al., 2021). Other studies have shown that intrinsic motivation has an important impact on learning engagement. Students with high intrinsic motivation are actively engaged in learning, are more likely to feel proud of their achievements, and are less likely to suffer from academic burnout (Andrew et al., 2017; Chen, 2019).

Aim and the Hypotheses of the Research

According to the current literature, academic burnout is an important factor that negatively affects students’ academic performance and mental health (May et al., 2015), and it is considered an important risk for many developmental processes (ÖZhan & YÜKsel, 2022). In addition to these negative effects, it is thought that the emergence of academic burnout can be prevented through different college-based interventions. With this in mind, it is considered important to carry out studies to prevent academic burnout or reduce its negative impact on students. With this research, it is thought that studying variables of academic self-efficacy, which directly affects academic burnout, as well as intrinsic motivation and learning engagement, which mediate the effect of self-efficacy on academic burnout, is of significance. Several studies have examined the relationship between academic self-efficacy, intrinsic motivation, learning engagement, and academic burnout. However, there are no studies in which academic self-efficacy, intrinsic motivation, learning engagement, and academic burnout are examined together by also considering the mediating role of intrinsic motivation and learning engagement. In this respect, the present study is considered important in terms of increasing the current knowledge in the literature. In a word, this study aims to shed light on the relationship between academic self-efficacy and academic burnout, as well as the chain mediating role of intrinsic motivation and learning engagement. It will provide data-based evidence for the effects of academic self-efficacy, intrinsic motivation, and learning engagement, which are thought to be three important instruments that can be used to prevent or reduce the effects of academic burnout.

Based on the evidence presented above, we constructed a chain mediating model (Figure 1) and formulated the following hypotheses.

Figure 1.

Hypothesized relationships among academic self-efficacy, intrinsic motivation, learning engagement, and academic burnout.

Academic self-efficacy is negatively related to academic burnout.

Intrinsic motivation is negatively related to academic burnout.

Learning engagement is negatively related to academic burnout.

Intrinsic motivation and learning engagement play a chain mediating role between academic self-efficacy and academic burnout.

Methods

Participants

The questionnaire was created using the Questionnaire Star online survey platform (https://www.wjx.cn) and distributed through the WeChat application. Using a random sampling method, we conducted questionnaires in five universities: Huzhou Normal University, Jiaxing University, Shaoxing University, Shanghai Normal University, and Hangzhou Normal University. According to the questionnaire answer settings, each IP address could complete only one questionnaire. In total, 1,677 questionnaires were collected, with invalid ones removed, resulting in 1,431 valid questionnaires. Among these, 689 respondents were male (48.15%), 742 were female (51.85%), 916 were first-year students (64.01%), and 515 were second-year students (35.99%).

Measurement Tools

According to the translation and back-translation procedure (Cheung et al., 2020), the following four questionnaires were translated into Chinese and adjusted to fit the specific conditions of college students’ classroom learning. Subsequently, the Chinese and English versions were compared to ensure each item accurately matched the original meaning. The final questionnaire items were established once consensus was reached on the concepts in both languages. The Chinese version was tested and validated through factor analysis and model fitting.

Academic Self-Efficacy Questionnaire

The Academic Self-Efficacy scale from the Patterns of Adaptive Learning Scales (PALS) (Midgley et al., 2000) was used for measurement. This scale consists of six items that ask respondents to report on their general learning situations over the past six months, reflecting their overall self-efficacy level, such as, “I am confident that I can master the knowledge and skills taught by teachers this year.” A 5-point Likert scale was used, with 1 denoting strongly disagree and 5 denoting strongly agree. The average scores for all items were calculated, with higher scores indicating higher academic self-efficacy levels. In the present report, the Cronbach’s α-value was 0.80. The fit indices were as follows: χ2/df = 9.25, RMR = 0.03, GFI = 0.96, CFI = 0.92, TLI = 0.92, and NFI = 0.91.

Intrinsic Motivation Questionnaire

The measurement used a simplified version of the Intrinsic Motivation Inventory (Kotera et al., 2023), consisting of six items that measure three dimensions: intrinsic motivation to learn, intrinsic motivation toward accomplishment, and intrinsic motivation to experience stimulation. In this study, the background condition in English learning was added to the intrinsic motivation scale, such as, “in English learning class, I feel happy when my grades improve.” The questionnaire items were scored using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), with a Cronbach’s α-value of 0.89. The fit indices were as follows: χ²/df = 8.26, RMR = 0.05, GFI = 0.95, CFI = 0.95, TLI = 0.92, and NFI = 0.95.

Learning Engagement Questionnaire

The learning engagement questionnaire—University Student Engagement Inventory (USEI) (Maroco et al., 2016) was utilized, consisting of 15 items that measure three dimensions: behavioral engagement, cognitive engagement, and emotional engagement. Factor analysis results suggested that the items “I don’t feel very accomplished at this school and I talk to people outside the school on matters that I learned in class” were deleted due to low factor loadings (0.375 and 0.386, respectively). The final learning engagement comprised three dimensions: behavioral engagement with 5 items, cognitive engagement with 4 items, and emotional engagement with 4 items. The revised scale’s fit indices were as follows: χ2/df = 3.38, RMSEA = 0.07, CFI = 0.95, TLI = 0.92, amd NFI = 0.93, indicating acceptable structural validity. The Cronbach’s α-values for the three sub-scales were 0.88, 0.87, and 0.80, respectively.

Academic burnout questionnaire

The academic burnout questionnaire—Maslach Burnout Inventory-Student Survey (MBI-SS) (Schaufeli et al., 2002) was utilized, consisting of 15 items that measure three dimensions: Exhaustion, Cynicism, and Professional Efficacy. A 5-point Likert scale was used, with 1 denoting strongly disagree and 5 denoting strongly agree. The average scores for all items were calculated, with higher scores indicating higher academic burnout levels. Factor analysis results suggested that the items “I feel burned out from my studies,” “I feel stimulated when I achieve my study goals,” and “I have learned many interesting things during the course of my studies” were deleted due to low factor loadings (0.317, 0.349, and 0.328, respectively). The final academic burnout comprised three dimensions: exhaustion with 4 items, cynicism with 4 items, and professional efficacy with 4 items. The revised scale’s fit indices were as follows: χ²/df = 2.04, RMR = 0.03, GFI = 0.96, CFI = 0.97, TLI = 0.97, and NFI = 0.96, indicating acceptable structural validity. The Cronbach’s α-values for the three sub-scales were 0.80, 0.87, and 0.85, respectively.

Data Analysis

Maximum likelihood estimation was used to handle missing values and achieve unbiased parameter estimates and precise standard errors. Descriptive statistics (mean, standard deviation, and correlation coefficients) were calculated for the research variables using SPSS 20.0 software. The PROCESS program developed by Hayes (2013) (available at https://www.afhayes.com) was used for mediation analysis, assessing the chain mediating effect of academic self-efficacy and flow experience on the correlation between intrinsic motivation and learning engagement. The significance of regression coefficients was tested by means of the Bootstrap method, obtaining standard errors of parameter estimates and confidence intervals. Confidence intervals that did not include zero indicate statistical significance.

In order to analyze the reliability and validity of the questionnaire, we computed the factor loadings, average variance extracted (AVE), composite reliability (CR), and Cronbach’s α. As shown in Table 1, all factor loadings were above 0.6, and AVE values were >0.5, showing good convergent validity. Internal consistency was assessed using CR, ranging from 0.82 to 0.89, exceeding the recommended threshold of 0.7. Questionnaire reliability analysis was conducted using Cronbach’s α, with coefficients ranging from 0.76 to 0.89, surpassing the recommended threshold of 0.6, which demonstrates variable reliability. Based on this analysis, the questionnaires were considered to be reliable and valid.

Table 1.

Reliability and Validity Analysis.

Variable	Items	M ± SD	Loading	Cronbach’s α	CR	AVE
Academic self-efficacy	SE1	3.28 ± 0.94	0.75	0.76	0.84	0.51
	SE2	3.70 ± 0.84	0.71
	SE3	4.19 ± 0.64	0.71
	SE4	3.89 ± 0.76	0.63
	SE5	3.73 ± 0.82	0.76
Intrinsic motivation	IM1	4.08 ± 0.67	0.79	0.89	0.89	0.57
	IM2	3.85 ± 0.78	0.76
	IM3	4.30 ± 0.67	0.79
	IM4	3.94 ± 0.78	0.80
	IM5	4.22 ± 0.70	0.74
	IM6	4.26 ± 0.67	0.67
Learning engagement	BE1	3.32 ± 1.07	0.74	0.88	0.88	0.59
	BE2	3.22 ± 1.09	0.78
	BE3	2.87 ± 1.09	0.74
	BE4	3.45 ± 1.09	0.85
	BE5	3.36 ± 1.13	0.74
	EE1	3.21 ± 0.98	0.69	0.87	0.87	0.63
	EE2	3.03 ± 1.14	0.89
	EE3	3.25 ± 1.04	0.75
	EE4	3.36 ± 1.04	0.83
	CE1	3.78 ± 0.88	0.80	0.80	0.82	0.53
	CE2	4.13 ± 0.71	0.83
	CE3	3.86 ± 0.83	0.72
	CE4	3.86 ± 0.83	0.54
Academic burnout	EB1	3.24 ± 0.98	0.77	0.87	0.87	0.63
	EB2	3.44 ± 1.00	0.79
	EB3	3.40 ± 1.11	0.78
	EB4	3.06 ± 1.03	0.82
	CB1	2.78 ± 1.09	0.71	0.86	0.86	0.61
	CB2	3.13 ± 1.09	0.78
	CB3	2.55 ± 1.09	0.76
	CB4	2.64 ± 1.13	0.86
	PB1	2.44 ± 0.80	0.68	0.77	0.88	0.68
	PB2	2.72 ± 0.94	0.60
	PB3	2.30 ± 0.84	0.72
	PB4	2.27 ± 0.82	0.72

Results

Test for Common Method Bias

Harman’s single-factor test was used to assess the severity of common method bias (Podsakoff et al., 2003). The results showed that there were five factors with eigenvalues surpassing 1, accounting for 61.42% of the variance. Notably, the first factor explained only 15.68% of the variance, falling below the critical threshold of 40%. Consequently, the study concluded that the impact of common method bias on the results was negligible.

Descriptive Statistics and Correlation Analysis

The means, standard deviations, and correlation coefficients of the variables are enlisted in Table 2. As shown, there were significant correlations between all the variables. Accordingly, there was a high and significant negative correlation between intrinsic motivation (r = −0.89, p < .001), learning engagement (r = −0.82, p < .001), and academic burnout scores, which are the dependent variables of the research. A moderate and significant negative correlation was found between academic self-efficacy (r = −0.57, p < .001) and academic burnout scores. Besides, there was a moderate and significant positive correlation between academic self-efficacy (r = 0.54, p < .001), intrinsic motivation (r = 0.70, p < .001), and learning engagement, while there was a low significant positive correlation between academic self-efficacy (r = 0.25, p < .001) and intrinsic motivation. According to the recommendation of Cronk (2008), the correlation criteria are as follows: 0.30 or less as low correlation; between 0.30 and 0.70 as moderate; values greater than 0.70 as high.

Table 2.

Pearson Correlation Between the Variables.

Variables	M ± SD	1	2	3
1. Academic self-efficacy	3.76 ± 0.57
2. Intrinsic motivation	2.90 ± 0.84	0.25**
3. Learning engagement	3.43 ± 0.63	0.54**	0.70**
4. Academic burnout	2.83 ± 0.63	−0.57**	−0.89**	−0.82**

Chain Mediating Effect of Intrinsic Motivation and Learning Engagement

Using academic burnout as the outcome variable, academic self-efficacy as the predictor variable, and intrinsic motivation and learning engagement as mediating variables, the PROCESS program established by Hayes was employed to conduct mediation analysis. The outcomes are presented in Table 3. Academic self-efficacy significantly positively predicts intrinsic motivation (β = 0.37, p < .001) and learning engagement (β = 0.43, p < .001). Intrinsic motivation significantly positively predicts learning engagement (β = 0.45, p < .001) and significantly negatively predicts academic burnout (β = −0.52, p < .001). Learning engagement significantly negatively predicts academic burnout (β = −0.18, p < .001).

Table 3.

Regression Model Results.

Outcome variable	Predictor variable	R ²	F	β	t
Academic burnout	Academic self-efficacy	0.33	209.41	−0.63	−14.47
Intrinsic motivation	Academic self-efficacy	0.06	29.49	0.37	5.43
Learning engagement	Intrinsic motivation	0.63	366.98	0.45	19.80
Learning engagement	Academic self-efficacy	0.63	366.98	0.43	12.87
Academic burnout	Intrinsic motivation	0.93	1959.45	−0.52	−38.24
	Learning engagement			−0.18	−8.57
	Academic self-efficacy			−0.33	−19.67

Further analysis was conducted using the bias-corrected nonparametric percentile Bootstrap method (with 5,000 resamples) to analyze whether intrinsic motivation and learning engagement mediate the link between academic self-efficacy and academic burnout within a 95% confidence interval (Table 4 and Figure 2). The indirect effects of intrinsic motivation and learning engagement (Bootstrap 95% confidence intervals) do not include zero, indicating that both the factors significantly regulate the link between academic self-efficacy and academic burnout. The total indirect effect stands at 0.30, constituting 47.62% of the overall effect. The mediation effects operate through three pathways on academic burnout: academic self-efficacy → intrinsic motivation → academic burnout, with an effect of 0.19 (30.16% of total effect); academic self-efficacy → intrinsic motivation → learning engagement → academic burnout, with an effect of 0.03 (4.76% of total effect); academic self-efficacy → learning engagement → academic burnout, with an effect of 0.08 (12.70% of total effect). These results indicate the presence of a sequential mediation effect of academic self-efficacy on academic burnout, suggesting that academic self-efficacy not only significantly negatively impacts learning engagement but also indirectly affects it through the chain mediation of intrinsic motivation and learning engagement.

Table 4.

Mediating Effect Analysis.

	Path	Boot SE	95% CI	Effect	Percent (%)
Direct effect	AS→AB	0.17	[−0.37, −0.30]	−0.33	52.38
Indirect effect	AS→IM→AB	0.43	[−0.28, −0.11]	−0.19	30.16
	AS→IM→LE→AB	0.01	[−0.05, −0.02]	−0.03	4.76
	AS→LE→AB	0.02	[−0.11, −0.05]	−0.08	12.70
Total indirect effect		0.05	[−0.40, −0.21]	−0.30	47.62
Total effect		0.04	[−0.72, −0.54]	−0.63

Notes: AS = academic self-efficacy; IM = intrinsic motivation; LE = learning engagement; AB = academic burnout.

Figure 2.

Chain mediating effect of Academic Self-efficacy and Academic Burnout.

Discussion

This study aims to examine the relationship between college students’ academic self-efficacy and academic burnout, as well as the mediating role of intrinsic motivation and learning engagement. Overall, our findings confirm the proposed hypotheses. In this study, 40.37% of college students had varying degrees of academic burnout, which is consistent with Chang’s findings (Chang et al., 2016). In this study, the academic burnout score of Chinese college students was 2.83 ± 0.63, which was lower than 2.97 measured by the same instrument (Wang et al., 2021).

Academic Self-Efficacy and Academic Burnout

This study found that there is a significant negative correlation between college students’ academic self-efficacy and academic burnout, that is, the higher college students’ academic self-efficacy, the lower the degree of academic burnout. The hypothesis H1 was confirmed and is consistent with findings in the literature (Adams et al., 2020; Bulfone et al., 2020). This may be related to the fact that academic self-efficacy affects students’ evaluation of their own learning ability. When students accomplish the same tasks or face the same difficulties, students with lower academic self-efficacy will imagine the scene of failure, focus on their shortcomings, and imagine the potential difficulties to be even more terrible. So that they doubt their own learning ability, unwilling to put in more efforts and adopt corresponding strategies to solve the difficulties encountered in learning, and even resort to deliberately avoiding learning (improper behavior), which will inevitably spawn poor learning performance (low sense of achievement). There is no doubt that many characteristics of academic burnout are revealed. However, students with high academic self-efficacy will evaluate themselves more objectively and will overcome difficulties with positive attitudes, resulting in higher academic achievement and lower academic burnout. ÖZhan (2021) found that academic self-efficacy can reinforce positive emotions, such as motivation, happiness, enthusiasm, and so forth. With the increase of positive emotions, college students’ positive attitude towards classroom interaction will develop, and in turn, their perception of learning will change, leading to a reduction in academic burnout.

Mediating Role of Intrinsic Motivation

This study found that college students’ intrinsic motivation for learning plays a partial mediating role between academic self-efficacy and academic burnout, which means that academic self-efficacy can not only directly affect academic burnout but also indirectly affect academic burnout through intrinsic motivation. Hypothesis H2 is thus confirmed. Firstly, there exists a positive correlation between academic self-efficacy and intrinsic motivation, which is consistent with the findings of Alhadabi (2019). Secondly, there is a negative correlation between intrinsic motivation and academic burnout (Rubino et al., 2009). The improvement of intrinsic motivation helps learners to maintain positive attitudes and cultivate persistent characters, which will be helpful in grappling with various difficulties, and will effectively reduce the risk of academic burnout. On the contrary, those who lack intrinsic motivation do not devote enough efforts to learning, devoid of sense of achievement, ultimately encounter academic burnout. In addition, academic self-efficacy can indirectly affect academic burnout through intrinsic motivation. On one hand, college students with high academic self-efficacy foster confidence in their learning ability, which helps to enhance their intrinsic motivation and encourages them to study harder in order to achieve their goals, contriving to suffer less from academic burnout. On the other hand, college students with low academic self-efficacy barely obtain confidence in their learning ability. They lose enthusiasm and interest in learning more easily. When they are faced with mounting difficulties, they will develop insufficient motivation to learn and thus induce academic burnout. Therefore, effective measures may be taken to improve students’ academic self-efficacy to reduce academic burnout; meanwhile, intrinsic motivation may be enhanced to indirectly reduce academic burnout.

Mediating Role of Learning Engagement

In this study, it is found that college students’ learning engagement plays an intermediary role between academic self-efficacy and academic burnout, that is, academic self-efficacy can not only directly affect college students’ learning engagement but also indirectly affect academic burnout through learning engagement. Hypothesis H2 was confirmed. Academic self-efficacy positively affects learning engagement. Students with high self-efficacy are willing to put more effort into their learning and are better to cope with difficulties and challenges, thus enhancing learning engagement (Martin & Rimm-Kaufman, 2015). Sökmen (2021) found that students with high academic self-efficacy are able to make appropriate changes to the learning environment to enhance their engagement in learning. Alemayehu and Chen (2021) found that academic self-efficacy has a strong positive effect on learning engagement, while learning engagement has a negative impact on academic burnout, which is consistent with the findings of Liu (2018). In brief, learning engagement can bring a sequence of positive effects. Individuals with higher learning engagement are better at applying strategies and have a stronger sense of self-control, resulting in higher satisfaction and academic performance, reducing negative psychological emotions and the level of academic burnout.

The Serial Mediation of Intrinsic Motivation and Learning Engagement

This study found that intrinsic motivation and learning engagement play a significant role in the chain mediation between academic self-efficacy and academic burnout. The hypothetical H4 is confirmed. First of all, intrinsic motivation has a significant positive effect on learning engagement. The higher the learners’ intrinsic motivation, the higher the degree of learning engagement, which is consistent with the findings of Yin and Wang (2016). Intrinsic motivation is one of the core factors for college students to complete learning tasks. Higher intrinsic motivation helps to form strong learning intention, deep learning cognition, and positive emotions. In the process of learning, the higher the intrinsic motivation of college students, the stronger their learning intention, and the deeper understanding of the value of learning, which will help to improve the level of their learning engagement (Guay et al., 2008). This study also found that learning engagement significantly negatively predicts college students’ academic burnout, and learning engagement plays an intermediary role between intrinsic motivation and academic burnout. This is consistent with the findings of other literature (Akanni et al., 2019; Wang et al., 2021). College students with higher intrinsic motivation will actively participate in academic activities and pay more attention to solving academic problems. They will be full of vitality in classroom learning and are willing to devote their time and energy to academic activities. Moreover, this continuous enthusiasm for learning provides an important mechanism through which college students can ensure their concentration, vitality, and dedication to learning. For instance, self-determined motivation theory regards intrinsic motivation as the core factor for individuals to complete learning tasks, and when individuals have higher motivation to learn, they have higher concentration and enthusiasm to learn. Such learning behavior is self-determined and can reflect the individual’s emotion and cognition in the learning process (Deci & Ryan, 2000). Learning engagement and academic burnout are two extremes of an individual’s learning state. College students gain a sense of self-efficacy by being fully engaged in their studies, especially when engagement leads to higher academic achievement and satisfaction, which increases their self-confidence and reduces the level of burnout. This is consistent with Chen’s research, which concluded that a high level of engagement in learning can motivate students to learn and accomplish satisfactory academic performance, thereby reducing burnout (Chen, 2019; Liu et al., 2018). Overall, this study reveals that academic self-efficacy not only directly affects academic burnout but also indirectly affects academic burnout through intrinsic motivation and learning engagement. The chain-mediated effect of intrinsic motivation and learning input offers a new perspective for reducing college students’ academic burnout. Therefore, while improving the intrinsic motivation of college students, we can also intervene in their learning engagement to reduce academic burnout.

Research Limitations

First, the use of a cross-sectional design to determine the effects of college students’ self-efficacy on academic burnout implies that the causal relationship between the variables may not be fully established, and further longitudinal studies should be designed to explore the effects of college students’ psychosocial perception variables on their academic burnout. Second, in order to facilitate sampling, this study only investigated freshman and sophomore college students and did not include juniors and seniors, resulting in a sample whose representativeness will be affected to some extent. Third, since learning is an interaction of individuals, activities, and environments, more variable factors should be included in the future to further probe into the potential effects of these factors on college students’ academic burnout. In order to examine more deeply the mechanism of college students’ self-efficacy on academic burnout, future research should stratify students in different grades and majors and examine the impact of self-efficacy on academic burnout under the moderating effect of individualized factors. Third, it is necessary to explore in depth the characteristics of changes in intrinsic motivation, learning engagement, and changes in the predictive path of academic burnout in the context of students’ self-efficacy enhancement through longitudinal surveys and qualitative research methods, so as to provide intervention strategies for reducing the level of academic burnout among college students.

Conclusion

Our findings indicate that Chinese college students experience a certain degree of academic burnout, and also provide evidence for the mediating role of intrinsic motivation and learning engagement between academic self-efficacy and academic burnout. Improving college students’ academic self-efficacy, intrinsic motivation, and learning engagement can reduce academic burnout; on the other hand, academic self-efficacy indirectly affects academic burnout through the intermediary role of intrinsic motivation and learning engagement. In view of the above findings, we suggest that educators and college regulators pay attention to the problem of college students’ academic burnout. Specifically, teachers can use diverse teaching methods to guide students independent and exploratory learning and provide positive feedback in various forms through the goal achievement journey to boost confidence and satisfaction in students, so as to improve students’ self-efficacy in learning. In addition, teachers can tailor course resources according to different students’ characteristics and learning needs and maintain a certain level of challenge in the course content; create suitable teaching scenarios to pique interest in students as well as connect teaching materials closely to real life to encourage reflection; design progressive and meaningful questions to arouse their curiosity. Ultimately, college students’ motivation and commitment to learning can be improved to achieve the goals of reducing academic burnout and improving the quality of talent training.

Footnotes

Author Contributions

Xiao-Chun Wang contributed to searching process for studies and writing of the manuscript. Meng Zhang conceived of the study and participated in the design and performed the literature search and study selection. Jia-Xin Wang contributed to gathering information for the study and data extraction. All authors read and approved the final version of the manuscript and agree with the order of presentation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Zhejiang Provincial Philosophy and Social Sciences Planning Project (grant number 24NDJC228YBM), National College Students’ Innovation and Entrepreneurship Training Program (grant number 202210347036), and Zhejiang Provincial Education Scientific Planning Project (grant number 2023SCG045).

ORCID iD

Xiao-Chun Wang

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