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Article

Do Homework Effort and Approaches Matter? Regulation of Homework Motivation Among Chinese Students

by
Jianzhong Xu
Department of Counseling, Higher Education Leadership, Educational Psychology, and Foundations, Mississippi State University, Mississippi State, MS 39759, USA
Educ. Sci. 2025, 15(6), 666; https://doi.org/10.3390/educsci15060666
Submission received: 1 April 2025 / Revised: 14 May 2025 / Accepted: 19 May 2025 / Published: 28 May 2025
Versions Notes

Abstract

:
Informed by multiple theoretical frameworks, our study examined multilevel models of homework motivation management among middle-schoolers in China. At the individual level, homework motivation management was positively associated with managing time, managing emotion, cognitive reappraisal, time on extracurricular activities, homework effort, and deep approach. In addition, there was a positive correlation between homework motivation management and homework time at both individual and class levels. Our study expands previous research by revealing that homework motivation management was positively associated with homework effort and deep approach after accounting for other relevant constructs. The implications of these findings are discussed relating to homework practices and further investigations.

1. Introduction

Commonly referred to as “tasks assigned to students by school teachers that are meant to be carried out during nonschool hours” (Cooper, 1989, p. 7), homework is a long-standing and well-established instructional practice worldwide (Fan et al., 2017; Fernández Alonso et al., 2022; Xu et al., 2024). By its very nature, homework is situated within the context of multiple competing activities that are often more enticing, attractive, or appealing during nonschool hours (Tas et al., 2016; Xu et al., 2020). With more tempting alternative activities and less structure and adult supervision compared to classwork, a notable challenge facing many students is the management of homework motivation (i.e., purposefully sustaining or enhancing their willingness to engage in and complete their homework; Fong et al., 2024; Wolters, 2011; Yang et al., 2016).
Drawing from self-regulated learning theory (Wolters & Benzon, 2013; Zimmerman, 2008) and expectancy–value theory (Eccles & Wigfield, 2002, 2020), previous research has linked homework motivation management to multiple variables, including background variables like prior achievement, expectancy belief, and students’ role such as managing time (Xu, 2014; Yang et al., 2016). Yet, previous research has largely overlooked other theoretically relevant variables, such as effort and deep approach from self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2020), growth mindset theory (Dweck, 2006; Dweck & Yeager, 2019), and student approaches to learning (SAL; Asikainen & Gijbels, 2017; Biggs, 2003). Recent research alluding to the predictive effects of these variables on the regulation of motivation (e.g., Fong et al., 2024; Villar et al., 2024) and homework motivation (e.g., Xu, 2023; Xu & Corno, 2022) underscores the need to include these variables.
Our research intends to address this gap by simultaneously exploring the associations between homework motivation management and constructs of theoretical interest. This line of inquiry is vital for middle school mathematics homework, as student motivation tends to decrease during secondary school education (Opdenakker et al., 2012; Smit et al., 2017), and as students generally receive a higher volume of mathematics homework compared to other subjects (Xu, 2018). In addition, the shift to a more abstract mathematics curriculum in middle school further exacerbates motivational issues, including elevated mathematics anxiety (Dettmers et al., 2011).
This line of inquiry is especially relevant for Chinese students for the following reasons. First, Chinese culture places a great emphasis on diligence, persistence, and perseverance (Li, 2001), prioritizing effort over innate ability as the cornerstone of academic success (Martin et al., 2014). Second, Chinese students often employ a distinctive learning approach known as “memorization with understanding” (Ho & Hau, 2008; Purdie & Hattie, 2002). Third, mathematics holds unique significance in Chinese culture, with success in this field closely linked to cultural identity (Mu, 2014). Consequently, many Chinese students experience substantial academic stress, particularly in mathematics, fueled by high parental expectations and intense peer competition (Zhao et al., 2015).

2. Theoretical Frameworks

Self-regulated learning theory is directly relevant to our study. Typically characterized by “self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals” (Zimmerman, 2005, p. 14), self-regulation involves individuals proactively setting goals, applying strategies to accomplish them, monitoring progress, and reflecting on outcomes (Zimmerman & Moylan, 2009).
Conceptualized as a key aspect of self-regulation, the regulation of motivation is commonly defined as monitoring and controlling one’s state or level of motivation and applying strategies to activate, sustain, or enhance one’s own motivation (Pintrich, 2004; Villar et al., 2024; Wolters, 2011). Pintrich’s framework considers motivation, together with behavior, context, and cognition, as one of four key areas for self-regulation, which encompasses four phases: forethought, monitoring, control, and reflection. Additionally, the regulation of motivation is examined within the volitional control framework (Boekaerts & Corno, 2005), which focuses on the implementation stage following goal setting and is marked by self-regulatory actions like purposive striving. Kuhl (1985) identified motivation control as a key covert strategy within his taxonomy of volitional strategies, involving the enhancement or maintenance of motivation when the intention is weaker than competing intentions.
These frameworks suggest that the regulation of motivation could be shaped by multiple variables. Self-regulation implies that individual and social differences might affect individuals’ attempts at regulation (Pintrich, 2004; Wolters, 2011). Hence, the regulation of motivation could be shaped by background characteristics such as parent education and prior knowledge, as well as adult monitoring from parents and teachers.
Pintrich’s (2004) framework suggests that the regulation of motivation could be shaped by other self-regulatory areas, including the regulation of behavior through time management and the regulation of context through managing one’s study environment. This aligns with existing literature, which suggests that contextual and behavioral factors can affect self-regulation (Eccles & Wigfield, 2020; Ramdass & Zimmerman, 2011).
Kuhl (1985), in his taxonomy of volitional strategies, highlighted emotion control as a crucial strategy, which involves regulating and managing inhibitory emotional states. Individuals who actively regulate unpleasant emotion may strengthen their intention to overcome motivational setbacks, suggesting a positive link between emotion control and the regulation of motivation. Additionally, cognitive reappraisal—reinterpreting a situation to change its emotional significance (Gross & Thompson, 2007)—might also positively influence the regulation of motivation.
In addition, expectancy value theory (Eccles & Wigfield, 2020) suggests that individuals are more likely to apply self-regulatory strategies if they believe they can succeed in a task and find it valuable or interesting (Pintrich & Zusho, 2002), and the regulation of motivation may be further shaped by expectancy, task value, and task interest (Schunk, 2005). Furthermore, opportunity cost can influence task engagement and persistence (Eccles & Wigfield, 2020), while the time allocated for sports, extracurricular, and television may further influence regulation of motivation.

3. Literature Review

This section first reviews prior studies on homework motivation management, then examines the literature on homework effort and approaches. It concludes by outlining gaps in the extant literature and presenting the primary research question and hypotheses of the present study.

3.1. Prior Research on Homework Motivation Management

Several studies have linked homework motivation management to homework procrastination, homework completion, and student achievements. These studies reported that homework motivation management is associated negatively with homework procrastination (Xu, 2024b), and positively with homework completion (Xu, 2014, 2024b; Yang & Xu, 2015) and student achievement (Xu, 2024b; Xu & Corno, 2022; Yang & Tu, 2020).
Some studies further imply a range of variables that might influence homework motivation management (Xu, 2014; Yang et al., 2016), including background variables such as gender, prior knowledge, and parent education; adult monitoring through family help and teacher feedback; student motivation beliefs such as expectancy, value, interest, and cost; and aspects of student initiative like time management, emotional regulation, and cognitive reappraisal. Xu (2014), studying 1611 secondary students in the U.S., reported that, at the class level, homework motivation management was positively related to parent education. At the student level, it was positively related to family help, homework interest, and managing time.
Involving 1799 high-schoolers in China, Yang et al. (2016) reported that, at the class level, the management of homework motivation was related positively to homework interest, and negatively to teacher feedback. At the individual level, it was associated negatively with value belief and time on TV, and positively with expectancy belief, homework interest, time on sports, managing time, managing emotion, and cognitive reappraisal.

3.2. Homework Effort and Student Approaches to Homework

In Trautwein et al.’s (2006) homework model, homework effort was introduced as a crucial construct in the homework process. Xu and Corno (2022) expanded this model by including both homework effort and the regulation of homework motivation as central constructs. Yet, the potential linkage between these two constructs has not yet been empirically explored. Hence, in the context of our study, it would be important to investigate the predictive effect of homework effort on the management of homework motivation for the following reasons. First, from the perspective of self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2020), when students perceive their effort as self-determined or self-endorsed, their autonomous motivation can be heightened. Greater effort invested in an activity that resonates with their interest may result in greater motivation. Second, according to growth mindset theory (Dweck, 2006; Dweck & Yeager, 2019), students are more inclined to stay motivated and persist through setbacks and challenges when they hold the belief that their ability could be cultivated through effort and learning. In this framework, personal effort emerges as an important factor in maintaining and enhancing motivation. Third, putting effort into homework has been linked to higher homework expectancy, greater homework interest, and better academic achievement (Xu, 2018; Xu & Corno, 2022). As students grow more confident, competent, and interested in their homework, they are more inclined to regulate and monitor their motivation throughout their homework journey.
An influential educational tradition has differentiated the SAL into two broad categories—deep and surface approaches (Asikainen & Gijbels, 2017; Biggs, 2003). A deep approach entails pursuing a meaningful, in-depth understanding of learned material by connecting different concepts, integrating new and prior knowledge, and applying learning across various contexts. By contrast, a surface approach relies on rote learning to fulfill external demands and requirements, such as passing an exam or avoiding trouble, while minimizing effort and engagement. Thus, students with a deep approach to learning are more inclined to regulate their motivation, as they are propelled by intrinsic factors such as curiosity, personal meaning, and inherent rewards in their learning, and driven by a genuine interest in understanding and integrating the material with their existing knowledge (Kohn, 2006). On the flip side, students with a surface approach to learning are less inclined to regulate their motivation, as they are driven by external demands and rewards, typically putting in the least effort necessary, rather than seeking a deep understanding fueled by an intrinsic interest in learning.
It is intriguing to observe that Asian students, particularly Chinese students, tend to use a unique learning approach referred to as “memorization with understanding” or “deep memorizing” (Chand et al., 2015; Ho & Hau, 2008; Liem et al., 2008). Purdie and Hattie (2002) identified two main perspectives associated with this approach, as follows: (a) it is easier to remember material that is already understood, and (b) understanding can be enhanced through the process of memorization. In other words, this approach involves a combination of deep and surface strategies, using memorization to ensure thorough retention while also facilitating deeper comprehension—viewing memorization and understanding as intertwined rather than mutually exclusive (Liem et al., 2008; Xu, 2024a; Zhao & Hu, 2020). Thus, for Chinese students, the surface approach may be less maladaptive than it typically is for Western students (Guo & Leung, 2021; Ho & Hau, 2008).
In reporting the findings, correlation coefficients (r) reflect the strength and direction of relationships, with accompanying p-values indicating statistical significance. Drawn from the SAL, recent studies have tapped into homework approaches (Rosário et al., 2014; Tas et al., 2016; Xu, 2023; Yang et al., 2024). Involving Chinese middle school students, Xu (2023) reported a very weak negative correlation between deep and surface approaches to homework (r = −0.07, p < 0.05). Additionally, the study found that homework interest was positively related to a deep approach (r = 0.50, p < 0.01) and negatively related to a surface approach (r = −0.18, p < 0.01). Given our discussion on the significance of task interest (Eccles & Wigfield, 2020), especially homework interest (Yang et al., 2016), in regulating homework motivation, there is a crucial need to investigate the predictive influences of student approaches to homework on the management of homework motivation.

3.3. The Present Investigation

Drawn from self-regulated learning and expectancy–value theories (Eccles & Wigfield, 2002, 2020; Pintrich, 2004; Wolters & Benzon, 2013), prior research has connected homework motivation management to multiple variables (e.g., Xu, 2014; Yang et al., 2016). They include background variables like gender, prior knowledge, and parent education; adult monitoring through family help and teacher feedback; motivational components such as expectancy belief, value belief, interest, and cost as reflected in after-school time use; and student initiative such as arranging homework environment, managing time, managing emotion, and cognitive reappraisal. However, these investigations did not connect homework motivation management to homework effort and homework approaches. Hence, it is crucial to bridge this gap in the present investigation.
Our primary research question is as follows: To what extent is homework motivation management associated with homework effort and homework approaches? Guided by self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2020), growth mindset theory (Dweck, 2006; Dweck & Yeager, 2019) and the SAL (Asikainen & Gijbels, 2017; Biggs, 2003), along with relevant homework research (Xu, 2018; Xu & Corno, 2022), we hypothesized that homework motivation management would be positively associated with homework effort. Drawn from the existing literature on the SAL (Asikainen & Gijbels, 2017; Biggs, 2003) and relevant homework research (Xu, 2023; Yang et al., 2016), we hypothesized that homework motivation management would be positively associated with the deep approach to homework. Due to the tendency of Chinese students to engage in “memorization with understanding” (Liem et al., 2008; Zhao & Hu, 2020), we were less certain about the relationship between homework motivation management and the surface approach, as it may be less maladaptive for Chinese students than for their Western counterparts (Guo & Leung, 2021; Ho & Hau, 2008).

4. Method

4.1. Participants and Procedure

The current study involved 1282 students from Grades 7–8 in China, all of the Han nationality. The participants included 50.8% male students from 34 classes in Yunnan and Fujian Provinces, both in southern China. To capture a broad representation of socioeconomic strata, students were selected from six regular public schools in these two provinces that permitted data collection. The sample size was determined following the recommendation of having a minimum of 30 groups, with approximately 30 individuals in each group (Kreft & de Leeuw, 1998; Maas & Hox, 2005). Grades 7 and 8 were selected, because they are widely classified as middle school grades. In contrast, Grade 6 is considered part of elementary school in countries like Spain (Núñez et al., 2015), whereas Grade 9 is typically considered part of high school in countries such as the United States (Pharris-Ciurej et al., 2012).
The participants were on average 13.2 years old (±0.5). Both mothers and fathers had similar educational levels, with 10.9 years (±3.0) and 11.0 years (±3.0) of schooling. This is closely aligned with the average of 10.8 years of education among China’s working-age population (T. Huang & Lardy, 2021).
Reflecting typical educational practices in China, the participants typically have mathematics daily, with each class period lasting between 40 and 45 min. They are anticipated to have a regular schedule and a designated space for homework, with parents optimizing the homework environment and providing necessary resources. About 72.7% of participants performed mathematics homework a minimum of five days a week, spending an average of 48 min (±30) on homework daily, consistent with prior studies on Chinese homework patterns (Xu, 2022; Yang & Tu, 2020).
The current investigation obtained parent consent and student assent, which gained approval from the institutional review board (MYRG2017-00122-FED). Data collection occurred approximately two and a half months into the school year to ensure that students had adequate exposure to their mathematics homework. Research assistants administered the measures (described in the following section) during regular school hours, and teachers were asked to leave the classroom to minimize potential response biases. Each student was assigned a unique code to connect survey responses with standardized mathematics scores collected approximately five months later, resulting in an overall response rate of 93%.

4.2. Instrumentation

4.2.1. Independent Variables

The participants provided information on their parents’ highest education level, spanning from elementary education (6 years) to a graduate degree (19 years). Given the high correlation between maternal and paternal education (r = 0.74, p < 0.001), we derived a composite measure termed “parent education”, calculated as the average years of schooling obtained by both parents.
In addition, the participants rated the frequency of family help they received, from never (1) to routinely (5). Furthermore, they provided information on the time spent on mathematics homework, sports, television, and extracurricular activities such as mathematics club, chess, and music.
To control for previous mathematics achievement, we retrieved participants’ grades from the conclusion of the preceding school year, as recorded in official transcripts. The grading scale ranged from F (failing) to A (excellent).
In our current study, multiple scales were used. Most of these scales have been validated in previous research in China (e.g., Xu et al., 2016; Yang & Xu, 2018; Yang et al., 2024). For readers’ convenience, the items for each scale and their reliability coefficients are presented in Table 1, whereas the corresponding response options are described in the table’s notes.
Teacher Feedback. Four items measured how frequently teachers provided comments and suggestions on mathematics assignments such as checking and correcting (α = 0.75; ω = 0.76). The reliability estimates mirror the findings from prior studies on Chinese students (Xu, 2023; Yang et al., 2016; 0.69 ≤ α ≤ 0.74).
Homework Interest. Four items assessed how much participants enjoyed and anticipated mathematics assignments (α = 0.92; ω = 0.93). The reliability estimates matched those reported in previous research on Chinese students (Xu & Corno, 2022; Yang et al., 2016; 0.91 ≤ α ≤ 0.94).
Homework Environment. Four items assessed the participants’ initiatives to select and restructure their homework setting, such as arranging supplies and resources for mathematics assignments (α = 0.69; ω = 0.70). The reliability estimates mirror the findings from previous studies on Chinese students (Yang & Tu, 2020; Yang & Xu, 2015; 0.67 ≤ α ≤ 0.72).
Managing Time. Three items assessed the participants’ initiatives to schedule and track their time for mathematics homework, such as tracking the amount of time spent on homework (α = 0.76; ω = 0.76). The reliability estimates match results from previous studies on Chinese students (Yang & Tu, 2020; Yang & Xu, 2015; 0.71 ≤ α ≤ 0.77).
Homework Emotion Regulation Scale (HERS). The HERS was used to assess emotion management and cognitive reappraisal (Xu et al., 2016, 2017). Three items measured emotion regulation, including up-regulating pleasant and down-regulating negative emotions (α = 0.86; ω = 0.86). Three items measured cognitive reappraisal such as interpreting negative stimulus in a less emotional way (α = 0.85; ω = 0.85). Similar to prior research (Xu et al., 2016, 2017), these two subscales were empirically distinguishable in the current study (CFI = 0.991; RMSEA = 0.043; SRMR = 0.022). The reliability estimates match those reported in prior studies on Chinese students (Xu et al., 2016, 2017) for emotion regulation (0.82 ≤ α ≤ 0.83) and cognitive reappraisal (0.87 ≤ α ≤ 0.89).
Homework Expectancy Value Scale (HEVS). We measured expectancy belief and value belief by applying the HEVS (Yang & Xu, 2018). Four items assessed expectancy belief such as one’s ability to successfully complete homework tasks (α = 0.84; ω = 0.84). Four items assessed value belief, such as the perceived utility of completing mathematics tasks (α = 0.91; ω = 0.91). Aligning with a previous validation (Yang & Xu, 2018), expectancy belief and value belief were factorially distinct in the current study (CFI = 0.982; RMSEA = 0.050; SRMR = 0.023). The reliability estimates closely align with those observed in previous studies on Chinese students (e.g., Xu & Corno, 2022; Yang & Xu, 2018) for expectancy belief (0.79 ≤ α ≤ 0.80) and value belief (0.84 ≤ α ≤ 0.86).
Homework Effort. Four items assessed the extent to which the participants seriously work on mathematics homework (Trautwein et al., 2015; Xu, 2018). The reliability estimates from the current investigation (α = 0.83; ω = 0.83) are congruent with those from prior studies involving Chinese students (e.g., Xu, 2018; Xu & Corno, 2022; 0.76 ≤ α ≤ 0.86).
Homework Approach Scale (HAS). This scale was used to measure deep approach and surface approach (Yang et al., 2024). The deep approach emphasized learning and comprehension (3 items; α = 0.81; ω = 0.81), while the surface approach focused on memorization and repetition (3 items; α = 0.83; ω = 0.83). In line with the findings of Yang et al. (2024), these two subscales were empirically distinguishable in the current study (CFI = 0.996; RMSEA = 0.021; SRMR = 0.023). The reliability estimates matched those observed in previous studies on Chinese students (Yang et al., 2024) for the deep approach (α = 0.79) and the surface approach (α = 0.80).

4.2.2. Dependent Variable

Motivation Management. Guided by existing literature on motivational regulation (Kuhl, 1985; Wolters, 2003, 2011; Yang et al., 2016), three items measured the participants’ initiatives to enhance or maintain their willingness to follow through mathematics assignments, including strategies such as interest enhancement, self-consequating, and efficacy self-talk (α = 0.76; ω = 0.76). The reliability estimates mirror the findings from previous studies involving Chinese students (Yang & Tu, 2020; Yang & Xu, 2015; 0.76 ≤ α ≤ 0.82). Concerning its validity evidence, in our study, motivation management was related negatively to homework procrastination (r = −0.36, p < 0.001), and positively to mathematics achievement (r = 0.28, p < 0.001) and homework completion (r = 0.37, p < 0.001).

4.3. Data Analysis

To handle the nested data structure, we conducted multilevel modeling following Raudenbush and Bryk’s (2002) approach. We first standardized the continuous variables, allowing the regression weights to be comparable to standardized coefficients used in the typical multiple regression procedure. The full maximum likelihood estimation was used for performing all analyses in HLM 8.2.
Model 1 contains sixteen individual-level variables and four class-level variables, as included in a prior study on high school students (Yang et al., 2016). Specifically, individual-level variables were gender, parent education, prior achievement, family help, teacher feedback, homework interest, arranging the environment, managing time, emotion management, cognitive reappraisal, expectancy belief, value belief, and time on homework, sports, extracurricular activities and TV. Class-level variables were grade, parent education, teacher feedback, and homework interest.
To examine the predictive effects of homework effort and homework approaches, Model 2 included three additional student-level variables—homework effort, deep approach, and surface approach. Model 1 and Model 2 were random-intercept models (Raudenbush & Bryk, 2002), as we did not have any priori hypotheses concerning the variance in predictive power of individual-level predictors between classes.
To separate individual and compositional effects (Graupensperger et al., 2019), we group-mean-centered parent education, teacher feedback, and homework interest at the individual level, and grand-mean-centered them at the class level. Missing data, which ranged from 0.0% to 3.7% (M = 0.9; SD = 0.7), were addressed by using the expectation-maximization method.

5. Results

5.1. Preliminary Analyses

While Likert scales are ordinal, averaging or summing multiple items can justify treating them as interval data (Carifio & Perla, 2008). Parametric statistical methods generally assume normality for interval data. Yet, these methods have demonstrated robustness even when this assumption is violated (Norman, 2010). In multilevel modeling, findings should remain unaffected as long as their distributions are approximately normal (Raudenbush & Bryk, 2002). In this study, the kurtosis and skewness values for the Likert-type scales were between −1 and +1, except for one instance where kurtosis the value for homework effort exceeded 1 (i.e., 1.46).
As parent education, teacher feedback, and homework interest were aggregated within each class to create class-level variables, it became necessary to assess the reliability of these combined measures (Miller & Murdock, 2007). Aggregate reliabilities (ICC2) were found to be 0.89 for parent education, 0.60 for teacher feedback, and 0.78 for homework interest. Hence, the aggregated reliabilities were considered appropriate for our study, as an aggregate reliability coefficient of 0.60 is generally viewed as satisfactory (Trautwein & Lüdtke, 2009).
Table 2 displays descriptive statistics and correlations for each variable. Except for gender and time spent on sports, motivation management was significantly related to all predictors.
We evaluated the Variance Inflation Factor (VIF) to check for possible multicollinearity. The results indicate that multicollinearity was not a significant issue, with all VIF values below 5 (Shrestha, 2020) and the highest being 2.44.

5.2. Multilevel Analyses

The null model was used to assess the variability in motivation management between and within classes. We found that 9.7% of the variance was associated with the class level, while 90.3% was associated with the student level.
Intraclass correlations (ICC1) were calculated by conducting null models, in which each predictor used as the dependent variable. The findings reveal ICC1 values spanning from 0.01 to 0.22, averaging 0.08 (SD = 0.06). They consist of parent education (0.18), prior mathematics achievement (0.22), family help (0.03), teacher feedback (0.03), homework interest (0.09), homework environment (0.14), managing time (0.10), managing emotion (0.08), cognitive reappraisal (0.05), expectancy belief (0.08), value belief (0.07), time on homework (0.16), time on sports (0.01), time on extracurricular activities (0.03), time on TV (0.05), homework effort (0.07), surface approach (0.03), and deep approach (0.09). An ICC1 as small as 0.02 or 0.01 can increase the risk of Type 1 error (Kreft & de Leeuw, 1998; Nielsen et al., 2021), and so subsequent analyses were performed applying multilevel models.
For the tests assessing the homogeneity of Level 1 variance, the null model (χ2 = 36.358, df = 33, p = 0.315) and Model 1 (χ2 = 45.850, df = 33, p = 0.068) were not significant, whereas Model 2 (χ2 = 48.871, df = 33, p = 0.037) was significant. However, fixed effects and standardized errors remain robust even if the assumption of homogeneity is violated (Garson, 2012). Furthermore, the histograms and normal Q-Q plot confirm that the Level 1 residuals were normally distributed.
Model 1, as outlined in Table 3, comprises sixteen variables at the individual level and four variables at the class level. By applying the likelihood ratio test, we found that Model 1 offers a significant improvement over the null model [χ2(20) = 909.627, p < 0.001]. Model 1 explains 48.9% of the variance in motivation management at the individual level, 97.5% at the class level, and 53.6% of the total variance.
To investigate the predictive influences of homework effort and homework approaches, Model 2 introduces three additional variables at the student level (homework effort, deep approach, and surface approach). The likelihood ratio test has revealed that Model 2 significantly improved upon Model 1 [χ2(3) = 27.901, p < 0.001], capturing an extra 1.1% of the variance at the student level.
Model 2 captures 50% of the variance at the individual level, 97.65 at the class level, and 54.6% of the total variance in motivation management. Congruent with our hypotheses, the management of homework motivation was found to be positively associated with homework effort and deep approach to homework. Additionally, at the individual level, motivation management was positively associated with managing emotion, cognitive reappraisal, managing time, and time spent on extracurricular activities. Finally, motivation management showed a positive relationship with homework interest at both the student and class levels.

6. Discussion

Informed by multiple theoretical perspectives and extant literature, our investigation examined multilevel models of students’ regulation of homework motivation. Notably, it explicitly linked homework motivation management to homework effort and homework approaches, which have been overlooked in the current research on homework motivation. In the next section, we examine predictors from previous research on homework motivation management, followed by a discussion of our findings concerning homework effort and homework approaches.

6.1. Predictor Variables in Prior Research

Aligning with theoretical frameworks on expectancy–value, self-regulation, and volitional control (Eccles & Wigfield, 2020; Kuhl, 1985; Pintrich, 2004; Wolters, 2011; Zimmerman, 2008) and a previous study involving high-schoolers (Yang et al., 2016), our findings reveal that homework motivation management was positively related to homework interest at both the student and class levels. Additionally, at the student level, homework motivation management was positively related to managing time, managing emotion, and cognitive reappraisal. Considering that Yang et al. (2016) studied high school students whereas our present study involved middle school students, these findings suggest that these relationships are likely applicable to both middle and high school students.
Meanwhile, the question remains: how do we explain our results that homework motivation management was unrelated to both expectancy belief and value belief, when a previous study on high school students (Yang et al., 2016) found that homework motivation management was related positively to expectancy belief and negatively to value belief? Regarding expectancy belief, one plausible explanation is that the predictive effect of expectancy belief on homework motivation management might be mediated by the deep approach to homework. Students with higher expectancy belief—confidence in their ability to do well on homework—are more inclined to apply a deep approach to homework, striving to understand concepts in depth and relate them to one another. This approach may make homework more meaningful and engaging, which, in turn, may enhance students’ capacity to manage and sustain their motivation. This explanation is supported by a related study that found that expectancy belief was positively related to a deep approach to homework (Xu, 2024a). Further substantiation comes from our supplementary analysis, in which homework motivation management was positively related to expectancy belief (b = 0.06, p = 0.03) after we removed the deep approach to homework from Model 2.
With respect to value belief, one likely explanation is that mathematics has traditionally been held in high esteem in China. Deeply rooted in Confucian culture, mathematics is a core element of “Chineseness” (Mu, 2014). Not surprisingly, it is a key and compulsory subject in the National College Entrance Examination (NCEE; Cai et al., 2019). Chinese high school students with high value belief may find less need to motivate themselves in mathematics assignments (Yang et al., 2016). Conversely, for the middle-schoolers in our study, the influence of value belief on managing homework motivation might be less pronounced. This is likely because of the following: (a) for approximately half of the middle school students entering regular high school, the NCEE is still a few years away, and (b) the remaining half either enroll in vocational school or drop out. Another plausible explanation is that younger students, like those in middle school, may be more driven by external sources of motivation such as parental expectations or teacher approval (e.g., focusing on compliance or avoiding punishment; Eccles & Wigfield, 2002, 2020), making their value belief less central to their homework motivation management. In addition, middle schoolers may not yet have a clear sense of how academic tasks, such as mathematics homework, link to their future goals (Woolley et al., 2013), leading to a weaker relationship between value belief and the regulation of homework motivation.
Our study indicates that homework motivation management was positively related to time on extracurricular activities, and not related to time on sports. These results differ from those of Yang et al.’s (2016) study of high-schoolers, in which there was a positive association between homework motivation management and time spent on sports, a negative association with time spent on TV, and no connection with time spent on extracurricular activities. One explanation is that middle-schoolers are still developing self-regulatory skills (e.g., time management). The extracurricular activities available to them tend to be more diverse and less specialized, offering broad developmental benefits without intense competition or stress. This supportive environment may help promote students’ self-regulation, positively affecting their management of homework motivation.
High-schoolers, who are more autonomous and face higher academic pressures (e.g., preparing for the NCEE), often participate in sports that require effective time management. This may enhance their ability to manage homework motivation. Yet, heightened academic stress and social distraction such as TV may undermine their ability to manage homework motivation. This explanation concerning academic stress is to some degree supported by our result that homework motivation management was unrelated to teacher feedback at the class level in our investigation, whereas a previous study on high schoolers (Yang et al., 2016) reported a negative relationship between homework motivation management and teacher feedback at the class level.

6.2. Homework Effort and Homework Approaches

The most central and noteworthy results of our investigation concern the predictive influences of homework effort and homework approaches on the management of homework motivation. Homework effort has been conceptualized as a key variable in homework models (Trautwein et al., 2006; Xu & Corno, 2022). In the last two decades, homework effort has been linked to a range of other major variables in these homework models, such as expectancy belief, homework interest, completion, and academic achievement (Dettmers et al., 2010; Rosário et al., 2018; Trautwein et al., 2006; Trautwein & Lüdtke, 2009; Xu, 2018; Xu & Corno, 2022). Although self-determination theory (Ryan & Deci, 2020) and growth mindset theory (Dweck, 2006), along with recent meta-analyses on motivational regulation (e.g., Fong et al., 2024; Villar et al., 2024) and homework studies (e.g., Xu, 2018), imply that homework effort may influence the management of homework motivation, no previous research has examined the predictive influence of homework effort on homework motivation management.
Thus, our study makes a significant advancement in revealing the beneficial role of homework effort in enhancing the management of homework motivation. This suggests that investing greater effort in homework can support homework motivation management by developing self-discipline and positive study habits like time management, by instilling and building resilience towards academic setbacks and challenges, and by promoting a sense of progress, confidence, and competence. In addition, for Chinese students in particular, sustained effort may gradually lead them to seek and cultivate genuine interest over time (Li, 2002; Xu, 2018), aligning with the long-standing notion that “if one works hard, one will slowly build up one’s interest and study skills” (Hau & Salili, 1996, p. 135). Notably, this beneficial role is evident in the realm of homework, even after considering other pertinent variables drawn from multiple theoretical frameworks in our models—self-regulation, expectancy-value, self-determination, growth mindset, and the SAL, as discussed below.
Building on recent research into the application of SAL in homework investigation (Rosário et al., 2014; Tas et al., 2016; Xu, 2023; Yang et al., 2024), our study makes another significant contribution by explicitly linking the deep approach and the surface approach to the management of homework motivation. The data reveal a positive relationship between a deep approach to homework and homework motivation management. This result empirically supports our hypothesis that the deep approach to homework can enhance the regulation of homework motivation, by fostering a deeper understanding and greater confidence in students, by making assignments more meaningful, engaging, and interesting, and by enhancing their resilience to challenges and setbacks. Thus, our study moves beyond previous studies on whether the deep approach links to homework interest, completion, and academic performance (Rosário et al., 2014; Tas et al., 2016; Yang et al., 2024b). Theoretically, the current investigation offers valuable insights by integrating multiple theoretical frameworks including self-regulation, expectancy–value theory, self-determination, growth mindset, and the SAL within the realm of homework. For example, as discussed in the previous section, our study revealed that the predictive effect of expectancy belief on homework motivation management may be mediated by the deep approach to homework, suggesting that the deep approach to homework emerges as a more powerful predictor than expectancy belief.
How do we make sense of the finding that the surface approach to homework was unrelated to homework motivation management? One possible explanation is that, as with other Eastern Asian students, Chinese students frequently use a distinctive learning approach known as “memorization with understanding” (Guo & Leung, 2021; Ho & Hau, 2008; Liem et al., 2008). This approach involves the simultaneous application of deep and surface approaches, such as using rote learning to deeply memorize and understand the material (Xie et al., 2022; Xu, 2024a). Consequently, whereas a surface approach to homework may not boost homework motivation, it may not necessarily undermine homework motivation either. Additionally, a surface approach to homework may occasionally be desirable and effective in the short run, enabling students to swiftly finish homework assignments for approval-seeking purposes. This is particularly relevant in collectivist countries such as China, where students are more inclined to internalize social approval goals in their academic tasks, including homework (King et al., 2014; Xu, 2022a). This may further buffer against any negative association between a surface approach to homework and homework motivation management.
Compared with Model 1 (excluding homework effort and homework approaches), Model 2 captured an extra 1.0% of the total variance in homework motivation management. At first glance, the overall effect size might appear small. Yet, various factors ought to be taken into account when evaluating its practical significance. First, homework motivation management is influenced by a broad range of variables informed by self-regulation and expectancy–value theories, and homework effort and homework approaches are among a few factors influencing the dynamics of homework motivation management. Second, it is crucial to recognize that our models included several strong predictors (e.g., cognitive reappraisal and managing emotion), which considerably limit the variance that could be attributed to homework effort and homework approaches. Third, in many contexts, small effect sizes can still be meaningful (Trautwein et al., 2012). In this study, the predictive effects of homework effort and the deep approach are expected to accumulate and exert a stronger influence on homework motivation management over time. This is particularly meaningful and significant given that student motivation often declines during secondary school years (Opdenakker et al., 2012; Smit et al., 2017).

6.3. Strengths, Limitations and Implications for Further Research

Drawn from multiple theoretical frameworks, namely, self-regulated learning theory (Pintrich, 2004; Wolters, 2011), expectancy-value theory (Eccles & Wigfield, 2002, 2020), self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2020), growth mindset (Dweck, 2006; Dweck & Yeager, 2019), and the SAL (Asikainen & Gijbels, 2017; Biggs, 2003), alongside insights from previous studies (Fong et al., 2024; Villar et al., 2024; Xu, 2018, 2023; Yang et al., 2016), the present investigation establishes explicit linkages between the management of homework motivation and multiple theoretically pertinent variables using multilevel analyses. Yet, no previous investigation has explored the combined predictive effects of these variables on homework motivation management. The current study notably advanced previous research by revealing that homework motivation management was positively related to homework effort and deep approach, even when accounting for other theoretically relevant constructs. Moreover, the current study highlighted the advantages of integrating multiple frameworks—self-regulated learning, expectancy–value, self-determination, growth mindset, and the SAL—in the context of motivation regulation, particularly in managing homework motivation.
While our study highlights the importance of our models in revealing the multifaceted factors influencing homework motivation management, it is important to acknowledge several limitations. First, aside from including prior mathematics achievement and subsequent mathematics achievement to provide predictive validity evidence related to homework motivation management, our findings were based on cross-sectional survey data. In the view of Trautwein and Lüdtke (2009), “effect” in this context ought to be regarded as a “predictive effect”. Hence, consistent with the recommendation by Fong et al.’s (2024) meta-analytic investigation on motivational regulation, future research could address this limitation by incorporating experiments, observations, trace methods, and cross-lagged panel analyses.
Second, although our study draws on multiple frameworks including self-regulated learning, expectancy–value, self-determination, growth mindset, and the SAL, unobserved predictors, such as time spent on the internet, after-school tutoring, or peer influence, might have affected the management of homework motivation. Hence, it would be desirable for future research to consider these variables.
Third, our investigation centers on mathematics homework in middle schools in China. Yet, the regulation of motivation can be affected by grade level and geographic regions (Fong et al., 2024). Additionally, as discussed above, homework motivation management may be influenced by subject matter, school level, and cultural differences, including value belief and time on extracurricular activities. Thus, it would be beneficial to explore the patterns of homework motivation management across different subjects, educational levels, and cultural contexts.
Finally, our investigation adopts a variable-centered approach to examine homework motivation management, incorporating major constructs from various frameworks such as self-regulated learning, expectancy–value, self-determination, growth mindset, and the SAL across the entire sample. Given that examining the simultaneous influences of theoretically relevant constructs can provide a more comprehensive understanding of motivational regulation (Z. Huang et al., 2024), adopting a person-centered approach to explore these interactions within specific subgroups would be informative.

6.4. Practical Implications

Since this investigation is the first to connect motivation management with homework effort and approaches, any practical implications ought to be considered cautiously. Nevertheless, our findings may offer useful insights for enhancing the management of homework motivation.
Our result, indicating a positive relationship between homework motivation management and homework effort, underscores the need to foster homework effort throughout the homework process. It is crucial to nurture a growth mindset by promoting positive views of effort (Dweck & Yeager, 2019; Murphy & Dweck, 2010). For instance, telling students that it is vital to put in their best effort even if they do not always get homework assignments right helps instill the belief that effort results in improvement, progress, and academic success. In addition, viewing challenges and setbacks as opportunities to develop and learn instead of obstacles to avoid is vital in fostering this mindset. Similarly, it would be equally important to recognize, acknowledge, and celebrate not only students’ homework completion and performance, but also their effort, improvement, and growth over time.
Moreover, connecting homework to students’ interests and real-life experiences may enhance its relevance and engagement. This is especially important given that (a) homework interest and homework effort were found to be reciprocally related (Xu, 2018), and (b) in this study, homework interest at both the student and class levels was positively predictive of homework motivation management. Teachers may integrate real-world scenarios tied to students’ interests, such as analyzing sports statistics and constructing virtual models, as well as their career aspirations, like simulating career-related tasks reflective of various professions. Additionally, incorporating social media and educational games that challenge students with mathematical problems—such as tracking growth rates of likes and followers on social platforms, or playing “Math Jeopardy” and “Treasure Hunts”—may make homework more appealing and engaging.
Our result indicates a positive association between homework motivation management and a deep approach to homework, highlighting the importance of focusing on this approach. It would be beneficial to focus on optimizing the planning, preparation, and integration of homework assignments into lessons to enhance students’ understanding of the material. This may involve designing relevant and engaging homework tasks that require problem-solving and critical thinking, while also breaking them down into manageable steps with appropriate support and guidance. This may also entail concentrating on the deeper meanings and forming connections between key mathematical concepts, models, and ideas, as well as connections between new knowledge and previous experiences. This could further entail offering high-quality homework feedback—prompt, clear, detailed, and constructive—to assist students in bridging the gap between their present and desired performance, as feedback quality positively predicted homework interest (Xu, 2023), which both the current investigation and a previous study (Yang et al., 2016) found to be positively associated with the management of homework motivation.
Finally, it would be highly desirable to invite students to express their thoughts on what helps them stay motivated with their homework and how teachers and families can enhance their support in managing homework motivation. By taking into account students’ viewpoints, teachers and families can offer more tailored support, providing more personalized assistance, such as time management, while fostering a classroom climate that champions curiosity, effort, persistence, growth, and deep understanding.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board, University of Macau (MYRG2017-00122-FED).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be available upon request.

Conflicts of Interest

The author declares no conflicts of interest.

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Table 1. Items and alpha/omega * reliability estimates.
Table 1. Items and alpha/omega * reliability estimates.
ScalesItemsαω
Teacher feedback a “How much of your math HW is collected by math teacher?”
“How much of your math HW is checked for completion by math teacher?”
“How much of your math HW is checked for accuracy by math teacher?”
“How much of your math HW is corrected by math teacher?”		0.750.76
HW interest“I look forward to mathematics HW”. b
“Mathematics HW is fun”. b
“I enjoy mathematics HW”. b
“How do you like about mathematics HW in general?” c		0.920.93
Arranging the environment d“Locate the materials I need for my math homework”.
“Find a quiet area”.
“Make enough space for me to work”.
“Turn off the TV”.		0.690.70
Managing time d“Set priority and plan ahead”.
“Keep track of what remains to be done”.
“Remind myself of the available remaining time”.		0.760.76
Emotion management d“Tell myself not to be bothered with previous mistakes”.
“Tell myself to calm down”.
“Cheer myself up by telling myself that I can do it”.		0.860.86
Cognitive reappraisal d“I think that there are good sides to it as well”.
“I think that I can learn something from the situation”.
“I think that it’s not all bad”.		0.850.85
HW expectancy ef“If I don’t understand something in math, I often think I’ll never understand it”.
“If I don’t understand something in math, I’m at a complete loss and don’t know how to catch up”.
“Whether or not I do my math HW, I don’t understand a thing in the lesson anyway”.
“I sometimes really dread mathematics HW”.		0.840.84
HW value ef“Our math HW takes a lot of time and is of little use to me”.
“I don’t learn much from our math HW”.
“There is no point in my doing math HW”.
“It makes barely any difference to me whether I do my math HW or not”.		0.910.91
HW effort e“In math HW, I invest much effort to understand everything”.
“I have recently been doing my math HW to the best of my ability”.
“I do my best on my math HW”.
“I always try to finish my math HW”.		0.830.83
Deep approach g“When I do math HW, I think about different ways to solve a math problem”.
“I ask myself questions about topics in math HW to check whether I understand the topics”.
“I find that doing math HW can at times be exciting as a good novel or movie”.		0.810.81
Surface approach g“I generally restrict my math HW to what is specifically set as I think it is unnecessary to do anything extra”.
“I see no point in learning math materials which is not likely to be in the examination”.
“I find I can get by in most math assignments by memorizing key sections rather than trying to understand them”.		0.830.83
Motivation management d“Find ways to make math HW more interesting”.
“Praise myself for good effort”.
“Reassure myself that I am able to do math HW when it is hard”.		0.760.76
Note. * Cronbach’s alpha (Cronbach, 1951) and McDonald’s omega (McDonald, 1999). HW = homework. a None (1), Some (2), About half (3), Most (4), All (5). b Strongly disagree (1), Disagree (2), Neither disagree nor agree (3), Agree (4), Strongly agree (5). c Don’t like it at all (1), Don’t like it some (2), Neither like nor dislike it (3), Like it some (4), Like it very much (5). d Never (1), Rarely (2), Sometimes (3), Often (4), Routinely (5). e Strongly disagree (1), Disagree (2), Agree (3), Strongly agree (4). f Reverse-scored. g Not at all true of me (1) to Very true of me (7).
Table 2. Descriptive statistics and correlations.
Table 2. Descriptive statistics and correlations.
VariablesMSDSK1234567891011121314151617181920212223
1 Gender (female = 0)0.510.50−0.05−2.00---
2 Parent education10.962.790.38−0.350.03---
3 Prior math achievement3.311.54−0.35−1.39−0.030.36 †---
4 Family help2.861.050.09−0.530.030.13 †0.02---
5 Teacher feedback3.640.87−0.50−0.100.010.000.09 †0.18 †---
6 Homework interest3.490.90−0.640.43−0.020.10 †0.30 †0.27 †0.28 †---
7 Homework environment3.740.86−0.660.22−0.17 †0.18 †0.33 †0.16 †0.21 †0.35 †---
8 Managing time3.141.01−0.12−0.51−0.09 †0.11 †0.26 †0.17 †0.20 †0.39 †0.55 †---
9 Managing emotion3.570.98−0.51−0.17−0.09 †0.18 †0.38 †0.21 †0.27 †0.47 †0.49 †0.52 †---
10 Cognitive reappraisal3.191.06−0.05−0.600.000.08 †0.18 †0.16 †0.17 †0.30 †0.29 †0.39 †0.59 †---
11 Homework expectancy3.040.70−0.55−0.020.020.18 †0.40 †0.08 †0.12 †0.38 †0.27 †0.28 †0.41 †0.20 †---
12 Homework value3.270.68−0.770.43−0.11 †0.13 †0.30 †0.06 *0.17 †0.34 †0.34 †0.29 †0.40 †0.20 †0.47 †---
13 Time on homework47.8730.271.050.820.030.13 †0.13 †0.020.040.020.21 †0.19 †0.13 †0.030.050.06 *---
14 Time on sports56.3252.871.270.890.31 †0.05−0.07 *0.13 †0.07 *0.04−0.030.010.010.06 *0.01−0.02−0.04---
15 Time on extracurricular47.2945.651.472.170.15 †0.03−0.030.10 †0.030.030.010.03−0.020.04−0.02−0.08 †−0.10 †0.50 †---
16 Time on TV46.1551.251.381.360.11 †−0.09 †−0.13 †−0.010.07 *−0.09 †−0.17 †−0.12 †−0.11 †−0.02−0.16 †−0.12 †0.020.34 †0.45 †---
17 Homework effort3.070.55−0.541.46−0.12 †0.15 †0.29 †0.16 †0.20 †0.43 †0.41 †0.39 †0.46 †0.30 †0.26 †0.34 †0.12 †−0.06 *−0.05−0.11 †---
18 Deep approach4.691.64−0.430.51−0.040.14 †0.33 †0.21 †0.21 †0.47 †0.29 †0.31 †0.43 †0.30 †0.32 †0.30 †0.050.020.02−0.08 †0.29 †---
19 Surface approach2.871.740.72−0.410.21 †−0.02−0.15 †0.040.01−0.14 †−0.17 †−0.09 †−0.15 †−0.02−0.28 †−0.30 †−0.050.10 †0.10 †0.16 †−0.13 †0.00---
20 Grade (7th = 0)0.590.50−0.38−1.98−0.01−0.07 *−0.08 †−0.060.07 †0.000.000.030.040.10 †−0.06 *−0.03−0.010.02−0.06 *0.03−0.040.02 †0.05---
21 Parent education-C10.871.28−0.12−1.51−0.010.45 †0.41 †−0.030.000.11 †0.30 †0.15 †0.20 †0.050.26 †0.19 †0.32 †−0.010.01−0.11 †0.21 †0.17 †−0.16 †−0.16 †---
22 Teacher feedback-C3.640.20−0.21−0.340.010.000.08 †0.06 *0.23 †0.19 †0.14 †0.17 †0.17 †0.14 †0.10 †0.07 †0.07 *0.00−0.06 *−0.07 *0.09 †0.19 †0.030.33 †−0.01---
23 Homework interest-C3.470.31−0.19−0.500.020.14 †0.26 †0.13 †0.13 †0.33 †0.28 †0.27 †0.26 †0.18 †0.21 †0.18 †0.17 †0.01−0.01−0.11 †0.22 †0.28 †−0.020.010.32 †0.57 †---
24 Motivation management3.090.95−0.02−0.35−0.020.13 †0.32 †0.22 †0.25 †0.48 †0.41 †0.49 †0.67 †0.53 †0.35 †0.29 †0.10 †0.050.06 *−0.06 *0.43 †0.43 †−0.11 †0.06 *0.15 †0.21 †0.29 †
Note: C = Class. S = Skewness. K = Kurtosis. * p < 0.05. † p < 0.01.
Table 3. Multilevel results for homework motivation management.
Table 3. Multilevel results for homework motivation management.
Model PredictorNull ModelModel 1Model 2
bSEbSEbSE
Student level
Gender (female = 0) 0.050.040.070.04
Parent education −0.010.02−0.010.02
Prior mathematics achievement 0.040.020.030.02
Family help 0.030.020.020.02
Teacher feedback 0.020.020.020.02
Homework interest 0.14 ***0.030.10 ***0.02
Homework environment 0.030.020.020.02
Managing time 0.11 ***0.030.10 ***0.03
Managing emotion 0.37 ***0.030.34 ***0.03
Cognitive reappraisal 0.18 ***0.020.17 ***0.02
Expectancy belief 0.05 *0.030.050.03
Value belief −0.020.03−0.040.03
Time on homework 0.000.020.000.02
Time on sports −0.020.02−0.010.02
Time on extracurricular activities 0.06 *0.020.06 **0.02
Time on TV 0.000.020.010.02
Homework effort 0.09 ***0.03
Deep approach 0.08 **0.02
Surface approach −0.030.02
Class level
Grade (7th = 0) 0.030.050.030.05
Parent education −0.040.05−0.070.05
Teacher feedback 0.190.100.180.09
Homework interest 0.25 *0.100.20 *0.10
Residual (σ2)0.907 (0.036)0.463 (0.019)0.453 (0.018)
Intercept (τ00)0.097 (0.030)0.002 (0.004)0.002 (0.003)
Explained variance
Within classes 48.9%50.0%
Between classes 97.5%97.6%
Total 53.6%54.6%
Deviance statistics (parameters)3566.950 (3)2657.323 (23)2629.422 (26)
Note. * p < 0.05. ** p < 0.01. *** p < 0.001.
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Xu, J. Do Homework Effort and Approaches Matter? Regulation of Homework Motivation Among Chinese Students. Educ. Sci. 2025, 15, 666. https://doi.org/10.3390/educsci15060666

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Xu J. Do Homework Effort and Approaches Matter? Regulation of Homework Motivation Among Chinese Students. Education Sciences. 2025; 15(6):666. https://doi.org/10.3390/educsci15060666

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Xu, Jianzhong. 2025. "Do Homework Effort and Approaches Matter? Regulation of Homework Motivation Among Chinese Students" Education Sciences 15, no. 6: 666. https://doi.org/10.3390/educsci15060666

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Xu, J. (2025). Do Homework Effort and Approaches Matter? Regulation of Homework Motivation Among Chinese Students. Education Sciences, 15(6), 666. https://doi.org/10.3390/educsci15060666

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