Burned-Out Classroom Climate,Intrinsic Motivation,and Academic Engagement: Exploring Unresolved Issues in the Job Demand-Resource Model

Abstract

This study aimed to test the multilevel mediating effect of intrinsic motivation on the relationship between burned-out classroom climate and academic engagement and theoretically investigate the unresolved issues in the Job Demands-Resources model. Data were collected from 1015 high school students from 43 classes. Multilevel structural equation modeling indicated that a high level of burned-out classroom climate was related to a low level of academic engagement. In addition, intrinsic motivation mediated the path from burned-out classroom climate to academic engagement. Practical implications to improve classroom climate and academic engagement are discussed.

Keywords

academic burnout classroom climate intrinsic motivation academic engagement JD-R model multilevel structural equation modeling

The Korean National Report announced that the level of academic stress among Korean adolescents was highly ranked among Organization for Economic Cooperation and Development (OECD) countries, implying that schools in Korea put students under pervasive academic pressure with penalties to force them to prioritize higher academic performance (Cho et al., 2018). Most Koreans value academic elitism—they pursue an academic degree from a higher-ranking university, which they regard it potentially guarantee a privileged life of economic success and a superior workplace with outstanding working conditions such as company benefits. Teachers and parents in South Korea want students to prioritize academic work and invest their energy in academic achievement (Bong et al., 2008). These surroundings from social values of academic elitism leaded South Korean students to consider an academic degree from the top-tier university as a shortcut directly toward future success (Cho, 2017). Compared to other OECD countries, the Korean educational atmosphere highlighting academic degree from the top-tier university generated fierce competition for higher academic achievement, which means heavy burdens academic workload as well as psychological stress on adolescents. Students suffer from academic overload to do and academic achievement pressure from their teachers and parents to survive amid competition, which lay them exposed to academic demands (Song et al., 2015). Consequently, adolescents under those surroundings are the less satisfied with life and lowered academic engagement as well as at the high risk of academic burnout (Cho, 2017; Kim et al., 2010; Lee & Lee, 2018; Shin & Yu, 2014). To sum up, Korean educational feature, with prioritizing an academic degree issued by the top-tier university, could generate lots of academic work to do and cause parents or teachers to value higher GPA, which influence academic burnout as academic demands (Noh et al., 2013). This study aimed to investigate how burnout as a shared experience (i.e., burned-out climate) among students impact positive academic mental health (i.e., academic engagement) to gauge the effect of burned-out climate as a Korean-specific features on academic engagement among students.

Concept of Academic Burnout

Burnout is a psychological symptom in response to chronic job stressors (Maslach et al., 2001) and defined as “to fail and to wear out, or to become exhausted by making excessive demands on energy, strength, or resources” (Freudenberger, 1974, p. 159). Burnout was initially studied in individuals working in the human service sector including medical health care, social services, and education (Maslach & Jackson, 1981). The modification of the burnout general version (i.e., Maslach Burnout Inventory-General Survey: MBI-GS, Schaufeli, 1996), due to its more general definition and not including working with people, contributed to expand the scope of burnout research to all employees (Schaufeli, Salanova, et al., 2002). Exhaustion, for instance, assesses fatigue by an agency causing being exhausted by academic work, except people (Schaufeli, Martinez, et al., 2002). Students can be regarded as employees when considering that students have regular work such as attending classes and assignments (Lin & Huang, 2014) and get evaluation with GPA instead of salary among workers (Lee & Ahn, 2014). Students experience fierce competition for better academic achievement as if workers compete each other for higher salaries or better incentives. Studying or academic work is psychologically the same as work for employees, and despite being unpaid, they receive a grade from their school or appraisals from significant others. Namely, burnout of students refers to being exhausted by academic demands, distancing from academic work, and being incompetent (Schaufeli, Martínez, et al., 2002). Some studies (e.g., Cho et al., 2018; Zhang et al., 2007) replicated similarity of burnout in working context between academic context.

The term academic burnout indicates the emotionally exhausted state among students. Three factors structure of academic burnout with exhaustion, cynicism, and inefficacy was identified with cross-national samples including China, Germany, Netherlands, and Spain (Gumz et al., 2013; Hu & Schaufeli, 2009; Lee et al., 2009; Schaufeli, Martínez, et al., 2002). Exhaustion refers to a state of being psychologically drained due to overwhelming demands with a lack of resources. Cynicism indicates a cynical and unenthusiastic attitude toward studying. Inefficacy relates with taking a pessimistic view of an individual’s ability in academic process and outcomes (Schaufeli, Martínez, et al., 2002). A five-factor structure with two additional distinctive components (i.e., antipathy and anxiety) showed better explanation of academic burnout of South Korean adolescents. Antipathy refers to a state of emotionally disliking studying caused by previous unfavorable experiences (e.g., being judged). Anxiety describes the state of worrying about things related to academic work (Lee et al., 2009). A psychological characteristic among Korean adolescents that not enough academic reward compared to their academic effort stirs up academic burnout (Lee et al., 2012). This may reflect the Korean educational culture of competitive educational fever that appraises academic performance, instead of appreciating academic process including academic effort of adolescents. It seems to be plausible for two culture-distinctive factors to include antipathy and anxiety.

Concept of Academic Engagement

Conversely, the concept of engagement is also prominent in the field of positive psychology that focuses on an individual’s psychological functioning and strengths rather than on malfunctioning and weakness (Seligman & Csikszentmihalyi, 2014). In this context, engagement is an affective-cognitive state with persistent and pervasive characteristics. It is defined as a “positive, fulfilling, work-related state of mind featured by vigor, dedication, and absorption” (Schaufeli, Salanova, et al., 2002, p. 74). Vigor refers to high levels of energy and the willingness and ability to invest effort in one’s work. Dedication relates with enthusiasm, inspiration, pride, and willingness to challenge a certain task. Absorption indicates a state of being concentrated and happily engrossed in work and an individual feels carried away by their job (Schaufeli, Martínez, et al., 2002). The concept of engagement can be translated to students and has been studied as academic engagement (Cho et al., 2018; Fiorilli et al., 2014). Moreover, engagement in the school context is called school engagement and comprises behavior, emotion, and cognition. The behavioral aspect refers to positive participation in school activities (e.g., keeping the school rules and handing in academic tasks on time). The emotional aspect relates with affective reactions toward school (e.g., school attachment, sense of belongingness to school, and appreciation value of study). The cognitive aspect refers to cognitive thoughts when students learn and are educated (e.g., self-regulation, cognitive learning strategy) (Fredericks et al., 2004; Jimerson et al., 2003; Skinner et al., 2008). Taken together, academic engagement addresses only academic work itself, whereas school engagement comprehensively covers the student experience in school, including complying with rules and academic work. This study adopted academic engagement, not school engagement, as academic burnout includes the psychological state caused by academic work without school-related experiences, such as being rejected; burnout researchers consider academic engagement as an oppositional to academic burnout in a response to the academic work itself (Schaufeli & Taris, 2014).

Unresolved Six Issues in the JD-R Model

The present study focused on reciprocal causation and the multilevel issues, inspired by following six unresolved issues in the JD-R model. The revised Job Demands-Resources (JD-R) model encompasses burnout and engagement heuristically. The model suggested two unidirectional causal processes at the individual level: the health impairment process, where high demands cause strain and health impairment, and the motivational process, where high resources cause an increase in motivation and productivity. In these parallel processes, both burnout and engagement have mediating roles. However, the JD-R model has several unresolved issues such as (1) epistemological status: JD-R model is the heuristic and empirical findings-based model that specifies demands and resources, and its outcomes; however, it does not enlighten the mechanism of relations among components. (2) The nature of job demands and job resources: the negative relation between demands and engagement is not always found significant, occasionally be even positive (Crawford et al., 2010). Rather, the further study to investigate the definition of demands and resources is recommended. It would be more plausible and valid to consider challenges, instead of demands, as resources when with a positive effect on outcomes of engagement and burnout. (3) The role of personal resources: within the JD-R model, the personal resources should be placed in a various way as the third variables of moderator and mediators as well as antecedents. (4) The distinction between the health impairment and the motivational process: the JD-R model depicts the independent process of the motivational and the health impairment, which however remains connected between two pathways. For better understanding of two processes, it is recommended the broader perspective to compass the indirect and the direct effect of demands on engagement, with two parallel processes related each other or to examine the health impairment and the motivational process simultaneously. (5) Reciprocal causation and (6) multilevel issues (Schaufeli & Taris, 2014). The rest two issues of the inspiration of the current study were covered in the following.

Relations of Burnout to Engagement

Although the JD-R model previously proposed a unidirectional causal process, several longitudinal research demonstrated reciprocity in the motivational process, which implies that work or personal resources and engagement affect each other (Hakanen et al., 2008; Llorens et al., 2007; Schaufeli et al., 2009; Xanthopoulou et al., 2009). This spiral process in the motivational process implies that the JD-R model could have a dynamic nature beyond linear causation. Some researchers (e.g., Bakker & Demerouti, 2018; Schaufeli & Taris, 2014) recommended that further studies should uncover the dynamics between the motivational process and the health impairment process in the JD-R model. Salanova et al. (2010) also pointed out that the relationship between demands and engagement is complexed due to these spirals since engagement varies depending on other variables such as the characteristics of demands (Cavanaugh et al., 2000; LePine et al., 2005), individual perceptions of demands and resources (Bakker & Demerouti, 2007), and the combined effects of resources and demands (Bakker et al., 2007). Some empirical studies (e.g., Cho et al., 2018; Salanova et al., 2010; Salmela-Aro & Upadyaya, 2014) supported the cross-relations between the two parallel processes within the JD-R. A cross-sectional study (Cho et al., 2018) demonstrated that the academic demands rather positively fostered the academic engagement among high school students. A longitudinal research, however, from the ninth to the eleventh grade in school identified the negative causal relations from academic burnout to engagement in the framework of the JD-R (Salmela-Aro & Upadyaya, 2014). Academic burnout directly predicted academic engagement negatively 1 year later. That is, the mutual relations between the motivational process and the health impairment process seems to be plausible within the JD-R. Practical antecedents to impact academic engagement regardless of the direction remains uncovered, which needs further study (Bakker & Demerouti, 2018). Hence, the present study sought to investigate the direction how an antecedent impacts academic engagement by adopting academic burnout as demands.

Multilevel Approach to Burnout

The open nature of the JD-R model illustrates flexibility to extensively apply to various settings (e.g., academic setting), which casts practical as well as theoretical implications. Applying the JD-R model to an academic setting uncovered specific and general features of academic settings compared to work settings. Specifically, as mentioned earlier, demands could rather enhance academic engagement (Cho et al., 2018), and academic burnout longitudinally was detrimental to academic engagement as well as life satisfaction (Salmela-Aro & Upadyaya, 2014). Likewise, the heuristic nature of the JD-R model reflects the characteristics of a certain population. In the same vein, the characteristics of a certain population should be considered for comprehensive understanding mental health of them and for fundamental understanding JD-R model. That is, within the framework of the JD-R, in an attempt to studying and building practical intervention for academic engagement, the positive academic mental health, the group interaction among students that is the one of the major features of adolescents. For this, the present study applied the multilevel approach to burnout, as which the JD-R theorist already suggested (Bakker & Demerouti, 2018; Schaufeli & Taris, 2014).

The JD-R model assumes psychosocial processes including shared experiences within a team (i.e., collective burnout). Burnout at the team level effected an individual’s burnout at work as well as at school (Bakker et al., 2006; Kiuru et al., 2008). Hence, burnout may be contagious at a group level (Salmela-Aro, 2017). Burnout as a group experience seems to support the contagiousness of burnout which some populations go through: general practitioners (Bakker et al., 2001), care nurses (Bakker et al., 2005), and teachers (Meredith et al., 2020). The more the general practitioners perceive colleagues’ burnout complaint, the more emotional exhaustion they reported (Bakker et al., 2001). Further, general practitioners showed a tendency to develop into cynical attitude toward their works (i.e., cynicism) as results of coping exhaustion by putting themselves distant from. A replicative but clarified study among intensive care nurses (Bakker et al., 2005) supports the role of burned-out climate at the unit level as demands in the pathway to burnout at the individual level. Differences between intensive care units were greater than individual differences of burnout, of which sequential multilevel analysis showed that burnout at the group level significantly were responsible for the burnout of an individual nurse even after controlling for explained variances by obvious demands. That is, burnout as a collective perception can play a role as an antecedent (i.e., demands) to evoke burnout of an individual. The level of contagiousness of burnout depends on the strength of interpersonal relationship among teachers specified with frequency, multiplexity, and embeddedness (Meredith et al., 2020). That is, frequent interactions, which increases the exposure to other’s emotional feelings, predicted the contagion of burnout. For multiplexity, teachers, who had more interactions for the work-oriented goal unconsciously and the private interest with others consciously, suffered from burnout as other teachers did. For embeddedness, overlapped relations such as mutual friends showed stronger explanation of burnout contagion. Taken together, burnout contagion would instigate individual’s burnout, negatively related with engagement within an individual. The relational interactions within units which could boost to perceive unit members’ facial emotion account for burnout contagion. Applying this to the academic setting, the burnout contagion within units could correspond to the burned-out climate within the classroom as shared experiences. Supportive longitudinal studies (Kiuru et al., 2008, 2009) scaffolded this analogy that applies burnout contagion to burned-out climate formulated by interactions among students. Peer group influence as a peer interaction showed a significant prediction of similar educational track (Kiuru et al., 2009). The longitudinal trajectory of burnout even had come to draw a similar line over time in accordance with peer groups (Kiuru et al., 2008). To sum up, shared burnout experience by peer interactions is linked to burned-out climate in the classroom, which predicts educational track and the pattern of change in academic burnout.

Classroom climate is a shared belief and attitude among classmates that is developed through interactions in the classroom and affects students’ psychological functioning. Although not enough studies to address the obvious features of classroom climate, Wang and his colleagues (2020) pointed that classroom climate impacted academic outcomes, socio-emotional development, and behavioral problems based on their meta-analysis. Academic outcomes indicate achievement, motivation to learn, and academic mental health such as academic engagement (Wang & Degol, 2014; Wang et al., 2019). Socio-emotional development encompass depression, anxiety, and stress (Pössel et al., 2013). Behavioral problems refer to aggression, disruptive behaviors in the classroom (Kaplan et al., 2002; Thomas et al., 2011). The present study focused on the academic experience in the classroom and thus highlighted the academic outcomes of motivation to learn and academic engagement to test the effect of contagiousness of burnout as classroom climate in the population of adolescents. Hence, the current study could contribute to identify the function of the burned-out climate as demands which was conceptualized in the JD-R via intrinsic motivation in the relation with academic engagement.

Motivation in the Relation Between Burnout and Engagement

We reviewed the intrinsic motivation as personal resources to specify the relationship between burned-out climate as demands and academic engagement. In Self-Determination Theory (SDT; Ryan & Deci, 2002), motivation is on the continuum of developmental stages from highly internalized to highly externalized. There are five types of autonomy regulation. The first four are extrinsic: amotivation, an absence of intention to do something; external motivation, performance under external constraints; introjected motivation, motivated internally but not completely taken as one’s own; and identified motivation, performance in accordance with an individual’s goals or the importance of the task value. The final type of autonomy is intrinsic motivation, which is motivation to learn, prototypical-autonomous, and self-determined behavior. Intrinsic motivation represents personal resources affected by the classroom climate and positively related to engagement (Reeve et al., 2004). Considering that the key concept of autonomy is the willingness to make choices independently (Littlewood, 1996; Ryan & Deci, 2002), burnout can erode autonomy (Fernet et al., 2015; Pines, 1993). Next, motivational process essentially results in academic engagement (Skinner et al., 2009). Intrinsic motivation is a solid and pure component that facilitate positive outcomes due to the fulfillment and internal growth that an individual perceives. A student with intrinsic motivation tends to seek challenges rather than avoidance and to explore for learning and development (Karimi & Sotoodeh, 2020). Thus, SDT theorists regard that intrinsic motivation drives students to engage in academic work actively, the academic engagement (Ryan & Deci, 2009). Some empirical studies (e.g., Chau & Cheung, 2018; Froiland & Worrell, 2016) repeatedly pointed the strong causal relation from intrinsic motivation and academic engagement.

Taken together, personal resources within the JD-R model could play varying roles (e.g., mediators, moderators, demands, and resources) depending on the context, which needs to describe the specified role of personal resources aligned with the purpose of a certain study. In this study, intrinsic motivation is set as a mediator to specify the relationship between burned-out climate as demands and engagement for investigating the fundamental evidence to facilitate students to engage actively in school where excessive academic demands are inherent. Burned-out people tend to perceive more demands, while engaged people utilize their resources. In academic setting, the burned-out climate in the classroom would be perceived as a demanding external condition involving with academic emotional demands for students, which deteriorate one’s academic engagement directly or indirectly via negatively tapping autonomous motivation to learn. Further, this proposes the necessity to decompose the individual differences in engagement from the perspectives of the individual and the class levels simultaneously.

To verify the mediating effect of intrinsic motivation on the relationship between the burned-out climate and academic engagement, the current study adopted the Multilevel Structural Equational Model (MSEM). Figure 1 presents the research model, which represents the research question: Does burned-out climate influence academic engagement through intrinsic motivation?

Figure 1.

Research model and alternative model.

Method

Procedures and Participants

The sample consisted of 12^th grade South Korean high school students in urban communities. A total of 1015 students from 43 classes at eight schools completed a survey. A longitudinal study was conducted from July 2015 to February 2016; the data used in this study were second time-point data collected in October 2015. The time just before the Soo-Neung, the Korean Scholastic Aptitude Test, was considered the proper moment to measure academic burnout and academic engagement because this period has the highest level of academic stress and commitment. Students voluntarily participated the survey with teachers’ instruction at each school. The present study was approved by the Institutional Review Board at a certain university in South Korea. In all, 588 (57.9%) female students responded. As all the participants were in the same school year, the average age was 18 years.

Measures

Academic Burnout

To measure burned-out classroom climate, the Korean Academic Burnout Inventory (KABI; Lee et al., 2009) was used. This measurement is a modified and developed version of the Maslach Burnout Inventory–Student Survey (MBI-SS; Schaufeli, Salanova, et al., 2002). Three components constitute the MBI-SS—exhaustion, cynicism, and inefficacy. Two additional components of antipathy and anxiety reflect the educational context of Korean school-aged students. The previous research (Lee et al., 2009) demonstrated the five-factor structure of the KABI with a better fit for describing academic burnout in Korea. This measurement contains 25 items on five components, each consisting of five items: exhaustion (e.g., “I feel emotionally drained by my studies”), cynicism (e.g., “I am skeptical that studying will help in the future”), inefficacy (e.g., “I do not have the ability and qualities to study”), antipathy (e.g., “I hate studying”), and anxiety (e.g., “I am so worried about studying”). Participants rated the items on a 5-point Likert scale. In this study, at the individual level, the internal consistency (i.e., Cronbach’s alpha) was .89 for exhaustion, .90 for cynicism, .93 for inefficacy, .89 for antipathy, and .91 for anxiety.

Intrinsic Motivation

Korean Academic Self-Regulation Questionnaire (K-SRQ-A; Kim, 2002) was used to assess intrinsic motivation. The K-SRQ-A was developed for the Korean context by validating the Academic Self-Regulation Questionnaire (SRQ-A; Ryan, 2002) and integrating it with a modified Academic Motivation Scale (AMS; Hayamizu, 1997). The K-SRQ-A consists of five dimensions of autonomy: amotivation, extrinsic motivation, introjected motivation, identified motivation, and intrinsic motivation. Each components contains six items rated on a 6-point Likert scale. Only a single dimension of intrinsic motivation (e.g., “I study because it is interesting for me to study”) was used for this study, and the internal consistency was .92.

Academic Engagement

To assess the level of academic engagement, the Korean Academic Engagement Inventory (KAEI) was used (Lee & Lee, 2012). The KAEI is a developed and validated version of the Utrecht Work Engagement Scale-Students (UWES-S; Schaufeli, Salanova, et al., 2002). The KAEI consists of four subscales with items rated on a 5-point Likert scale: vigor (e.g., “I feel excited when I go to school for class”), dedication (e.g., “studying makes me feel engaged”), absorption (e.g., “while I am studying, I become immersed and focused”), and efficacy (e.g., “I am able to deal with challenging academic tasks”). Internal consistency was .89 for vigor, .88 for dedication, .89 for absorption, and .90 for efficacy.

Strategy of Analysis

Multi-Level Modeling (MLM) is a general method to analyze a mediational effect considering with nested data structure by decomposing the variance of dependent factors into within level (i.e., level 1) and between level (i.e., level 2) effects. However, it has the limitation that the components could be conflated as the effects of the mediator on dependent variables for the within and between components are constrained to be statistically equal (Zhang et al., 2009). Bias in the between components’ effect can produce misleading conclusions about the between-level indirect effect, although the within and between components are separated depending on the individual and between levels. Conversely, MSEM, an alternative multilevel analytic method, has recently been presented (Preacher et al., 2011). It allows for an examination of the direct and indirect effects separately at each level because all variables are separated at the between and within levels. Therefore, this study conducted MSEM to test the structural relationships, allowing for a differentiation of between and within levels—academic burnout was at the between level, while intrinsic motivation and academic engagement were at both between and within levels. Transformed Z-scores were used to interpret the results. The preliminary analyses for descriptive statistics and correlations were conducted using SPSS 21.0, and the variances among variables across classes were examined with Mplus. The present study employed PINT software for power analysis with multilevel design (Snijders, 2005; Snijders, & Bosker; 1993). MSEM was performed also with Mplus 7.4. To evaluate the model, we used the comparative fit index (CFI) and the Tucker–Lewis index (TLI), as the Chi-squared test is sensitive to sample size (Hong, 2000). CFI and TLI values above 0.90 are regarded as indicators of good fit (Browne & Cudeck, 1992).

Results

Table 1 presents the means, SDs, and correlations among the research variables at the between and within levels. The correlation coefficients for class level and student level were similar, but the former was higher. Academic burnout was negatively related to intrinsic motivation (r_(B) = −.65, r_(W) = −.39) and academic engagement (r_(B) = −.64, r_(W) = −.46), while intrinsic motivation was positively related to academic engagement (r_(B) = .90, r_(W) = .75). In addition, the intra-class correlations (ICCs) for intrinsic motivation and academic engagement were .114 and .140, respectively, meaning that 11.4% and 14.0% of the variance were explained by the class. The significant differences of these two variables at the class level implies that a multilevel analysis is necessary. To ensure the efficiency of the sample size of level 1 and level 2, a power analysis was conducted as Snijders (2005) proposed with PINT software. The result suggested that when the number of level 2 units is 43 (i.e., 43 classes), n = 15 is the required size of the level 2 unit for the current model (Cohen, 2013). Hence, 43 classes for the level 2 and 23-24 to level 2 each at the level 1 of 1015 is sufficient for multilevel modeling. Overall, the data used in this study were more suitable for multilevel analysis than single-level analysis to explain the mediation. Concretely, the model fit of the multilevel model (i.e., research model) showed a TLI of .84 and a CFI of .95, whereas in the single-level model (i.e., alternative model), the TLI and CFI were .60 and .62, respectively. Figure 1 shows the research model and the alternative model of this study.

Table 1.

Descriptive Statistics and Intercorrelations of Variables.

Variables	1	2	3	M	SD
1. Academic burnout	−	−.65^**	−.64^**	2.55	.77
2. Intrinsic motivation	−.39^**	−	.90^**	3.16	1.08
3. Academic engagement	−.46^**	.75^**	−	2.73	.76

p < .01.

Note. Above the diagonal is class level, below is student level.

Research Question Testing

To identify the research questions, we conducted a three-step mediation model analysis as per Baron and Kenny (1986). In the first step, we investigated the mediating effect of intrinsic motivation on the academic burnout—academic engagement path was analyzed with MSEM. The results were presented in Table 2. According to the mediation model analysis procedure of Baron and Kenny (1986), in the first step, we examined whether the independent variable predicted the mediator statistically significant, finding that the regression coefficient of academic burnout on intrinsic motivation was significant (β = −.342, p < .001). This indicates that the higher the academic burnout, the lower the student’s intrinsic motivation. In the second step, we examined whether the independent variable predicted the dependent variable, finding that the regression coefficient of academic burnout on academic engagement was significant (β = −.194, p < .001). This indicates that the higher the academic burnout, the lower the academic engagement of students. Finally, in the third step, we examined the decreased effects of the independent variable on the dependent variable with controlling for mediator, finding that the regression coefficient of academic burnout for academic engagement was not statistically significant when intrinsic motivation was controlled (β = .038, p > .05). In addition, the regression coefficients of intrinsic motivation for academic engagement were statistically significant for both between (β = 1.067, p < .001) and within groups (β = .639, p < .001), and the total (combined) regression coefficient was also statistically significant (β = 1.706, p < .001). Furthermore, the mediating effect of intrinsic motivation as analyzed by MSEM was also statistically significant (β = −.669, 95% CI [−1.117, −.221]). This means that intrinsic motivation fully mediates the effect of academic burnout on academic engagement. Therefore, the higher the level of academic burnout in the classroom, the lower the intrinsic motivation of individual students, which may reduce academic engagement. Multicollinearity was tested and found not to be problematic (Robinson & Schumacker, 2009).

Table 2.

The Mediating Effects of Intrinsic Motivation on the Relationship Between Class Stressors and Academic Engagement.

Step	Path	B	S.E.	p
1	Academic burnout (X) → Intrinsic motivation (M)	−0.342***	0.036	0.000
2	Academic burnout (X) → Academic engagement (Y)	−0.194***	0.024	0.000
3	Academic burnout (X) → Academic engagement (Y)	0.038	0.161	0.814
	Intrinsic motivation (M) → Academic engagement (Y): Between groups	1.067***	0.176	0.000
	Intrinsic motivation (M) → Academic engagement (Y): Within groups	0.639***	0.031	0.000
	Intrinsic motivation (M) → Academic engagement (Y): Total (between and within groups)	1.706***	0.173	0.000

Path	Mediating effect coefficient (b)	S.E.	P	95% Confidence interval
Path	Mediating effect coefficient (b)	S.E.	P	Lower	Upper
X → M → Y	−0.669 ***	0.174	0.000	−1.117	−0.221

*** p < .001.

Discussion

The present study was inspired by the two unresolved issues of the reciprocity between demands and engagement and the multilevel feature of the shared perceptions (e.g., group demands or resources) and the experiences (e.g., collectively burned-out team) in the JD-R model (Schaufeli & Taris, 2014). The current study explored the effect of burnout as a shared experience on student engagement and intrinsic motivation. Thus, this study investigated burnout and engagement by dividing the variances into student (i.e., level 1) and class (i.e., level 2) levels and exploring the effect of classroom climate (i.e., burned-out climate in the class level) on academic engagement at the student level. To this end, we introduced intrinsic motivation as a mediator to uncover the characteristics of engagement among students. Two resultant issues are discussed as follows.

First, each level showed a distinct variance in academic burnout and engagement. That means that the members of the same class share experiences in burnout and engagement. The present findings simply captured a momentary snapshot. Longitudinal studies of Finnish adolescents showed that students in the same group had similar levels of burnout and engagement over time (Kiuru et al., 2008, 2009). The consistent finding of burnout and engagement as a shared experience can be accounted for by peer-selection. For instance, a small burned-out group is likely to interact with and influence other groups within the same class, eventually resulting in every class member suffering from academic burnout; academic engagement may be contagious in a similar manner (Kiuru et al., 2008, 2009).

Next, a significant indirect effect of burned-out classroom climate on academic engagement via intrinsic motivation was revealed in the absence of a direct effect on academic engagement. These findings suggest that burned-out classroom climate could be detrimental to academic engagement such that a burned-out classroom climate undermines intrinsic motivation and leads to dysfunctional academic engagement. Applying the indirect effect of burnout at the class level to the school context, when a burned-out climate pervades a whole class and causes intrinsic motivation to deteriorate, engagement of students are apt to decrease. Moreover, the significance of the multilevel indirect effect (i.e., the class level) indicates that this sequential process also appears at the class level. For instance, a burned-out classroom climate is apt to thwart the intrinsic motivation of a class, which then aggravates the level of engagement of the whole class.

Practical Implications

These findings should be considered by school counselors and teachers. First, school staff members could deal with burnout contagion proactively based on the present findings in an effort to decrease demands of burned-out climate which is detrimental to academic engagement of an individual. Staff members involved in the school context, during the test period, should proactively prevent burnout to avoid burnout contagion (Kiuru et al., 2008). Failure to notice mild burnout in a small group within a class may gradually lead to burnout spreading to the whole class and potentially create an epidemic at the school level. Thus, all staff members, including teachers and school counselors, should pay close attention to even the subtle characteristics of academic-related psychological well-being in small peer groups, such as burnout (Salmela-Aro, 2017). It would help teachers decipher the dynamics among peers in a classroom that teachers pay attention to the class activities to facilitate peer interaction. Academic activities include group projects that group members are encouraged to discuss or to debate, and to make plans for successful team tasks or peer mentoring that a pair of mentor and mentee should teach academic knowledge. Without interacting with peers, a single burned-out student may not do something for stimulating the burned-out climate. Academic activities, however, instigate peer interaction and then function both in bright and the dark side despite of its beneficial to academic work. Just close and intensive attention to the students’ academic activities in the class enables teachers to observe out and to decide which small group needs early intervention for burnout. For this, teachers could utilize the burnout study findings to provide guideline enough to screen the student at the risk of burnout: low self-efficacy, not actively engaged in academic work, absent-minded, loss in attention and working memory, and absenteeism (Cho, 2020; Deligkaris et al., 2014; Sakakibara et al., 2020). Thus, by observing students when they are involved in academic activities, teachers could sense the small peer group at the risk of burnout. Additionally, a systemized and regular checking of students’ academic mental health could help teachers and counselors closely observe students and determine whether early intervention is needed (Balfanz & Byrnes, 2019; Oyen et al., 2020). Consensus to necessity for regularly investigating the academic mental health of students requires the school-organizational administrative support. Else, school counselors who is responsible for the mental health of students should monitor and be aware of contextual factors that students perceive within their own class, such as a burned-out climate.

Second, teachers accomplish raising the academic engagement among adolescents by simply stimulating one’s intrinsic motivation. Within academic class, teacher could provide a rationale, the reason students study the assigned to the certain academic class, which students demonstrate more interest in the class, learning, and academic engagement (Jang, 2008). That is, teachers can take care of the academic mental health including the burned-out climate and the academic engagement simply when they fulfill their own class. Additionally, an initial intervention to foster a small, engaged group could facilitate an engaged climate at the class level via the contagiousness of academic engagement among peer groups (Kiuru et al., 2009). For instance, teacher support of autonomy and psychological needs could cultivate a higher level of engagement, as could motivational interventions implemented by an external agency, such as researchers (Lazowski & Hulleman, 2016; Reeve et al., 2019). In sum, the present findings provide empirical evidence for early intervention, regardless of form (e.g., group or individual counseling), engagement and burnout, and contextual factors (e.g., burned-out climate) that counselors should explore to understand individual engagement.

Theoretical Implications

The theoretical implications of this study should be reviewed. This study verified a multilevel issue to identify the effects of burnout on individual engagement and found that intrinsic motivation functions as a bridge between burned-out climate at the group level and engagement at the individual level. Fundamentally, the identified multilevel approach to burnout implies that burnout is circular. That is, a burned-out student may spread burnout to their peer group, and their burned-out group may influence the burnout and engagement of individual students in the same class. In addition, taking the burned-out classroom climate as a demand in the form of shared experiences among students suggests reciprocity between burnout and engagement—a mutual-dynamic aspect of psychological well-being in an academic setting. The revised JD-R model includes two distinctive processes—a health impairment process from demands to burnout and a motivational process from resources to engagement. However, this should be reviewed. Instead of placing engagement on a continuum in opposition to burnout, considering them as multidimensional instead of unidimensional may be more beneficial. There may be students, for instance, who are high in burnout and engagement simultaneously. Research to support the multidimensionality of burnout and engagement has reported the emergence of adolescent groups with high level of both burnout and engagement (Salmela-Aro et al., 2016; Tuominen-Soini & Salmela-Aro, 2014), an effect seen in groups of workers as well (Mäkikangas et al., 2017). However, there are few empirical studies on the stability of an engaged/burned-out group. Further study on the multidimensionality of these two factors is needed to help people in the school context understand and devise appropriate interventions for student mental health. For this purpose, researchers, including teachers and counselors, should understand the interactive dynamics between burnout and engagement.

Limitations and Further Study

Several important aspects should be investigated further. First, potential third factors that might influence student engagement were not considered. Given the contagiousness of motivation, it may also spread between teachers and students at cross-level as well as between students (Radel et al., 2010; Salmela-Aro, 2017). This study regarded the aggregated motivation of students as the perceived motivation of the whole class, ignoring the effects of the teacher on motivation. To explain the variance in engagement at the individual and class levels, an advanced multilevel approach that includes teachers (i.e., motivation and engagement of both teachers and students) should be considered in future studies. Second, the survey in this study was conducted just before the special exam called Soo-Neung, the Korean Scholastic Aptitude Test. This means that the results of this study may be different from the usual student experience. Therefore, the findings should be interpreted in this specific context and require caution for generalization. Third, this study was implemented based on a cross-sectional analysis to determine the directionality of the reciprocal relations between engagement and burnout found in previous studies. Hence, a longitudinal study to explore the reciprocity between factors with multilevel perspectives is still needed.

Footnotes

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 Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF- 2020S1A5A2A01043871)

ORCID iDs

Soohyun Cho

Sang Min Le

Data Availability

The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.

Author Biographies

Min Young Lee is a research professor in the Brain and Motivation Research Insttitute at Korea University. She is interested in academic emotion, school counseling, and student's development.

Soohyun Cho is an assistant professor in the Department of Education at the Keimyung University. Her research interests include burnout measurement, technology in counseling, and digital therapeutics.

Sang Min Lee is a Professor in the Department of Education at the Korea University. His research interests include counseling process, stress management, and policy issues in counseling.

References

Bakker

A. B.

Demerouti

(2007). The job demands‐resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115

Bakker

A. B.

Demerouti

(2018). Multiple levels in job demands-resources theory: Implications for employee well-being and performance. In Diener

Oishi

Tay

(Eds), Handbook of well-being: DEF Publishers. nobascholar.com

Bakker

A. B.

Emmerik

H. v.

Euwema

M. C.

(2006). Crossover of burnout and engagement in work teams. Work and Occupations, 33, 464–489. https://doi.org/10.1177/0730888406291310

Bakker

A. B.

Hakanen

J. J.

Demerouti

Xanthopoulou

(2007). Job resources boost work engagement, particularly when job demands are high. Journal of Educational Psychology, 99(2), 274–284. https://doi.org/10.1037/0022-0663.99.2.274.

Bakker

A. B.

Le Blanc

P. M.

Schaufeli

W. B.

(2005). Burnout contagion among intensive care nurses. Journal of Advanced Nursing, 51(3), 276–287. https://doi.org/10.1111/j.1365-2648.2005.03494.x

Bakker

A. B.

Schaufeli

W. B.

Sixma

H. J.

Bosveld

(2001). Burnout contagion among general practitioners. Journal of Social and Clinical Psychology, 20(1), 82–98. https://doi.org/10.1521/jscp.20.1.82.22251

Balfanz

Byrnes

(2019). Early warning indicators and intervention systems: State of the field. In Fredericks

J. A.

Reschley

A. L.

Christenson

S. L.

(Eds), Handbook of student engagement interventions (pp. 45–55). Academic Press.

Baron

R. M.

Kenny

D. A

(1986). The moderator -mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Bong

Kim

Shin

J. Y.

Lee

(2008). Exploration of socio-cultural factors affecting Korean adolescents’ motivation. Korean Journal of Psychological and Social Issues, 14(1), 319–348.

10.

Browne

M. W.

Cudeck

(1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

11.

Cavanaugh

M. A.

Boswell

W. R.

Roehling

M. V.

Boudreau

J. W.

(2000). An empirical examination of self-reported work stress among U.S. managers. Journal of Applied Psychology, 85(1), 65–74. https://doi.org/10.1521/10.1037/0021-9010.85.1.65

12.

Chau

Cheung

(2018). Academic satisfaction with hospitality and tourism education in Macao: the influence of active learning, academic motivation, and student engagement. Asia Pacific Journal of Education, 38(4), 473–487. https://doi.org/10.1080/02188791.2018.1500350

13.

Cho

(2017). Pattern of changes in academic burnout across soo-NeungDoctoral dissertation. Korea University.

14.

Cho

(2020). A preliminary validation study for the Korean version of the burnout assessment tool(K-BAT). Korean Journal of Industrial and Organizational Psychology, 33(4), 461–499. https://doi.org/10.29318/KER.24.3.1

15.

Cho

Lee

(2018). Study on academic burnout and engagement among highschool students: Applying the job demands-resources model. The Korea Educational Review, 24(3), 1–26. https://doi.org/10.29318/KER.24.3.1

16.

Cohen

(2013). Statistical power analysis for the behavioral sciences. Routledge.

17.

Crawford

E. R.

LePine

J. A.

Rich

B. L.

(2010). Linking job demands and resources to employee engagement and burnout: A theoretical extension and meta-analytic test. Journal of Applied Psychology, 95, 834–848. https://doi.org/10.1037/a0019364

18.

Deligkaris

Panagopoulou

Montgomery

A. J.

Masoura

(2014). Job burnout and cognitive functioning: a systematic review. Work & Stress, 28(2), 107–123. https://doi.org/10.1080/02678373.2014.909545

19.

Fernet

Austin

Trépanier

S. G.

Dussault

(2015). How do job characteristics contribute to burnout? Exploring the distinct mediating roles of perceived autonomy, competence, and relatedness. European Journal of Work and Organizational Psychology, 22(2), 123–137. https://doi.org/10.1080/1359432X.2011.632161

20.

Fiorilli

Galimberti

De Stasio

Di Chiacchio

Albanese

(2014). School burnout inventory with Italian students of high school. Psicologia Clinica Dello Sviluppo, 18(3), 403–424. https://doi.org/10.1449/78365

21.

Fredericks

J. A.

Blumenfeld

P. C.

Paris

A. H.

(2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109. https://doi.org/10.3102/00346543074001059

22.

Freudenberger

H. J.

(1974). Staff burn-out. Journal of Social Issues, 30, 159–165. https://doi.org/10.1111/j.1540-4560.1974.tb00706.x.

23.

Froiland

J. M.

Worrell

F. C.

(2016). Intrinsic motivation, learning goals, engagement, and achievement in a diverse high school. Psychology in the Schools, 53(3), 321–336. https://doi.org/10.1002/pits.21901

24.

Gumz

Erices

Brähler

Zenger

(2013). Factorial structure and psychometric criteria of the German translation of the Maslach Burnout Inventory--Student Version by Schaufeli et al. (MBI-SS). Psychotherapie, Psychosomatik, Medizinische Psychologie, 63(2), 77–84. https://doi.org/10.1055/s-0032-1323695

25.

Hakanen

J. J.

Perhoniemi

Toppinen-Tanner

(2008). Positive gain spirals at work: From job resources to work engagement, personal initiative and work-unit innovativeness. Journal of Vocational Behavior, 73, 78–91. https://doi.org/10.1016/j.jvb.2008.01.003

26.

Hayamizu

(1997). Between intrinsic and extrinsic motivation: Examination of reasons for academic study based on the theory of internalization. Japanese Psychological Research, 39(2), 98–108.

27.

Hong

(2000). The criteria for selecting appropriate fit indices in structural equation modeling and their rationales. The Journal of Clinical Psychology, 19(1), 161–177.

28.

Schaufeli

W. B.

(2009). The factorial validity of the maslach burnout inventory-student survey in China. Psychological Reports, 105(2), 394–408. https://doi.org/10.2466/PR0.105.2.394-408

29.

Jang

(2008). Supporting students’ motivation, engagement, and learning during an uninteresting activity. Journal of Educational Psychology, 100(4), 798–811. https://doi.org/10.1037/a0012841

30.

Jimerson

S. R.

Campos

Greif

J. L.

(2003). Toward an understanding of definitions and measures of school engagement and related terms. The California School Psychologist, 8, 7–27. https://doi.org/10.1007/BF03340893

31.

Kaplan

Gheen

Midgley

(2002). Classroom goal structure and student disruptive behaviour. British journal of educational psychology, 72(2), 191–211. https://doi.org/10.1348/000709902158847.

32.

Karimi

Sotoodeh

(2020). The mediating role of intrinsic motivation in the relationship between basic psychological needs satisfaction and academic engagement in agriculture students. Teaching in Higher Education, 25(8), 959–975. https://doi.org/10.1080/13562517.2019.1623775

33.

Kim

A. Y.

(2002). Validation of taxonomy of academic motivation based on self-determination theory. Korean Journal of Educational Psychology, 16(4), 169–187.

34.

Kim

Lee

Shin

Park

Lee

(2010). The relationship between academic demand and academic burnout: The role of control. The Korean Journal of School Psychology, 7(1), 69–84. https://doi.org/10.16983/kjsp.2010.7.1.69

35.

Kiuru

Aunola

Nurmi

J.-E.

Leskinen

Salmela-Aro

(2008). Peer group influence and selection in adolescents’ School Burnout: A longitudinal study. Merrill-Palmer Quarterly, 54(1982), 23–55. https://doi.org/10.1353/mpq.2008.0008.

36.

Kiuru

Nurmi

J.-E.

Aunola

Salmela-Aro

(2009). The role of peer groups in adolescents’ educational trajectories. European Journal of Developmental Psychology, 6(5), 521–547. https://doi.org/10.1080/17405620701330599

37.

Lazowski

R. A.

Hulleman

C. S.

(2016). Motivation interventions in education. Review of Educational Research, 86(2), 602–640. https://doi.org/10.3102/0034654315617832

38.

Lee

S. M.

Ahn

S. H.

(2014). Theoretical review on burnout symptoms in education. Korean Medical Education Review, 16(20), 57–66. https://doi.org/10.17496/kmer.2014.16.2.057

39.

Lee

S. M.

(2012). The development and validation of Korean academic engagement inventory (KAEI). Korean Journal of Educational Methodology Studies, 24(1), 131–147. https://doi.org/10.6115/fer.2014.52.1.43

40.

Lee

M. Y.

Lee

S. M.

(2018). The effects of psychological maladjustments on predicting developmental trajectories of academic burnout. School Psychology International, 39(3), 217–233. https://doi.org/10.1177/0143034318766206

41.

Lee

Y. B.

Lee

S. M.

Lee

(2009). Development of Korean academic burnout scale. Korea Educational Review, 15(3), 59–78. https://doi.org/10.6115/fer.2014.52.1.43

42.

Lee

Puig

Lee

S. M.

(2012). The effect of the demand control and effort reward imbalance models on the academic burnout of Korean adolescents. Asia Pacific Journal of Education, 32(1), 113–123. https://doi.org/10.1080/02188791.2012.655373.

43.

LePine

J. A.

Podsakoff

N. P.

LePine

M. A.

(2005). A meta-analytic test of the challenge stressor-hindrance stressor framework: An explanation for inconsistent relationships among stressors and performance. Academy of Management Journal, 48(5), 764–775. https://doi.org/10.2307/20159696

44.

Lin

S.-H.

Huang

Y.-C.

(2014). Life stress and academic burnout. Active Learning in Higher Education, 15(1), 77–90. https://doi.org/10.1177/1469787413514651

45.

Littlewood

(1996). “Autonomy”: An anatomy and a framework. System, 24(4), 427–435. https://dx-doi-org.web.bisu.edu.cn/10.1016/s0346-251x(96)00039-5

46.

Llorens

Schaufeli

Bakker

Salanova

(2007). Does a positive gain spiral of resources, efficacy beliefs and engagement exist? Computers in Human Behavior, 23(1), 825–841. https://dx-doi-org.web.bisu.edu.cn/10.1016/j.chb.2004.11.012

47.

Mäkikangas

Hyvönen

Feldt

(2017). The energy and identification continua of burnout and work engagement: Developmental profiles over eight years. Burnout Research, 5(C), 44–54. https://doi.org/10.1016/j.burn.2017.04.002

48.

Maslach

Jackson

S. E.

(1981). The Maslach burnout inventory. CA Consulting Psychologists Press.

49.

Maslach

Schaufeli

W. B.

Leiter

M. P.

(2001). Job burnout. Annual Review of Psychology, 52, 397–422. https://doi.org/10.1146/annurev.psych.52.1.397

50.

Meredith

Schaufeli

Struyve

Vandecandelaere

Gielen

Kyndt

(2020). ‘Burnout contagion’ among teachers: A social network approach. Journal of Occupational and Organizational Psychology, 93(2), 328–352. https://doi.org/10.1111/joop.12296

51.

Noh

Shin

Lee

S. M.

(2013). Developmental process of academic burnout among Korean middle school students. Learning and Individual Differences, 28(2013), 82–89. https://doi.org/10.1016/j.lindif.2013.09.014

52.

Oyen

K. A.

Eklund

von der Embse

(2020). The landscape of advocacy in public schools: The role of school psychologists. Psychological Services, 17(S1), 81–85. https://doi.org/10.1037/ser0000373

53.

Pines

A. M

(1993). Burnout: An existential perspective. In Schaufeli

W.B.

Maslach

Marek

(Eds), Professional burnout: Recent developments in theory and research (pp. 33–52). Taylor & Francis.

54.

Pössel

Rudasill

K. M.

Sawyer

M. G.

Spence

S. H.

Bjerg

A. C.

(2013). Associations between teacher emotional support and depressive symptoms in Australian adolescents: A 5-year longitudinal study. Developmental Psychology, 49(11), 2135–2146. https://doi.org/10.1037/a0031767.

55.

Preacher

K. J.

Zhang

Zyphur

M. J.

(2011). Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM. Structural Equation Modeling: A Multidisciplinary Journal, 18(2), 161–182. https://doi.org/10.1080/10705511.2011.557329

56.

Radel

Sarrazin

Legrain

Wild

T. C.

(2010). Social contagion of motivation between teacher and student: Analyzing underlying processes. Journal of Educational Psychology, 102(3), 577–587. https://doi.org/10.1037/a0019051

57.

Reeve

Cheon

S. H.

Jang

H.-R.

(2019). A Teacher-focused intervention to enhance students’ classroom engagement. In Fredericks

J. A.

Reschley

A. L.

Christenson

S. L.

(Eds), Handbook of student engagement interventions (pp. 87–102). Academic Press.

58.

Reeve

Jang

Carrell

Jeon

Barch

(2004). Enhancing students’ engagement by increasing teachers’ autonomy support. Motivation and Emotion, 28(2), 147–169. 10.1023/B:MOEM.0000032312.95499.6f

59.

Robinson

Schumacker

R. E.

(2009). Interaction effects: Centering, variance inflation factor, and interpretation issues. Multiple Linear Regression Viewpoints, 35(1), 6–11.

60.

Ryan

R. M.

(2002). The Self-Regulation Questionnaire (SRQ-A) http://www.psych.rochester.edu/SDT/measures/self-acad.html.

61.

Ryan

R. M.

Deci

E. L.

(2002). Overview of self-determination theory: An organismic dialectical perspective. In Deci

E. L.

Ryan

R. M.

(Eds), Handbook of self-determination research (pp. 3–33). University of Rochester.

62.

Ryan

R. M.

Deci

E. L.

(2009). Promoting Self-Determined School Engagement. In Wentzel

Wigfield

Miele

(Eds), Handbook of motivation at school (pp. 171–195): Routledge.

63.

Sakakibara

Shimazu

Toyama

Schaufeli

W. B.

(2020). Validation of the Japanese version of the burnout assessment tool. Frontiers in Psychology, 11, 1819. https://doi.org/10.3389/fpsyg.2020.01819

64.

Salanova

Schaufeli

W. B.

Xanthopoulou

Bakker

A. B.

(2010). The gain spiral of resources and work engagement: Sustaining a positive worklife. In Bakker

A. B.

Leiter

M. P.

(Eds), Work engagement: A handbook of essential theory and research (pp. 118–131). Psychology Press.

65.

Salmela-Aro

(2017). Dark and bright sides of thriving - school burnout and engagement in the Finnish context. European Journal of Developmental Psychology, 14(3), 337–349. https://doi.org/10.1080/17405629.2016.1207517

66.

Salmela-Aro

Moeller

Schneider

Spicer

Lavonen

(2016). Integrating the light and dark sides of student engagement using person-oriented and situation-specific approaches. Learning and Instruction, 43, 61–70. https://doi.org/10.1016/j.learninstruc.2016.01.001

67.

Salmela‐Aro

Upadyaya

(2014). School burnout and engagement in the context of demands–resources model. British Journal of Educational Psychology, 84(1), 137–151. https://doi.org/10.1111/bjep.12018.

68.

Schaufeli

W. B.

(1996). Maslach Burnout Inventory-General Survey (MBIGS). Maslach burnout inventory manual.

69.

Schaufeli

W. B.

Bakker

A. B.

Van Rhenen

(2009). How changes in job demands and resources predict burnout, work engagement, and sickness absenteeism. Journal of Organizational Behavior, 30, 893–917. https://doi.org/10.1002/job.595

70.

Schaufeli

W. B.

Martínez

I. M.

Pinto

A. M.

Salanova

Bakker

A. B.

(2002a). Burnout and engagement in university students. Journal of Cross-Cultural Psychology, 33(5), 464–481. https://doi.org/10.1177/0022022102033005003

71.

Schaufeli

W. B.

Salanova

González-romá

Bakker

A. B.

(2002b). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3, 71–92. https://doi.org/10.1023/A:1015630930326

72.

Schaufeli

W. B.

Taris

T. W.

(2014). A critical review of the job demands-resources model: Implications for improving work and health. In Bauer

G. F.

Hämmig

(Eds), Bridging occupational, organizational and public health: A transdisciplinary approach (pp. 43–68). Springer.

73.

Seligman

M. E. P.

Csikszentmihalyi

(2014). Positive psychology: An introduction. In Flow and the foundations of positive psychology: The collected works of mihaly Csikszentmihalyi (pp. 279–298). Springer.

74.

Shin

H. J.

K. L.

(2014). Connectedness as a mediator of the relationship between academic stress and academic burnout. Korean Journal of Youth Studies, 21(1), 367–393.

75.

Skinner

Furrer

Marchand

Kindermann

(2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology, 100, 765–781.

76.

Skinner

E. A.

Kindermann

T. A.

Connell

J. P.

Wellborn

J. G.

(2009). Engagement and Disaffection as Organizational Constructs in the Dynamics of Motivational Development. In Wenzel

K. R.

Wigfield

(Eds), Handbook of Motivation at School (pp. 223–245). Routledge.

77.

Snijders

T.A.B.

(2005). Power and sample size in multilevel linear models. In Everitt

B.S.

Howell

D.C.

(Eds), Encyclopedia of Statistics in Behavioral Science (Vol. 3, pp. 1570–1573): Wiley.

78.

Snijders

T. A. B.

Bosker

R. J.

(1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237–259. https://doi.org/10.3102/10769986018003237

79.

Song

Bong

Lee

Kim

S.-i.

(2015). Longitudinal investigation into the role of perceived social support in adolescents' academic motivation and achievement. Journal of Educational Psychology, 107(3), 821–841. https://doi.org/10.1037/edu0000016

80.

Thomas

D. E.

Bierman

K. L.

Powers

C. J.

(2011). The influence of classroom aggression and classroom climate on aggressive-disruptive behavior the influence of classroom aggression and classroom climate on aggressive–disruptive behavior. Child Development, 82(3), 751–757. https://doi.org/10.1111/j.1467-8624.2011.01586.x

81.

Tuominen-Soini

Salmela-Aro

(2014). Schoolwork engagement and burnout among Finnish high school students and young adults: Profiles, progressions, and educational outcomes. Developmental Psychology, 50(3), 649–662. https://doi.org/10.1037/a0033898

82.

Wang

M.-T.

Degol

(2014). Staying engaged: Knowledge and research needs in student engagement. Child Development Perspectives, 8(3), 137–143. https://doi.org/10.1111/cdep.12073

83.

Wang

M.-T.

Degol

J. L.

Henry

D. A.

(2019). An integrative development-in-sociocultural-context model for children’s engagement in learning. American Psychologist, 74(9), 1086–1102. https://doi.org/10.1037/amp0000522

84.

Wang

M.-T.

L. Degol

Amemiya

Parr

Guo

(2020). Classroom climate and children’s academic and psychological wellbeing: A systematic review and meta-analysis. Developmental Review, 57(61), 100912. https://doi.org/10.1016/j.dr.2020.100912

85.

Xanthopoulou

Bakker

A. B.

Demerouti

Schaufeli

W. B.

(2009). Reciprocal relationships between job resources, personal resources, and work engagement. Journal of Vocational Behavior, 74(3), 235–244. https://doi.org/10.1016/j.jvb.2008.11.003

86.

Zhang

Gan

Cham

(2007). Perfectionism, academic burnout and engagement among Chinese college students: A structural equation modeling analysis. Personality and Individual Differences, 43(6), 1529–1540. https://doi.org/10.1016/j.paid.2007.04.010

87.

Zhang

Zyphur

M. J.

Preacher

K. J.

(2009). Testing multilevel mediation using hierarchical linear models. Organizational Research Methods, 12(4), 695–719. https://doi.org/10.1177/1094428108327450