A self-determination perspective on Chinese fifth-graders’ task disengagement

Abstract

Engagement in academic tasks is important. However, compared to the large body of research on task engagement, the number of studies on task disengagement is quite limited. The aim of this study is to examine the associations between the motivational (self-determination) and attitudinal antecedents (learning orientations) of task disengagement. The sample consisted of 347 fifth-graders in China. We tested two mediation models that incorporated self-determination (autonomous versus controlled), learning orientation (collaborative versus competitive), and task disengagement among Chinese primary school students in learning English. Results showed collaborative learning orientation mediated the link between autonomous motivation and task disengagement. Collaborative learning orientation was also found to be negatively related to task disengagement.

Keywords

Chinese students learning orientations self-determination task disengagement

Engagement in academic tasks is important because it makes learning possible (Reeve, 2012). Compared to the large body of research on task engagement, the number of studies on task disengagement is quite limited. Efforts have been made to examine the possible factors that predict task disengagement, such as self-belief (Liem, Lau, & Nie, 2008), optimism (Nicholls, Polman, Levy, & Backhouse, 2008), and unsupportive coaching (Nicolas, Gaudreau, & Franche, 2011). However, deeper insights need to be gained regarding the nature of this construct, because a lack of contributors to engagement does not necessarily lead to disengagement.

Recent discussions of task disengagement reveal that limited self-determination may be behind the high levels of disengagement at school (Hafen et al., 2012), and students whose interests and motivations lie elsewhere tend to disengage in a competitive learning atmosphere (Hockings, Cooke, Yamashita, McGinty, & Bowl, 2008). Hence, the aim of the present study is to examine the associations between the motivational and attitudinal antecedents of task disengagement. We tested two models that incorporated self-determination (autonomous versus controlled), learning orientation (collaborative versus competitive), and task disengagement among Chinese primary school students.

Task engagement and disengagement

Furrer and Skinner (2003) defined task engagement as ‘a key construct in motivational models because it is considered a primary pathway by which motivational processes contribute to learning and development’ (p. 149). When learners are not engaged in tasks, we often describe the situation as ‘task disengagement’. Intuitively, disengagement and engagement are two opposite poles of the same bipolar dimension. In effect, this was reflected in past research practice wherein researchers tended to develop the definition of ‘disengagement’ by simply reversing the description of ‘engagement’ (e.g., Liem et al., 2008), or used higher versus lower scores to indicate engagement versus disengagement along the same scale (e.g., Reeve & Jang, 2006). This rather simplified way of conceptualizing disengagement could also explain the scarcity of empirical studies that directly examine disengagement, as it is usually implied in studies on engagement (Christenson, Reschly, & Wylie, 2012).

Along this line of reasoning, it could be argued that instead of being two opposite poles, engagement and disengagement are independent, yet negatively correlated states of mind. Fredricks, Blumenfeld, and Paris (2004) further argued that the measure of engagement could simply tap into a minimal level of participation in school, which could be taken as the absence of disengagement. This pointed to the trajectory where one’s engagement developed into disengagement via other transitional stages such as absence of disengagement. Jang, Kim, and Reeve (2016) also developed different scales to examine engagement and disengagement separately. Hence, it would be insufficient to understand task disengagement by simply reinterpreting the findings regarding engagement in an opposite way and assuming that the lack of factors that affect engagement would subsequently lead to disengagement.

Fredricks et al. (2004) have differentiated between the cognitive, emotional, and behavioral dimensions of task engagement. Compared to emotional engagement which are affective attitudes towards school, and cognitive engagement which involves self-regulated and metacognitive approaches to learning, behavioral engagement is mainly concerned with students’ participation in learning activities, including effort, attention, and persistence during learning tasks. In this study, we exclusively focus on the behavioral dimension in this study because behavioral engagement has been found to influence both cognitive and emotional engagement (Li & Lerner, 2013) and more substantial work has been done that verified the positive impact of behavoiral engagement on academic outcomes compared to the other two types of engagement (Pietarinen, Soini, & Pyhältö, 2014). As such, we examined task disengagement at the behavioral level and defined it as learners’ tendency to quit their involvement in and commitment to learning tasks (Fredricks et al., 2004; Jang et al., 2016).

We identified two constructs that have been extensively examined in the engagement literature—self-determination and learning orientation—so that our results on disengagement can be compared and contrasted to previous findings on engagement. We chose to examine disengagement in the context of English learning tasks for two considerations. First, English has been introduced as a compulsory subject in Chinese school in Grade 3 in primary schools since 2001 (Nunan, 2003), which provides ample opportunities for examining the level of student engagement in English learning in class. Secondly, as one of the university entry requirements and a key aspect for promotional prospects in workplaces, English has become increasingly important both in academic and career achievement in China. This has considerable implications for English classroom learning. On the positive side, the need to learn English could well drive students to be better engaged in English activities and tasks; on the negative side, the need to learn English could well also be transformed into control or pressure, which harms engagement in English learning. At this level, English tasks were deemed to be an appropriate context for examining engagement and disengagement in Chinese students.

Self-determination theory and task disengagement

Self-determination theory (SDT) proposes that humans can experience different types of motivation along a continuum: From the least autonomous form of motivation (extrinsic motivation), which is manifested when the task is engaged in for instrumental or external reasons, to the most autonomous form of motivation (intrinsic motivation), which is demonstrated when the task is engaged in for the inherent enjoyment and satisfaction. Between these two extremes are three categories of regulation—external regulation, introjected regulation, and identified regulation—each with an increased degree of internalization. Hence, external and introjected regulation are forms of controlled motivation, whereas identified and intrinsic regulation are forms of autonomous motivation (Deci & Ryan, 2008).

SDT has been argued to be a strong unifying theory to guide research and practice in disengagement. As Meyer and Gagné (2008) posited, SDT helps to explain not only engagement but also the behavioral reactions that can result in the absence of engagement. Indeed, while autonomous motivation has been empirically shown to lead to greater engagement in academic learning (Fall & Roberts, 2012), controlled striving was also found to positively predict cognitive and behavioral disengagement when athletes were faced with difficulties (Smith, Ntoumanis, Duda, & Vansteenkiste, 2011). The above relationship was observed not only in Western schools but also in Chinese classrooms. Teachers are highly respected as the only authority in Chinese classrooms. It is expected that students will conform and respond to the requirements by teachers, which will result in students’ lack of chances or motivations in thinking about their own needs. As such, learners’ lack of choice or autonomy eventually becomes the driving force for their disengagement with class activities (Soudien, 2006).

Learning orientation and task disengagement

How a student perceives the way to be engaged in schoolwork to a great extent defines the outcome of their engagement. In the current research, we used the term ‘learning orientation’ to refer to students’ personal beliefs or attitudes towards different learning approaches. Johnson and Johnson (1975) differentiated two attitudes towards learning: Attitudes towards cooperation versus competition. It has been consistently proven that, while learners interact to create a collective solution to a task or problem, they gain benefits, such as positive emotion and greater motivation, which contributes to advanced engagement in knowledge construction (Jones & Isroff, 2005). This could be explained through two research lines. On one hand, Duda and Nicholls (1992) argued that task achievement goals (wherein students were interested in learning for its own sake) were associated with the belief that success in the learning tasks stemmed from collaboration with others. While students exhibited genuine interest in learning tasks, they became intrinsically motivated and engaged in the tasks (Kusurkar, Croiset, & Ten Cate, 2011). On the other hand, Young (2011) argued that students have to rely on one another in a collaborative learning mode, which makes students feel personally responsible for the success of their peers. This facilitates a sense of belonging, ownership, and community for students, which helps them feel supported and engaged.

In contrast, orientation towards competitiveness in a classroom also has a great impact on students’ psychological well-being. Competitive orientation shares important theoretical roots with performance achievement goals, which emphasize social comparisons with others (Johnson & Johnson, 2011). As such, with a competitive learning orientation, students are more inclined to be extrinsically motivated with the goal to perform better than others, and better engaged in tasks because competition motivates students to participate in uninteresting learning activities and stimulates their involvement and interest (Annetta, Minogue, Holmes, & Cheng, 2009). However, some researchers have found that students who aim to outperform others exhibit disengaged behaviors (Crocker & Knight, 2005), so that they maintain positive viewpoints about their ability and performance (Hung, 2014). We argue that the former perspective is seen more in learning contexts where academic achievement is emphasized and academic competition is intense, such as Chinese classrooms. The cultural values and social beliefs embedded in Chinese classrooms have provided sufficient reasons for academic competition for survival arising from examination, school enrollment, and employment pressures (Li, Corrie, & Wong, 2008). In this situation, competitive learning orientation may actually promote learning engagement in order for better learning performance and inhibit disengagement.

Self-determination and learning orientation

Reciprocal relationships have been found between self-determined motivation and learning orientation. On one hand, it has been found that a stronger sense of autonomy helped students to share ideas, support each other during collaborative work, which provides greater scope for them to achieve group outcomes and engage in discussion (McLoughlan, 1998). On the other hand, autonomy can also be strengthened by collaborative support, as a collaborative learning paradigm provides students with a positive experience of group work, and this contributes to learner autonomy and self-direction (Brindley, Blaschke, & Walti, 2009). As Law (2011) argued, cooperative learning activities that emphasize students’ responsibility tend to encourage students’ efforts towards group performance and promote students’ autonomous motivation in the classroom.

By contrast, when students feel controlled to complete academic tasks, they generally feel reluctant to collaborate with others because they would not see an immediate need for participating in collaboration which typically takes more time and effort than individual work (Robins, Judge, Odendall, & Roodt, 2009). In another study, competition has also been found to decrease intrinsic motivation because it is typically used to pressure a target person to think, feel, or behave in a specific way (Nichols & Dawson, 2012). Studies of motivation have lent support to the notion that students lose interest and become disengaged if their autonomy is compromised by the pressure to win (Smith & Biddle, 2008).

Present research

Self-determination theory and learning orientation suggest that an individual’s motivation and thoughts while performing an academic task are important in determining subsequent engagement. Although these two constructs have been investigated separately in the engagement literature, it remains unknown whether the opposite relationships hold true for disengagement. In the present study, we examined the relationships between self-determination, learning orientation, and task disengagement. The study was guided by the following general research question: How did Chinese students’ self-determination and learning orientation contribute to their English task disengagement? Consistent with the foregoing discussion, we hypothesized that autonomous motivation is predicted to be associated with lower task disengagement (H₁), whereas controlled motivation is predicted to be associated with higher task disengagement (H₂). Collaborative learning orientation is predicted to be associated with lower task disengagement (H₃), whereas competitive learning orientation is predicted to be associated with higher task disengagement (H₄).

Further, we examined two mediation models to test the indirect effects. Model A posited that the effect of self-determination on task disengagement was mediated by learning orientation. Model B posited that the effect of learning orientation on task disengagement was mediated by self-determination. Specifically, we proposed that learning orientation would meditate the links between self-determination and task disengagement (H₅) and self-determination would meditate the links between learning orientation and task disengagement (H₆).

The present investigation advances our understanding of schoolchildren’s disengagement from academic tasks in several ways. First, this study adds to the very scarce existing literature on task disengagement from a self-determination perspective. Second, this study extends past work on these constructs by using structural equation modeling (SEM) to examine the two mediation models on task disengagement. This provides a fuller picture of how self-determination and learning orientation affect one’s task disengagement, and thus generates findings that are more informative for intervention design.

Method

Participants and procedure

A convenience sampling strategy was used in this study. In total, 347 fifth-graders from two public primary schools (five classes in each school) in the city of Wuhu (Anhui province) in mainland China participated in this study. English is offered as a compulsory subject in all the schools. The majority of the participants came from families with an average level of family income in the city. Some students failed to respond to all the items in the survey package, with over 20% of the questions left blank. Given the large percent of the missing responses for these children, we decided to remove their whole dataset. Thus, the removal of incomplete data left a final sample size of 308. The comparison between the final sample and removed cases did not show any significant differences. Of the final sample, 49.4% were boys, with a mean age of 11.57 years (SD = 0.58). Fifth-graders were chosen as the target population because academic achievement for fifth-graders is critical to a successful developmental trajectory through this period and into adolescence (Eccles, Roeser, Vida, Fredricks, & Wigfield, 2006).

Participants were asked to complete the questionnaires during their class time in the presence of their teacher and the research assistant. All students participated in the study voluntarily, after being informed of the purpose of the study and their rights to withdraw at any stage. The questionnaires were administered in Chinese, following a standard back-translation (Brislin, 1970). The English-language items were translated by the first author into Chinese, and then back-translated into English by a Chinese–English bilingual scholar. Discrepancies emerging from translation and back-translation were addressed. Minor modifications of the Chinese wording were made, in consideration of the participants’ linguistic and cultural backgrounds.

Measures

Self-determination

Ten items identifying the reasons children learn English were adapted from Ryan and Connell’s (1989) Academic Self-regulation Questionnaires (SQR-A). This questionnaire was developed for students in late elementary and middle school to measure their regulatory style. There are five items for autonomous motivation, such as ‘我做英语作业是希望能明白英语课上的內容’ (I do my English homework because I want to understand the subject), and five for controlled motivation, such as ‘如果我不做英语作业, 我会感觉很糟糕.’ (I do my English homework because I will feel bad about myself if I don’t do it) The participants were instructed to indicate their agreement with each item on a four-point Likert scale. In this study, the reliability coefficients for autonomous motivation and controlled motivation (after the removal of one item due to poor factor loading were 0.86 and 0.66, respectively).

Learning orientation

Students’ cooperative and competitive learning orientation were measured by six items taken from Form B of the Minnesota School Affect Assessment for fourth- to twelfth-grade students (Johnson, 1974). Each item was rated on a five-point Likert scale. Sample items include ‘我喜欢和同学们一起学英语.’ (I like to learn English when working together with others: Cooperative learning orientation) and ‘我希望自己英语分数比别人高’ (I like to get better English marks than other students do: Competitive learning orientation). In this study, the reliability coefficients for collaborative and competitive learning orientation were 0.72 and 0.80, respectively.

Task disengagement

Three items adapted from the effort regulation subscale of the Motivated Strategies for Learning Questionnaire (Pintrich, Smith, Garcia, & Mckeachie, 1991) were used to measure students’ task disengagement at the behavioral level. Instead of asking questions about participants’ level of engagement and using the reversed scores to indicate their disengagement level, we followed Liem and others’ (2008) practice and chose to ask questions that directly reflected the degree to which participants would give up or disengage in their English academic tasks. Sample items include ‘当英语课业很枯燥无聊时, 即使没做完, 我也不做了.’（When the work in English is dull and boring, I stop doing it even if it is incomplete.) Each item was rated on a five-point Likert scale. The higher the composite score of the scale, the more likely the students would disengage in the English task. In this study, the reliability coefficient of this scale was 0.77.

Results

Descriptive statistics

The descriptive statistics for all of the survey items were computed and examined for data normality (see Table 1). The skewness and kurtosis indices ranged from -2.58 to 2.76 and -0.63 to 7.70, respectively, which was within Kline’s (2005) recommendation. Hence, the data for this study were regarded as normal and suitable for further analyses. Before testing the proposed models, correlation coefficients were examined among the main variables under study. The results are presented in Table 1. In testing the proposed models, a confirmatory factor analysis was conducted with AMOS 21.0, using the maximum likelihood estimation (MLE) procedure. As this procedure assumes a multivariate normality of the observed variables, the data were examined using the Mardia’s normalized multivariate kurtosis value. The Mardia’s coefficient for the data in this study was 121.20, which was lower than the value of 360 computed based on the formula p(p + 2). On this basis, the multivariate normality of the data in this study was assumed.
Table 1.
Descriptives and correlations among main constructs.

1 2 3 4 5 Mean SD

1. Autonomous motivation − 3.31 0.70

2. Controlled motivation 0.48 − 3.55 0.51

3. Collaborative learning orientation 0.44 0.21 − 3.36 1.04

4. Competitive learning orientation 0.09 0.20 0.29 − 3.91 0.96

5. Task disengagement −0.22 −0.04 −0.21** −0.01 − 1.35 0.57

Note: **p < 0.01.

Evaluation of the measurement model

Confirmatory factor analysis using MLE on variance-covariance matrices was used to estimate the validity of all the variables within the proposed model. The results indicated an acceptable fit between the measurement model and the whole dataset: χ²= 227.66, df = 125, p < 0.001; CFI = 0.95; IFI = 0.95; TLI = 0.94; RMSEA =0.052; SRMR = 0.052.

Test of the structural model

SEM analysis of mediation Model A revealed an acceptable fit (Figure 1): χ²=265.03, df = 126, p < 0.001; CFI = 0.93; IFI = 0.930; TLI = 0.91; RMSEA = 0.060; SRMR = 0.066, AIC = 391.03; BIC = 399.34. The specific mediation effect of autonomous motivation on task disengagement through collaborative learning orientation was significant: Autonomous motivation positively predicted collaborative learning orientation (ß = 0.55, p < 0.001), which in turn was negatively associated with task disengagement (ß = −0.25, p < 0.001). Moreover, controlled motivation was significantly positively related to competitive learning orientation (ß = 0.32, p < 0.01). No other significant results were found. SEM analysis of mediation Model B (Figure 2) also showed an acceptable model fit: χ²= 282.82, df = 126, p < 0.001; CFI = 0.92; IFI = 0.92; TLI = 0.90; RMSEA = 0.064; SRMR = 0.078, AIC = 408.82; BIC = 417.13. Stronger collaborative learning orientation led to greater autonomous motivation (ß = 0.69, p < 0.001) and greater controlled motivation (ß = 0.39, p < 0.001), but less task disengagement (ß = −0.30, p < 0.01). Competitive learning orientation was negatively linked to autonomous motivation (ß = −0.30, p < 0.01). No other significant relationship was found in this model. The direct, indirect and total effects of these variables on disengagement in both models are presented in Table 2.
Figure 1.
Final mediation Model A.

Figure 2.
Final mediation Model B.

Table 2.
Direct, indirect and total effects on disengagement in Model A and Model B.

Hypothesized path Direct effect Indirect effect Total effect

Model A

AUTO → DISENGAGE (H₁) −0.15 −0.14* −0.29

CTRL → DISENGAGE (H₂) 0.06 0.01 0.06

COLLAB → DISENGAGE (H₃) −0.25 0.00 −0.25

COMP → DISENGAGE (H₄) 0.04 0.00 0.04

Model B

AUTO → DISENGAGE (H₁) −0.11 0.00 −0.11

CTRL → DISENGAGE (H₂) 0.06 0.00 0.06

COLLAB → DISENGAGE (H₃) −0.30 −0.05 −0.35**

COMP → DISENGAGE (H₄) 0.08 0.03 0.10

Note: AUTO = Autonomous motivation; CTRL = Controlled motivation; COLLAB = Collaborative learning orientation; COMP = Competitive learning orientation; DISENGAGE = Task disengagement.

*
p < 0.05; **p < 0.01.

Given that the number of degrees of freedom (126) and number of parameter estimated (63) were identical in both models, the model fit with lower values was usually preferred. In our case, the model with learning orientation as a mediator was preferred (Model A), due to its statistics results and theoretical grounding. On one hand, higher CFI, TLI and IFI yet smaller RMSEA, SRMR, and AIC indicated a better model fit; on the other hand, a significant mediation effect was only seen in Model A (p < 0.05). Next, although chi-square tests showed no significant differences between these two models, a model is preferred to the other if it can predict well the outcome variables much better than another and provide information that is more valuable (Chen, 2007). As such, we chose Model A as the final mediation model.

Discussion

In this study, we examined the potential factors that account for task disengagement in academic settings. First, among the four hypothesized direct effects, only collaborative learning orientation showed a significant direct effect on task disengagement in both models. The lack of link between the two forms of self-determination and task disengagement contradicted past theoretical accounts and empirical findings (e.g., Jang et al., 2016), yet was partially in line with Knee and Zuckerman’s (1998) report. In their longitudinal study, they investigated the effect of autonomy on university students’ engagement and disengagement. They found that autonomy was associated with less behavioral disengagement over time, whereas control was not. The authors attributed this to the lower reliability of the measurement scales, which is not the case in this study. We speculate that Chinese children’s learning practice differs greatly from that of their Western counterparts. In China, children’s learning is a major responsibility of parents, with the focus on developing qualities such as diligence, persistence, concentration, and enduring hardship (Cheung & Pomerantz, 2011), and cultivating a sense of autonomy in children is not seen as being critical to their learning (Chao, 1996). As such, self-determination might be much less emphasized in a child’s growth, and the over-focusing on concentration and persistence (indicators of engagement) may overwrite the potential effect of self-determined motivation.

With regards to learning orientation, the findings of collaborative learning orientation and disengagement followed the same pattern as in engagement literature. When students studied in a collaborative manner, the shared goal, two-way communication, and interdependence promoted engagement in learning tasks (Young, 2011) and decreased the level of disengagement. Nonetheless, competitive learning orientation was not related to disengagement as much as other researchers have argued (e.g., Annetta et al., 2009). We believe this was largely related to the Chinese learning culture. Chinese students have been experiencing high levels of stress in school because of competition within the education system (Hesketh et al., 2010). Some scholars have argued that the effects of competitiveness in a collectivistic culture such as China are different from those found in Western settings (King, McInerney, & Watkins, 2012). In the West, competitiveness has usually been associated with the adoption of performance goals but not mastery goals (Harackiewicz, Barron, Pintrich, Elliot, & Thrash, 2002). Yet, competitiveness led to the adoption of both mastery and performance goals in Chinese students (King et al., 2012). As Watkins (2009) argued, the Chinese viewed competition not only as something destructive but also as something that can lead to self-improvement and personal growth. Hence, the consequence and implication of competition in a collectivist culture may be less or even not negative. However, when competitive learning orientation was examined together with other motivational factors, such as self-determined motivation, the results could be complex. Possibly, the potential positive and negative effects of competition in class could co-exist, and sometimes may offset each other.

The indirect effect was only found in Model A, wherein collaborative learning orientation acted as a mediator in the relationship between autonomous motivation and task disengagement. This indicates that autonomous motivation needs to operate in a collaborative learning environment in order to play a significant role in reducing task disengagement in Chinese classrooms. The missing direct link between autonomous motivation and disengagement further confirmed the importance of collaborative learning in Chinese classrooms. Providing choice may not be always effective to produce positive outcomes. As Katz and Assor (2007) argued, ‘in contexts with strong collectivist and hierarchical orientations—typical of students from certain ethnic groups—choice can easily threaten the sense of relatedness and belonging to one’s in-group, as well as the need to be appreciated and loved by important authority figures’ (p.438). Therefore, in such contexts it is important to offer choices that align with students’ inclination to collaborate with others who share similar values and thoughts.

There are some limitations to our findings. First, the results of this study cannot be readily generalized to students from different cultural backgrounds. The missing link between controlled motivation and task disengagement and between competitive learning orientation and task disengagement, reminds us of the need to further investigate this issue in other samples. Second, the measure of task disengagement was limited in scope and design. Like engagement, task disengagement is a multidimensional construct. However, in this study, we examined only one dimension of task disengagement. More studies using the multidimensional approach of task disengagement are warranted. Third, the relatively low reliability coefficient for controlled motivation (below 0.70) urged us to re-examine this construct with Chinese samples in future studies. Fourth, the study did not control for key personal factors such as the participants’ age, sex, or SES. These variables need to be considered in future studies. Last, we relied solely on self-report information from students to assess their behavioral disengagement. This presents difficulties, as it only measures the participants’ memory, estimation, or perception of their disengaged behavior. In future studies, a more comprehensive picture could be drawn by implementing multiple methodologies.

Implications

The findings of the present study are significant for both theory and practice. The study contributes to the literature by explicating the role of self-determined motivation and learning orientation in predicting task disengagement, which advances our theoretical understanding of the mechanisms that underlie learning (dis)engagement. More specifically, the present results highlight the centrality of autonomy and collaborative learning in reducing student task disengagement. Specifically, collaborative learning orientation mediates the link between autonomous motivation and task disengagement. This not only enriches the body of disengagement literature, but also widens the scope of empirical research on engagement.

The results of this study have also led us to speculate how disengagement might be purposefully manipulated, controlled, and reduced: Strengthening both collaborative learning orientation and sense of autonomy. Seeking effective methods to curb children’s disengagement from learning may start with efforts to help teachers place autonomy and collaborative learning at the forefront of their teaching. For example, decision-making opportunities can be implemented at various stages of English learning to enhance autonomy. Providing students with a choice of reading texts, tools, and resources or the freedom to decide the means of assessing the learning outcome can contribute to creating autonomy-supportive learning tasks. Instruction of this type of autonomous learning tasks is also suggested to be implemented in the form of collaborative learning, according to our findings. In this way, the positive role of autonomy in reducing task disengagement would be greatly strengthened.

Conclusion

In conclusion, the present study suggests that Chinese schoolchildren who adopt a collaborative learning orientation with a stronger sense of autonomy in their learning are less likely to disengage from academic work. This implies that, for autonomous motivation to play a role in reducing task disengagement, it needs to be coupled with collaborative learning orientations. Autonomy in English learning is clearly advantageous, but its true relationship with (dis)engagement deserves more attention. Further research is needed to delve deeper into the reasoning behind the advantages of cultivating Chinese children’s autonomy in reducing their disengagement in academic tasks.

	1	2	3	4	5	Mean	SD
1. Autonomous motivation	−					3.31	0.70
2. Controlled motivation	0.48**	−				3.55	0.51
3. Collaborative learning orientation	0.44**	0.21**	−			3.36	1.04
4. Competitive learning orientation	0.09	0.20**	0.29**	−		3.91	0.96
5. Task disengagement	−0.22**	−0.04	−0.21**	−0.01	−	1.35	0.57

Hypothesized path	Direct effect	Indirect effect	Total effect
Model A
AUTO → DISENGAGE (H₁)	−0.15	−0.14*	−0.29**
CTRL → DISENGAGE (H₂)	0.06	0.01	0.06
COLLAB → DISENGAGE (H₃)	−0.25**	0.00	−0.25**
COMP → DISENGAGE (H₄)	0.04	0.00	0.04
Model B
AUTO → DISENGAGE (H₁)	−0.11	0.00	−0.11
CTRL → DISENGAGE (H₂)	0.06	0.00	0.06
COLLAB → DISENGAGE (H₃)	−0.30**	−0.05	−0.35**
COMP → DISENGAGE (H₄)	0.08	0.03	0.10

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.

Author biographies

Mingming Zhou is an Assistant Professor in the Faculty of Education at the University of Macau. Before she joined the University of Macau, she was an Assistant Professor at the National Institute of Education, Singapore. Her research focuses on self-regulated learning with key constructs including achievement motivation, motivation, cognition, and behavorial regulation. She is also dedicated to developing innovative research methods for researching the development of self-regulated learning in online learning settings. She has been teaching education and psychology courses at undergraduate and graduate levels while involved in different international projects around the globe.

Jing Ren is a Master’s student in the Faculty of Education at the University of Macau, with a specialization in science education. Her research interests include different ways to improve student achievement performance within and outside school from a psychological perspective.

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