The social contagion of student engagement in school

Abstract

Student engagement is a strong predictor of academic achievement and overall school success. Much of the research on engagement has focused on the role of personal psychological antecedents and social factors related to one’s teachers. Relatively fewer studies have focused on the influence of one’s classmates. Drawing on prior work on social contagion, this study aimed to examine whether classmates’ engagement influences one’s engagement. Questionnaires were administered to 848 secondary school students nested within 30 classes. Two waves of data were collected seven months apart. Multilevel modelling showed that a student’s Time 2 engagement was positively predicted by his/her classmates’ engagement at Time 1, providing evidence for the social contagion of engagement. These findings held even after controlling for autoregressor effects and other relevant covariates such as demographic factors and achievement goals. Our results suggest that students’ engagement in school is contagious and could be transmitted among classmates.

Keywords

social contagion student engagement engagement contagion

The social contagion of student engagement in school

Engagement in school refers to the quality of how students connect or involve themselves in school activities (Skinner, Kindermann, & Furrer, 2009). Student engagement is a crucial predictor of students’ achievement and is positively associated with numerous adaptive outcomes such as academic performance (Lee, 2014), school completion (Finn, 1989), optimal learning (Fredricks, Blumenfeld, & Paris, 2004), and even occupational outcomes after graduation (Abbott-Chapman et al., 2014). Student engagement has also been found to buffer students against absenteeism, dropout, academic adversity, failure, and a wide range of other maladaptive outcomes (Archambault, Janosz, Morizot, & Pagani, 2009; Skinner & Pitzer, 2012). Given its importance and malleability, researchers have become increasingly interested in understanding the antecedents of engagement.

Much of the existing research on engagement has focused on the role of personal and social factors in facilitating or inhibiting engagement. For personal factors, past studies have focused on motivation (Skinner et al., 2009; Wang & Eccles, 2013), academic emotions (Pekrun & Linnenbrink-Garcia, 2012) and personality traits (Komarraju & Karau, 2005) among others. For social factors, existing research has focused mostly teacher social support (Wang & Holcombe, 2010; Wentzel, Battle, Russell, & Looney, 2010) and student-teacher relationships (Gutiérrez & Tomás, 2019; Klem & Connell, 2004; Pianta, Hamre, & Allen, 2012; Quin, 2017).

However, the role of one’s classmates in facilitating or inhibiting engagement has been given relatively less attention (cf. Wang, Kiuru, Degol, & Salmela-Aro, 2018). One way that peers could influence student engagement is through the process of social contagion which refers to the involuntary “catching” of other’s characteristics with whom one is connected with, including attitudes, beliefs, and behaviors (Christakis & Fowler, 2013; Levy & Nail, 1993). Hence, this study aimed to examine the social contagion of student engagement, that is, whether the engagement of one’s classmates could influence one’s engagement. To address this aim, we measured students’ engagement at Time 1 and Time 2 and tested whether the engagement of one’s classmates at Time 1 predicts one’s engagement at Time 2. Below we reviewed relevant literature on the social contagion of student engagement.

Student engagement in school

Engagement is a multi-faceted construct (Fredricks et al., 2004). It is generally conceptualized as comprised of behavioral, emotional, and cognitive components (Archambault et al., 2009; Skinner et al., 2009). We focused on behavioral and emotional engagement in this study because others more readily perceive both types of engagement. Behavioral engagement refers to on-task attention, persistence, and involvement in academic and learning tasks. Emotional engagement refers to the positive emotional states as enthusiasm, interest, and enjoyment (Fredricks et al., 2004; Skinner et al., 2009). While student engagement and school engagement can at times be used interchangeably, we used the former term for parsimony and accuracy (see Appleton, Christenson, & Furlong, 2008). Hence, we purport that an engaged learner actively participates in various activities in school and expresses positive emotions towards these learning activities and tasks (Fredricks et al., 2004).

Among the antecedents of student engagement, past studies have focused on the critical role played by personal factors. A wide range of psychological antecedents such as motivational internalization (Froiland & Worrell, 2016), interest (Patall, Vasquez, Steingut, Trimble, & Pituch, 2016), self-efficacy beliefs (Chong, Liem, Huan, Kit, & Ang, 2018), achievement goals (Datu & Park, 2019; Duchesne, Larose, & Feng, 2019), implicit theories of ability (Dupeyrat & Mariné, 2005), and personality traits (Komarraju & Karau, 2005) among others have been identified as key predictors of student engagement.

Researchers have also focused on the role of social factors, especially those related to teachers as crucial predictors of student engagement. For example, meta-analytic studies by Roorda and colleagues (2017; 2011) found that teacher-student relationships were closely related to engagement. Wang and Holcombe (2010) found that teacher’s endorsement of learning-related goals and autonomy-supportive teaching influenced students’ engagement and achievement in school (see also Wang & Eccles, 2013). Students who have warm and caring relationships with their teachers (King, 2015b; Skinner et al., 2009; Wang & Eccles, 2012) and those who perceive their teachers to be more autonomy-supportive are also more engaged (Gutiérrez & Tomás, 2019). Hence, engagement can be conceptualized as being both shaped by personal and social-contextual factors (Connell & Wellborn, 1991; Furrer, 2010; Skinner & Belmont, 1993). In this study, however, we focus exclusively on the potential role of social contagion among peers which we elaborate below.

Social contagion

Aside from teachers, peers also play an essential role in developing student engagement (Estell & Perdue, 2013; Juvonen, Espinoza, & Knifsend, 2012). Peers can influence student engagement over time (Wang et al., 2018). Past studies have shown that peer support is closely associated with higher levels of engagement (see Wentzel, Muenks, McNeish, & Russell, 2017, 2018). Peer support is usually defined as the degree of social support that students receive from their peers. This support can be in the form of emotional support such as when students perceive their peers to care for and value them or academic/instrumental support such as when students receive academic-related help from their peers (Kiefer, Alley, & Ellerbrock, 2015; Wentzel et al., 2017, 2018).

Aside from peer support, another way through which peers can influence each other is through social contagion. Social contagion broadly refers to “the spread of affect, attitude, or behavior from person A (the ‘initiator’) to person B (the ‘recipient’)” (Levy & Nail, 1993, p. 275). Chartrand and Lakin (2013) emphasized that social contagion occurs rather frequently and that it can happen either consciously or unconsciously (see also Laurin, 2016). Though both peer support and social contagion are forms of social influence, social contagion can be distinguished from peer support because, at its core, social contagion is a priming phenomenon and refers to transmission within the same psychological domain (Bakker & Demerouti, 2013; Demerouti, 2012; Laurin, 2016).

Social contagion has also been termed as spillover effects, social interaction effects, or peer effects, among others (Bakker & Demerouti, 2013; Eisenberg, Golberstein, Whitlock, & Downs, 2013). Social contagion has been documented for several psychological variables including goals (Aarts, Gollwitzer, & Hassin, 2004; Loersch, Aarts, Payne, & Jefferis, 2008), motivation (Burgess, Riddell, Fancourt, & Murayama, 2018; Wild & Enzle, 2002), mindsets (King, 2020), flow (Culbertson, Fullagar, Simmons, & Zhu, 2015), stress (Oberle & Schonert-Reichl, 2016), and emotional states (Eisenberg et al., 2013; King & Datu, 2017) among others. However, the social contagion of student engagement has seldom been investigated.

Social contagion of student engagement

Engagement contagion has most often been explored in work contexts (e.g., work settings; Bakker, Emmerik, & Euwema, 2006; Bakker, Westman, van Emmerik, & Demerouti, 2009; Torrente, Salanova, & Llorens, 2013; Totterdell, 2000). For example, past studies have found that individuals nested within teams that were highly engaged became more engaged themselves (Bakker et al., 2006). Engagement contagion has been documented among a wide range of populations such as teachers (Bakker, 2005), athletes (Totterdell, 2000), corporate employees (Westman, Bakker, Roziner, & Sonnentag, 2011), and working couples (Bakker, Demerouti, & Schaufeli, 2005) among others.

Within the school context, past studies have focused on the contagion of motivational orientations (Radel, Sarrazin, Legrain, & Wild, 2010), mindsets (Burkley, Curtis, & Hatvany, 2017; King, 2020), and academic achievement (Fortuin, Geel, & Vedder, 2016). For example, Radel and colleagues (2010) found evidence for the contagion of motivational orientations. Their study showed that if a teacher is introduced and perceived to be intrinsically motivated (teaching as a volunteer) or extrinsically motivated (paid to teach), the student adopts the same type of motivation. Students, in turn, were asked to teach their peers. These student-teachers embodied the same kind of motivation they perceived in their teachers, demonstrating motivation contagion from teachers to students to their peers.

Burkley and colleagues (2017) used an experimental paradigm to examine social contagion and asked students to read vignettes about a student with either a growth or fixed mindset (i.e., the belief that intelligence can be improved through effort or is something fixed). Those who read the growth mindset vignette were more likely to adopt growth mindsets themselves. King (2020) likewise demonstrated evidence of mindset contagion in real classroom settings with classmates influencing each other’s mindsets. Student achievement has also been found to be subject to social contagion. Fortuin and colleagues (2016) used social network analysis to examine whether peers’ achievement scores become more similar over time. Students were asked to nominate seven friends and found that friends’ achievement scores influenced students’ achievement levels.

By extension, social contagion might also apply to engagement in school. For example, Appleton, Christenson, Kim, and Reschly (2006) found that peers are especially relevant to students’ engagement in school. When a student interacts with an engaged peer, he is also likely to become more engaged. Frequent interactions (e.g., Bakker & Xanthopoulou, 2009), observational learning and mimicry (Hatfield, Cacioppo, & Rapson, 2009), and the formation of self-generated expectations to perform during social interactions (Wild & Enzle, 2002) are critical mechanisms for social contagion. The closest social group with whom students spend most of their everyday interactions with are their classmates. As students spend a lot of their time interacting with their classmates (see Lam, McHale, & Crouter, 2014), this further increases the likelihood that their classmates’ engagement levels will influence their own.

How might engagement contagion occur? Social contagion can occur either unconsciously or consciously (Laurin, 2016). A hypothetical example might help illustrate this principle: Cherry sees her classmate Claudia as putting in a lot of effort in her schoolwork and enjoying her classes (behaviorally and emotionally engaged). The mental representation of engagement manifested in behaviors such as working hard and actively participating in in-class activities and in emotions such as enjoying schoolwork becomes highly accessible in Cherry’s mind. This mental accessibility will remain for a certain time, depending on the motivational relevance of school. Subsequently, when Cherry goes to school, she may find herself becoming more engaged as well. Cherry may fail to recognize that her engaged state was directly induced by her classmate Claudia’s engagement and instead infer that she has wanted to be engaged all along. Alternatively, social contagion can also be conscious, especially when individuals attune their psychological states to those around them. For example, Cherry sees many of her classmates to be highly engaged, and she consciously attunes herself to this highly engaged state, becoming more engaged herself in the process.

The present study

The present study aimed to examine the social contagion of engagement among classmates. To do so, we measured the engagement of secondary school students at Time 1 (T1) and Time 2 (T2). We tested whether the engagement levels of one’s classmates at T1 predicts engagement at T2 despite controlling for one’s T1 engagement. The conceptual model for the current study is shown in Figure 1. Specifically, we tested the following hypotheses:

H1: Classmates’ T1 engagement will predict student’s T2 engagement controlling for autoregressor effect and covariates (engagement contagion effect).

H2: Time 1 student engagement will predict engagement in T2 (autoregressor effect).

Figure 1.

Conceptual framework testing the social contagion of engagement among classmates.

To ensure that social contagion effects can be teased apart from one’s baseline level of engagement, we controlled for autoregressor effects (see Selig & Little, 2012). We also controlled for demographic factors such as gender, age, and year level.

Several studies have demonstrated that student engagement is shaped by students’ achievement goals (Ames, 1992; Elliot, 2005; Elliot, McGregor, & Gable, 1999). Hence, we included them as covariates. Two achievement goals were controlled, mastery goals which pertain to the desire to acquire knowledge for intrapersonal purposes and performance goals, defined as the desire to outperform others (Darnon, Dompnier, & Marijn Poortvliet, 2012; Elliot et al., 1999).

Method

Participants and procedures

We recruited 848 students from two public secondary schools in the Philippines for this study. The students were from 30 classes with 363 boys (42.81%) and 485 (57.19%) girls. Most students were between 13 to 15 years old, and their average age is 14.64 (SD = 1.52). There were 359 (42.33%) students in second year in high school, 251 (29.60%) in third year, and 238 (28.07%) in fourth year. These year levels are equivalent to grades 8, 9, and 10 in the American education system, respectively.

The data used in this study are part of a larger study on student motivation (King, 2015a, 2015b). However, the social contagion of student engagement has not yet been examined in previous studies. For context, it is vital to note basic information about the Philippine educational system. Philippine education is patterned after the American educational system, given its colonial experience under the United States (Tanodra, 2003). Mandatory education starts in Primary 1. Traditionally, Filipino students had to undergo six years of elementary education and four years of secondary school education. More recently, the government has instituted a K to 12 system, which means 12 years of compulsory education (Okabe, 2013). The average class size of secondary schools in the Philippines is 54, which is rather large compared to other countries (Orleans, 2007). The number of classes in a school year is around 200 days (Department of Education, 2019) where classes usually last from seven to nine classroom hours per day; Monday through Friday.

The second author administered the questionnaires in each class. Two months after the beginning of the school year (August, T1), scales that were intended to measure demographic characteristics, engagement, and other motivational variables (e.g., achievement goals) were administered. The same set of questionnaires was given seven months later, near the end of the school year (March, T2). The second wave of data collection was pragmatically set to seven months to collect data before the students complete the school year. This period also allows peer-influence to operate within the classroom, and at the same time proactively minimizes the chances of attrition (see Hansen, Tobler, & Graham, 1990; Tobler & Komro, 2011). Attrition in this study was minimized by the seven-month gap between the two data collection time points as longer durations between data collection is known to increase attrition rates (e.g., mean attrition rate is 27% at 12 months; Tobler & Komro, 2011). The support of the teachers and administrators from the schools where the data is collected is also instrumental in minimizing respondent attrition.

Instruments

Student engagement. Engagement in school was measured using the engagement subscale of the Engagement and Disaffection Scale developed by Skinner et al. (2009). Two dimensions of engagement were included in this: behavioral engagement (5 items; e.g., “I try hard to do well in school.”) and emotional engagement (5 items; e.g., “Class is fun.”). This scale was rated on a 4-point Likert scale (1 = “strongly disagree”, and 4 = “strongly agree”) with higher scores indicating a greater endorsement of the construct. We combined these two forms of engagement into one overall scale given their high correlations at both T1 (r_Time1 = .63, p < .001) and T2 (r_{Time2 = .} .69, p < .001). The Cronbach’s alpha for the combined scale is α = 0.79 at T1 and α = 0.86 at T2. This scale has also been found to have excellent psychometric properties in previous research including satisfactory ratings of internal consistencies, high cross-time stabilities, and good inter-rater reliabilities (Skinner et al., 2009). Overall, the measure is considered a good marker of the quality of student participation in classroom activities. It has also been used among Filipino students (e.g., King, McInerney, Ganotice, & Villarosa, 2015; King, McInerney, & Watkins, 2012).

Class engagement. Class engagement was computed by averaging the engagement scores of all students in one class, after excluding the relevant student’s engagement scores from the class average. Past studies have used a similar aggregation procedure to study social contagion effects (Bakker et al., 2006; Totterdell, 2000).

Covariates. Mastery goals and performance goals were measured using the mastery-approach and performance-approach subscales in the Achievement Goal Questionnaire-Revised developed by Elliot and Murayama (Elliot & Murayama, 2008). Two types of achievement goals are included in this scale: mastery-approach (α = 0.76; “I want to learn as much as possible this semester”) and performance-approach (α = 0.76; “My goal this semester is to get better grades than most other students”). Each construct was measured using three items. A sample mastery goal item is ‘I want to learn as much as possible this semester’. A sample performance goal item is ‘My goal this semester is to get better grades than most other students’. Achievement goal items were rated on a 6-point Likert scale ranging from 1 (strongly disagree) to 6 (strongly agree). Key demographics such as gender, age, and year level were all entered as covariates.

All questionnaires were administered in English as English is the medium of instruction in the Philippines. Previous research among Filipino students found that English scales perform equally well compared to translated Filipino questionnaires (Ganotice, Bernardo, & King, 2012, 2013).

Statistical analyses

Fifty-six respondents (6.61%) had missing data. Missing data for T1 data collection ranged from six (0.71%) to 24 (2.83%) participants.¹ Missing data at the T2 data collection ranged from 25 (2.95%) to 38 (4.48%) participants. To deal with the missing data,² we used multiple imputations with chained equations (MICE) (Royston & White, 2011; White, Royston, & Wood, 2011). A set of 20 iterations were created. To run the multilevel modelling analysis, the commands “mi estimate: mixed” and “mibeta” were used. All data analyses were implemented on STATA MP 14 (StataCorp, 2015).

As a preliminary analysis, we ran an unconditional model to evaluate whether multilevel modelling is appropriate based on the intra-class correlation (ICC). The ICC refers to the portion of the variance that exists between classes (see Bliese, 2000). An ICC higher than a value of 0.05 (5%) confirms that the variable under study possesses adequate class-level properties that can be explored through multilevel modelling (Gavin & Hofmann, 2002). Consequently, we ran three separate models to test our hypotheses.

In the first model, we tested for the autoregressor effect (i.e., student-level engagement in T1 predicting student-level engagement in T2; H2) by entering T1 student engagement as a predictor of T2 student engagement (Model 1). Then, for Model 2, we entered several covariates such as demographic characteristics (age, gender, and year level) and achievement goals (performance and mastery approach) as predictors of T2 student engagement. We also included the autoregressor effect (i.e., T1 student engagement) as a covariate. Autoregressor effects refer to “the effect of a construct on itself measured at a later time” (Selig & Little, 2012, p. 265). This allows us to examine the contagion effect while taking into account one’s engagement score at T1. Finally, to test the engagement contagion effect (H1), we entered T1 class engagement (Level 2) as a predictor of T2 student engagement (Level 1), controlling for covariates and the autoregressor effect (Model 3). Our engagement contagion hypothesis would be supported if T1 class engagement was a positive predictor for T2 student engagement.

Results

Table 1 presents the descriptive statistics and bivariate correlations among the variables at level 1. In the preliminary analysis, the unconditional model with T2 student engagement as the outcome yielded an ICC of 0.057 (SE = 0.02). This ICC value indicates that 6% of the total variance in T2 student engagement was between classes. This value also meets the criteria for using multilevel analysis (Gavin & Hofmann, 2002).

Table 1.

Descriptive statistics and bivariate correlations among the study variables at level 1.

	1	2	3	4	5	6	7
1. T1 engagement	–	.413***	.051	–.141***	–.072*	.397***	.319***
2. T2 engagement		–	–.002	–.093**	–.005	.248***	.186***
3. Gender			–	–.083*	–.002	.114***	–.007
4. Age				–	.709***	–.173***	–.148***
5. Year level					–	–.091**	–.116***
6. Mastery goal						–	.522***
7. Performance goal							–
Mean	3.25	3.26	–	14.64	–	4.92	4.61
SD	.40	.43	–	1.52	–	.88	.99

Note: *p<.05; **p<.01; ***p<.001; Gender was coded as 0 = male; 1 = female.

In Model 1, we tested the autoregressor effect (H2). We found that T1 student engagement predicted T2 student engagement (B = 0.459, p < 0.001) and accounted for 17.93% of the variance in T2 student engagement (see Table 1). When covariates are included, as in Model 2, T1 student engagement remained a positive predictor of T2 student engagement (B = 0.406, p < 0.001). These results supported H2. Age negatively predicted T2 student engagement (B = -0.03, p < 0.05) while year level (B = 0.06, p < 0.05) and T1 mastery approach goals (B = 0.04, p < 0.05) positively predicted T2 engagement, respectively. Model 2 accounted for 19.44% of the variance in T2 student engagement.

In Model 3, we tested for engagement contagion (H1). Model 3 included the autoregressor effect and covariates from Model 2 and then included T1 class engagement in the model. T1 class engagement was a positive and significant predictor of T2 student engagement (B = 0.265, p < 0.05). This model was significant [F(8, 814) = 24.26, p < 0.001] and predicted 19.95% of the variance in T2 student engagement thereby providing support for H1 (see Model 3 in Table 2).

Table 2.

Multilevel models predicting T2 student engagement.

	Model 1		Model 2		Model 3
	Autoregressor only		Autoregressor +covariates		Full model (Model 2 +Level 2 predictor)
Predictors	B	SE	B	SE	B	SE
1. T1 individual engagement	0.459***	0.03	0.406***	0.04	0.400***	0.04
2. Gender	–	–	–0.03	0.03	–0.02	0.03
3. Age	–	–	–0.03	0.01	–0.02	0.01
4. Year level	–	–	0.06*	0.02	0.06*	0.03
5. Mastery approach	–	–	0.04*	0.02	0.04*	0.02
6. Performance approach	–	–	0.01	0.02	0.003	0.02
7. T1 class engagement	–	–	–	–	0.265*	0.11
R²	17.93%		19.44%		19.95%

Note: *p<.05; ***p<.001; B = unstandardized coefficients.

Discussion

The literature on student engagement in school has mostly neglected the crucial role played by social contagion in facilitating engagement. The central aim of this study was to examine whether the engagement of one’s classmates can predict one’s own. Our results support the core hypothesis on the social contagion of engagement. Specifically, individual student engagement at T2 was positively predicted by class engagement at T1. The results held even after controlling for autoregressor effects (i.e., students’ T1 engagement predicting their T2 engagement). To ensure that the results are robust, we ruled out potentially confounding factors such as demographic factors and achievement goals. The effect of classmates’ engagement on one’s engagement in school remained significant.

Our findings corroborate previous studies on the contagion of engagement in the work domain (Bakker et al., 2006; Totterdell, 2000). This demonstrates that the social contagion of student engagement is also relevant in the educational context. Clustered peer-to-peer interaction is a potential pathway behind engagement contagion (Bakker & Xanthopoulou, 2009; Fredricks et al., 2004; Wentzel et al., 2017; Xerri, Radford, & Shacklock, 2018). When students are paired or grouped with engaged peers, their engagement in school also increases (see Kindermann, 2016; Kindermann, McCollam, & Gibson, 1996).

It is also plausible that student engagement is augmented by merely being in a more engaged group through the process of priming, social facilitation, or behavioral mimicry (Chartrand & Lakin, 2013; Guerin, 1986; Hatfield et al., 2009). As students progressively spend less time with their family and more time with their classmates and peers (Lam et al., 2014), the more frequent interactions they have with their classmates could further strengthen engagement contagion.

Engagement contagion can either occur consciously or unconsciously (Barsade, 2002; Hatfield et al., 2009). The series of experiments conducted by Aarts et al. (2004) provide evidence that people can automatically pursue the goals they observe from others. When students observe the highly engaged states of their classmates, they might automatically adopt or mimic them to emphasize how social contagion facilitates the process of blending with the social environment. In a review on behavioral mimicry by Chartrand and Lakin (2013), they referred to the automaticity of behavioral mimicry as the “chameleon effect”. Social contagion can also be conscious and be facilitated by rapport, the goal to affiliate, similarity, and prosocial orientation, among others (Chartrand & Lakin, 2013). Because the participants in this study are classmates, these facilitators are likely to be present in the classroom. Future studies can look into these potential moderators which might strengthen or weaken social contagion effects.

The findings also extend the literature that examined social contagion in the educational context. The closest work related to our study would be past studies that focused on the contagion of motivation within the school context (Burgess et al., 2018; Wild & Enzle, 2002). Like motivation, engagement can be perceived and inferred from peers. As a student interacts with engaged classmates, he forms a set of self-expectations similar to that of his peers that would, in turn, develop his engagement (see Wild & Enzle, 2002).

Overall, the present study provides evidence for the social contagion of engagement among classmates. By building on previous studies on the antecedents of student engagement (King, 2015a; Pekrun & Linnenbrink-Garcia, 2012; Skinner et al., 2009; Wang & Eccles, 2012; Wang & Holcombe, 2010) and engagement contagion in work settings (Bakker et al., 2006; Bakker, Westman, van Emmerik, et al., 2009; Bakker & Xanthopoulou, 2009; Torrente et al., 2013; Totterdell, 2000), we have addressed a critical theoretical gap in terms of the role of one’s peers in facilitating student engagement in school.

Study implications

The current study contributes to the literature in the following ways: First, it enriches the student engagement literature by advancing our understanding of how social contagion might play a role in facilitating engagement in school. Previous studies looked at different personal psychological factors that predict student engagement (King et al., 2015; Pekrun & Linnenbrink-Garcia, 2012; Skinner et al., 2009; Wang & Eccles, 2012; Wang & Holcombe, 2010) as well as social factors related to teachers (Gutiérrez & Tomás, 2019; Roorda et al., 2017; Roorda et al., 2011; Wang & Eccles, 2013). However, less research has focused on the crucial role played by peers, especially as regards social contagion processes.

Second, though there are past studies that did focus on engagement contagion, almost all of these studies have been confined to the work setting. This study extends the literature on engagement contagion to the school domain. Given the importance of engagement in school for a wide range of optimal outcomes, more work must be done to elucidate the different factors that promote or inhibit engagement.

Third, this study addresses the methodological shortcomings of previous research. Past studies have examined social contagion in dyadic or one-to-one interactions (e.g., Kandel, 1978; Wild & Enzle, 2002). However, students interact with multiple classmates and not just one teacher or one friend. Our study demonstrates that classmates can shape one’s engagement via social contagion processes, even after taking into account one’s prior engagement levels. Also, several past studies on social contagion have used experimental approaches that might have less ecological validity (Barsade, 2002; Wild & Enzle, 2002). Our study supplements past experimental findings with empirical data collected within the real ecologies of the classroom.

Finally, our study also has concrete, practical implications. School administrators could pay more attention to how students are assigned to classes. Perhaps, administrators can consider placing less engaged students in more engaged classrooms. Indirectly, our study also holds implications for the practice of “streaming” which emphasizes grouping highly achieving students together and clustering those of lower achievement (e.g., Chiu, Chow, & Joh, 2017). While highly engaged classmates can pull one’s engagement, the opposite may be true. Having disengaged peers might also lower one’s engagement (e.g., Li, Lynch, Kalvin, Liu, & Lerner, 2011).

Limitations and future directions

Despite its strengths, this study has several limitations. First, the data we collected were based on self-reports. The inclusion of teacher assessments on students’ engagement (e.g., behavioral observations) in future studies could make the study more methodologically robust.

Second, our study is conducted in a lower-middle-income country in the Southeast Asian context. However, this raises the question of whether the findings are culturally generalizable. Western countries, for example, also found that peers play a key role in student engagement (e.g., Appleton et al., 2006; Virtanen et al., 2018). Cross-cultural investigations could increase the generalizability of the study.

Third, the specific theoretical mechanisms behind engagement contagion need to be unpacked. Future studies can explore the role of individual differences such as personality characteristics (e.g., withdrawn personality disposition, introversion, or low social functioning) and other psycho-social conditions which are known to facilitate contagion (e.g., frequency of interactions, empathy, susceptibility to contagion, and similarity; Bakker, Westman, & Hetty van Emmerik, 2009).

Fourth, other dimensions of student engagement in school such as cognitive engagement (referring to the use of deep or sophisticated learning strategies; Fredricks et al., 2004) and agentic engagement (referring to a student’s intent to enrich his own learning experience; Reeve, 2012) can be explored. Researchers could examine whether these forms of engagement can also be transmitted among classmates.

Fifth, in this study we focus exclusively on the positive aspect of engagement. However, it is also possible that the converse of engagement (i.e., disengagement or disaffection) might also be relevant (Skinner, 2016). Students who are exposed to disengaged peers might develop lower levels of engagement (Furrer, 2010). Past studies have suggested that negative psychological constructs might have more powerful effects than positive ones given the negativity bias of the human brain (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001; Norris, 2019). Hence, it is important for future studies to also include measures of disengagement or disaffection to obtain a clearer picture of how the engagement/disengagement levels of one’s peers could affect oneself.

Conclusion

Our study provides evidence for the social contagion of student engagement. The findings highlight the social embeddedness of student engagement, and that amidst an engaged class, one can also adopt and consequently develop greater engagement. Classmates and peers must be taken into account to develop a fuller understanding of student engagement. Indeed, “the power in improving adolescent engagement may reside within the schools and the classrooms” (Wang et al., 2018, p. 158).

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Norman B Mendoza

Notes

Author biographies

Norman B Mendoza is a Doctoral student from the Department of Curriculum and Instruction, The Education University of Hong Kong. His research interests are in motivation, assessment, and well-being in the educational context. He is also the co-founder of LifeRisksPH, a non-profit youth organization focused on youth well-being and mental health based in Pampanga, Philippines.

Ronnel B King is an Associate Professor at the Faculty of Education, University of Macau and an Adjunct Associate Professor at The Education University of Hong Kong. His research interests focus on understanding the factors that underpin motivation and well-being and in developing positive psychology/education interventions to cultivate these optimal psychological states.

References

Aarts

Gollwitzer

P. M.

Hassin

R. R.

(2004). Goal contagion: Perceiving is for pursuing. Journal of Personality and Social Psychology, 87, 23–37. https://doi.org/10.1037/0022-3514.87.1.23

Abbott-Chapman

Martin

Ollington

Venn

Dwyer

Gall

(2014). The longitudinal association of childhood school engagement with adult educational and occupational achievement: Findings from an Australian national study. British Educational Research Journal, 40, 102–120. https://doi.org/10.1002/berj.3031

Allison

P. D.

(2003). Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112, 545–557. https://doi.org/10.1037/0021-843X.112.4.545

Ames

(1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84, 261–271.

Appleton

J. J.

Christenson

S. L.

Furlong

M. J.

(2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45, 369–386. https://doi.org/10.1002/pits.20303

Appleton

J. J.

Christenson

S. L.

Kim

Reschly

A. L.

(2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44, 427–445. https://doi.org/10.1016/j.jsp.2006.04.002

Archambault

Janosz

Morizot

Pagani

(2009). Adolescent behavioral, affective, and cognitive engagement in school: Relationship to dropout. The Journal of School Health, 79, 408–415. https://doi.org/10.1111/j.1746-1561.2009.00428.x

Bakker

A. B.

(2005). Flow among music teachers and their students: The crossover of peak experiences. Journal of Vocational Behavior, 66, 26–44. https://doi.org/10.1016/j.jvb.2003.11.001

Bakker

A. B.

Demerouti

(2013). The spillover–crossover model. In J. G. Grzywacz & E. Demerouti (Eds.), New frontiers in work and family research (pp. 55–70). Hove: Psychology Press.

10.

Bakker

A. B.

Demerouti

Schaufeli

W. B.

(2005). The crossover of burnout and work engagement among working couples. Human Relations, 58, 661–689. https://doi.org/10.1177/0018726705055967

11.

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

12.

Bakker

A. B.

Westman

Hetty van Emmerik

(2009). Advancements in crossover theory. Journal of Managerial Psychology, 24, 206–219.

13.

Bakker

A. B.

Westman

van Emmerik

I. H.

Demerouti

(2009). The crossover of work engagement between working couples. Journal of Managerial Psychology, 24, 220–236.

14.

Bakker

A. B.

Xanthopoulou

(2009). The crossover of daily work engagement: Test of an actor–partner interdependence model. The Journal of Applied Psychology, 94, 1562–1571.

15.

Barsade

S. G.

(2002). The ripple effect: Emotional contagion and its influence on group behavior. Administrative Science Quarterly, 47, 644–675. https://doi.org/10.2307/3094912

16.

Baumeister

R. F.

Bratslavsky

Finkenauer

Vohs

K. D.

(2001). Bad is stronger than good. Review of General Psychology, 5, 323–370. https://doi.org/10.1037/1089-2680.5.4.323

17.

Bliese

P. D.

(2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 349–381). Jossey-Bass.

18.

Burgess

L. G.

Riddell

P. M.

Fancourt

Murayama

(2018). The influence of social contagion within education: A motivational perspective. Mind Brain and Education, 12, 164–174. https://doi.org/10.1111/mbe.12178

19.

Burkley

Curtis

Hatvany

(2017). The social contagion of incremental and entity trait beliefs. Personality and Individual Differences, 108, 45–49. https://doi.org/10.1016/j.paid.2016.11.063

20.

Chartrand

T. L.

Lakin

J. L.

(2013). The antecedents and consequences of human behavioral mimicry. Annual Review of Psychology, 64, 285–308. https://doi.org/10.1146/annurev-psych-113011-143754

21.

Chiu

M. M.

Chow

B. W.-Y.

Joh

S. W.

(2017). Streaming, tracking and reading achievement: A multilevel analysis of students in 40 countries. Journal of Educational Psychology, 109, 915–934. https://doi.org/10.1037/edu0000188

22.

Chong

W. H.

Liem

G. A. D.

Huan

V. S.

Kit

P. L.

Ang

R. P.

(2018). Student perceptions of self-efficacy and teacher support for learning in fostering youth competencies: Roles of affective and cognitive engagement. Journal of Adolescence, 68, 1–11. https://doi.org/10.1016/j.adolescence.2018.07.002

23.

Christakis

N. A.

Fowler

J. H.

(2013). Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine, 32, 556–577. https://doi.org/10.1002/sim.5408

24.

Connell

J. P.

Wellborn

J. G.

(1991). Competence, autonomy, and relatedness: A motivational analysis of self-system processes. In M. R. Gunnar & L. A. Sroufe (Eds.) The Minnesota symposia on child psychology, Vol. 23. Self processes and development (pp. 43–77). Lawrence Erlbaum Associates, Inc.

25.

Culbertson

S. S.

Fullagar

C. J.

Simmons

M. J.

Zhu

(2015). Contagious flow: Antecedents and consequences of optimal experience in the classroom. Journal of Management Education, 39, 319–349. https://doi.org/10.1177/1052562914545336

26.

Darnon

Dompnier

Marijn Poortvliet

(2012). Achievement goals in educational contexts: A social psychology perspective. Social and Personality Psychology Compass, 6, 760–771. https://doi.org/10.1111/j.1751-9004.2012.00457.x

27.

Datu

J. A. D.

Park

(2019). Perceived school kindness and academic engagement: The mediational roles of achievement goal orientations. School Psychology International, 40, 456–473. https://doi.org/10.1177/0143034319854474

28.

David

C. C.

Albert

J. R. G.

Vizmanos

J. F. V.

(2018). Boys are still left behind in basic education. Quezon City, Philippines. https://pidswebs.pids.gov.ph/CDN/PUBLICATIONS/pidspn1820.pdf

29.

Demerouti

(2012). The spillover and crossover of resources among partners: The role of work–self and family–self facilitation. Journal of Occupational Health Psychology, 17, 184–195. https://doi.org/10.1037/a0026877

30.

Department of Education. (2019). School calendar for school year 2019–2020. https://www.deped.gov.ph/2019/04/26/april-22-2019-do-007-s-2019-school-calendar-for-school-year-2019-2020/

31.

Doherty

R. W.

Orimoto

Singelis

T. M.

Hatfield

Hebb

(1995). Emotional contagion: Gender and occupational differences. Psychology of Women Quarterly, 19, 355–371. https://doi.org/10.1111/j.1471-6402.1995.tb00080.x

32.

Duchesne

Larose

Feng

(2019). Achievement goals and engagement with academic work in early high school: Does seeking help from teachers matter? The Journal of Early Adolescence, 39, 222–252. https://doi.org/10.1177/0272431617737626

33.

Dupeyrat

Mariné

(2005). Implicit theories of intelligence, goal orientation, cognitive engagement, and achievement: A test of Dweck’s model with returning to school adults. Contemporary Educational Psychology, 30, 43–59. https://doi.org/10.1016/j.cedpsych.2004.01.007

34.

Eisenberg

Golberstein

Whitlock

J. L.

Downs

M. F.

(2013). Social contagion of mental health: Evidence from college roommates. Health Economics, 22, 965–986. https://doi.org/10.1002/hec.2873

35.

Elliot

A. J.

(2005). A conceptual history of the achievement goal construct. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 52–72). Guilford Publications.

36.

Elliot

A. J.

McGregor

H. A.

Gable

(1999). Achievement goals, study strategies, and exam performance: A mediational analysis. Journal of Educational Psychology, 91, 549–563. https://doi.org/10.1037/0022-0663.91.3.549

37.

Elliot

A. J.

Murayama

(2008). On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100, 613–628. https://doi.org/10.1037/0022-0663.100.3.613

38.

Estell

D. B.

Perdue

N. H.

(2013). Social support and behavioral and affective school engagement: The effects of peers, parents, and teachers. Psychology in the Schools, 50, 325–339. https://doi.org/10.1002/pits.21681

39.

Finn

J. D.

(1989). Withdrawing from school. Review of Educational Research, 59, 117–142.

40.

Fortuin

Geel

M. v.

Vedder

(2016). Peers and academic achievement: A longitudinal study on selection and socialization effects of in-class friends. The Journal of Educational Research, 109, 1–6. https://doi.org/10.1080/00220671.2014.917257

41.

Fredricks

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

42.

Froiland

J. M.

Worrell

F. C.

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

43.

Furrer

C. J.

(2010). Capturing the friendship context with a collective property: Friendship group engagement vs. disaffection. Journal of Adolescence, 33, 853–867. https://doi.org/10.1016/j.adolescence.2010.07.003

44.

Ganotice

F. A.

Bernardo

A. B. I.

King

R. B.

(2012). Testing the factorial invariance of the English and Filipino versions of the inventory of school motivation with bilingual students in the Philippines. Journal of Psychoeducational Assessment, 30, 298–303. https://doi.org/10.1177/0734282911435459

45.

Ganotice

F. A.

Bernardo

A. B. I.

King

R. B.

(2013). Adapting the facilitating conditions questionnaire (FCQ) for bilingual Filipino adolescents: Validating English and Filipino versions. Child Indicators Research, 6, 237–256. https://doi.org/10.1007/s12187-012-9167-1

46.

Gavin

M. B.

Hofmann

D. A.

(2002). Using hierarchical linear modeling to investigate the moderating influence of leadership climate. The Leadership Quarterly, 13, 15–33. https://doi.org/10.1016/S1048-9843(01)00102-3

47.

Guerin

(1986). Mere presence effects in humans: A review. Journal of Experimental Social Psychology, 22, 38–77.

48.

Gutiérrez

Tomás

J. M.

(2019). The role of perceived autonomy support in predicting university students’ academic success mediated by academic self-efficacy and school engagement. Educational Psychology, 39, 729–748. https://doi.org/10.1080/01443410.2019.1566519

49.

Hansen

W. B.

Tobler

N. S.

Graham

J. W.

(1990). Attrition in substance abuse prevention research: A meta-analysis of 85 longitudinally followed cohorts. Evaluation Review, 14, 677–685. https://doi.org/10.1177/0193841x9001400608

50.

Hatfield

Cacioppo

J. T.

Rapson

R. L.

(2009). Emotional contagion and empathy. In J. Decety & W. Ickes (Eds.), The social neuroscience of empathy (pp. 19–30). The MIT Press.

51.

Juvonen

Espinoza

Knifsend

(2012). The role of peer relationships in student academic and extracurricular engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 387–401). Springer.

52.

Kandel

D. B.

(1978). Homophily, selection, and socialization in adolescent friendships. American Journal of Sociology, 84, 427–436. https://doi.org/10.1086/226792

53.

Kiefer

S. M.

Alley

K. M.

Ellerbrock

C. R.

(2015). Teacher and peer support for young adolescents’ motivation, engagement, and school belonging. RMLE Online, 38, 1–18. https://doi.org/10.1080/19404476.2015.11641184

54.

Kindermann

T. A.

(2016). Peer group influences on students’ academic motivation. In K. R. Wentzel & G. B. Ramani (Eds.), Handbook of social influences in school contexts: Social-Emotional, motivation, and cognitive outcomes (pp. 31–47). Routledge.

55.

Kindermann

T. A.

McCollam

T. L.

Gibson

(1996). Peer networks and students’ classroom engagement during childhood and adolescence. In K. R. Wentzel & J. Juvonen (Eds.), Social motivation: Understanding children’s school adjustment (pp. 279–312). Cambridge University Press.

56.

King

R. B.

(2015a). Examining the dimensional structure and nomological network of achievement goals in the Philippines. Journal of Adolescence, 44, 214–218. https://doi.org/10.1016/j.adolescence.2015.07.019

57.

King

R. B.

(2015b). Sense of relatedness boosts engagement, achievement, and well-being: A latent growth model study. Contemporary Educational Psychology, 42, 26–38. https://doi.org/10.1016/j.cedpsych.2015.04.002

58.

King

R. B.

(2020). Mindsets are contagious: The social contagion of implicit theories of intelligence among classmates. The British Journal of Educational Psychology, 90, 349–363. https://doi.org/10.1111/bjep.12285

59.

King

R. B.

Datu

J. A.

(2017). Happy classes make happy students: Classmates’ well-being predicts individual student well-being. Journal of School Psychology, 65, 116–128. https://doi.org/10.1016/j.jsp.2017.07.004

60.

King

R. B.

McInerney

D. M.

Ganotice

F. A.

Villarosa

J. B.

(2015). Positive affect catalyzes academic engagement: Cross-sectional, longitudinal, and experimental evidence. Learning and Individual Differences, 39, 64–72. https://doi.org/10.1016/j.lindif.2015.03.005

61.

King

R. B.

McInerney

D. M.

Watkins

D. A.

(2012). Studying for the sake of others: The role of social goals on academic engagement. Educational Psychology, 32, 749–776. https://doi.org/10.1080/01443410.2012.730479

62.

Klem

A. M.

Connell

J. P.

(2004). Relationships matter: Linking teacher support to student engagement and achievement. The Journal of School Health, 74, 262–273. https://doi.org/10.1111/j.1746-1561.2004.tb08283.x

63.

Komarraju

Karau

S. J.

(2005). The relationship between the big five personality traits and academic motivation. Personality and Individual Differences, 39, 557–567. https://doi.org/10.1016/j.paid.2005.02.013

64.

Lam

C. B.

McHale

S. M.

Crouter

A. C.

(2014). Time with peers from Middle childhood to late adolescence: Developmental course and adjustment correlates. Child Development, 85, 1677–1693. https://doi.org/10.1111/cdev.12235

65.

Laurin

(2016). Interpersonal influences on goals: Current and future directions for goal contagion research. Social and Personality Psychology Compass, 10, 668–678. https://doi.org/10.1111/spc3.12289

66.

Lee

J.-S.

(2014). The relationship between student engagement and academic performance: Is it a myth or reality? The Journal of Educational Research, 107, 177–185. https://doi.org/10.1080/00220671.2013.807491

67.

Levy

D. A.

Nail

P. R.

(1993). Contagion: A theoretical and empirical review and reconceptualization. Genetic, Social, and General Psychology Monographs, 119, 233–284.

68.

Y. B.

Lynch

A. D.

Kalvin

Liu

J. J.

Lerner

R. M.

(2011). Peer relationships as a context for the development of school engagement during early adolescence. International Journal of Behavioral Development, 35, 329–342. https://doi.org/10.1177/0165025411402578

69.

Loersch

Aarts

Payne

B. K.

Jefferis

V. E.

(2008). The influence of social groups on goal contagion. Journal of Experimental Social Psychology, 44, 1555–1558. https://doi.org/10.1016/j.jesp.2008.07.009

70.

Medeiros

(2016). Handling missing data in Stata: Imputation and likelihood-based approaches [Paper presentation]. 2016 Swiss Stata Users Group Meeting, StataCorp LP.

71.

Norris

C. J.

(2019). The negativity bias, revisited: Evidence from neuroscience measures and an individual differences approach. Social Neuroscience. Advance online publication.https://doi.org/10.1080/17470919.2019.1696225

72.

Oberle

Schonert-Reichl

K. A.

(2016). Stress contagion in the classroom? The link between classroom teacher burnout and morning cortisol in elementary school students. Social Science & Medicine, 159, 30–37. https://doi.org/10.1016/j.socscimed.2016.04.031

73.

Okabe

(2013). Where does Philippine education go? The “K to 12” program and reform of Philippine basic education. Institute of Developing Economies (IDE), Discussion Paper No. 425, Research Promotion Department, Japan External Trade Organization. http://www.ide.go.jp

74.

Orleans

A. V.

(2007). The condition of secondary school physics education in the Philippines: Recent developments and remaining challenges for substantive improvements. The Australian Educational Researcher, 34, 33–54. https://doi.org/10.1007/Bf03216849

75.

Patall

E. A.

Vasquez

A. C.

Steingut

R. R.

Trimble

S. S.

Pituch

K. A.

(2016). Daily interest, engagement, and autonomy support in the high school science classroom. Contemporary Educational Psychology, 46, 180–194. https://doi.org/10.1016/j.cedpsych.2016.06.002

76.

Pekrun

Linnenbrink-Garcia

(2012). Academic emotions and student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). Springer.

77.

Pianta

R. C.

Hamre

B. K.

Allen

J. P.

(2012). Teacher-student relationships and engagement: Conceptualizing, measuring, and improving the capacity of classroom interactions. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 365–386). Springer.

78.

Quin

(2017). Longitudinal and contextual associations between teacher–student relationships and student engagement. Review of Educational Research, 87, 345–387. https://doi.org/10.3102/0034654316669434

79.

Radel

Sarrazin

Legrain

Wild

T. C.

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

80.

Reeve

(2012). A self-determination theory perspective on student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.) Handbook of research on student engagement (pp. 149–172). Springer.

81.

Roorda

D. L.

Jak

Zee

Oort

F. J.

Koomen

H. M. Y.

(2017). Affective teacher–student relationships and students’ engagement and achievement: A meta-analytic update and test of the mediating role of engagement. School Psychology Review, 46, 239–261. https://doi.org/10.17105/SPR-2017-0035.V46-3

82.

Roorda

D. L.

Koomen

H. M. Y.

Spilt

J. L.

Oort

F. J.

(2011). The influence of affective teacher–student relationships on students’ school engagement and achievement: A meta-analytic approach. Review of Educational Research, 81, 493–529. https://doi.org/10.3102/0034654311421793

83.

Royston

White

I. R.

(2011). Multiple imputation by chained equations (MICE): Implementation in Stata. J Stat Softw, 45, 1–20.

84.

Selig

J. P.

Little

T. D.

(2012). Autoregressive and cross-lagged panel analysis for longitudinal data. In B. Laursen, T. D. Little, & N. A. Card (Eds.) Handbook of developmental research methods (pp. 265–278). The Guilford Press.

85.

Skinner

E. A.

(2016). Engagement and disaffection as Central to processes of motivational resilience and development. In K. R. Wentzel & D. B. Miele (Eds.), Handbook of motivation at school (pp. 145–168). Routledge.

86.

Skinner

E. A.

Belmont

M. J.

(1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85, 571–581. https://doi.org/10.1037/0022-0663.85.4.571

87.

Skinner

E. A.

Kindermann

T. A.

Furrer

C. J.

(2009). A motivational perspective on engagement and disaffection. Educational and Psychological Measurement, 69, 493–525. https://doi.org/10.1177/0013164408323233

88.

Skinner

E. A.

Pitzer

J. R.

(2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 21–44). Springer US.

89.

StataCorp. (2015). Stata statistical software: Release 14.

90.

Tanodra

E. Q.

(2003). Philippine educational system. Verh Educational Enterprise.

91.

Tobler

A. L.

Komro

K. A.

(2011). Contemporary options for longitudinal follow-up: Lessons learned from a cohort of urban adolescents. Evaluation and Program Planning, 34, 87–96. https://doi.org/10.1016/j.evalprogplan.2010.12.002

92.

Torrente

Salanova

Llorens

(2013). Spreading engagement: On the role of similarity in the positive contagion of team work engagement. Revista de Psicología Del Trabajo y de Las Organizaciones, 29, 153–159.

93.

Totterdell

(2000). Catching moods and hitting runs: Mood linkage and subjective performance in professional sport teams. The Journal of Applied Psychology, 85, 848–859.

94.

Van Houtte

(2004). Why boys achieve less at school than girls: The difference between boys’ and girls’ academic culture. Educational Studies, 30, 159–173. https://doi.org/10.1080/0305569032000159804

95.

Virtanen

T. E.

Moreira

Ulvseth

Andersson

Tetler

Kuorelahti

(2018). Analyzing measurement invariance of the students’ engagement instrument brief version: The cases of Denmark, Finland, and Portugal. Canadian Journal of School Psychology, 33, 297–313. https://doi.org/10.1177/0829573517699333

96.

Wang

M.-T.

Eccles

J. S.

(2012). Social support matters: Longitudinal effects of social support on three dimensions of school engagement from Middle to high school. Child Development, 83, 877–895. https://doi.org/10.1111/j.1467-8624.2012.01745.x

97.

Wang

M.-T.

Eccles

J. S.

(2013). School context, achievement motivation, and academic engagement: A longitudinal study of school engagement using a multidimensional perspective. Learning and Instruction, 28, 12–23. https://doi.org/10.1016/j.learninstruc.2013.04.002

98.

Wang

M.-T.

Holcombe

(2010). Adolescents’ perceptions of school environment, engagement, and academic achievement in Middle school. American Educational Research Journal, 47, 633–662. https://doi.org/10.3102/0002831209361209

99.

Wang

M.-T.

Kiuru

Degol

J. L.

Salmela-Aro

(2018). Friends, academic achievement, and school engagement during adolescence: A social network approach to peer influence and selection effects. Learning and Instruction, 58, 148–160. https://doi.org/10.1016/j.learninstruc.2018.06.003

100.

Wentzel

K. R.

Battle

Russell

S. L.

Looney

L. B.

(2010). Social supports from teachers and peers as predictors of academic and social motivation. Contemporary Educational Psychology, 35, 193–202. https://doi.org/10.1016/j.cedpsych.2010.03.002

101.

Wentzel

K. R.

Muenks

McNeish

Russell

(2017). Peer and teacher supports in relation to motivation and effort: A multi-level study. Contemporary Educational Psychology, 49, 32–45. https://doi.org/10.1016/j.cedpsych.2016.11.002

102.

Wentzel

K. R.

Muenks

McNeish

Russell

(2018). Emotional support, social goals, and classroom behavior: A multilevel, multisite study. Journal of Educational Psychology, 110, 611–627. https://doi.org/10.1037/edu0000239

103.

Westman

Bakker

A. B.

Roziner

Sonnentag

(2011). Crossover of job demands and emotional exhaustion within teams: A longitudinal multilevel study. Anxiety, Stress, and Coping, 24, 561–577. https://doi.org/10.1080/10615806.2011.558191

104.

White

I. R.

Royston

Wood

A. M.

(2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30, 377–399.

105.

Wild

T. C.

Enzle

M. E.

(2002). Social contagion of motivational orientations. In Handbook of self-determination research (pp. 141–157). University of Rochester Press.

106.

Xerri

M. J.

Radford

Shacklock

(2018). Student engagement in academic activities: A social support perspective. Higher Education, 75, 589–605. https://doi.org/10.1007/s10734-017-0162-9