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What Keeps Chinese Students Motivated in Doing Math Homework? An Empirical Investigation


by Fuyi Yang, Jianzhong Xu, Heping Tan & Ningjian Liang - 2016

Background: As many students face the enduring challenge of maintaining their motivation to complete homework assignments, there is a critical need to pay close attention to homework motivation management (i.e., students’ efforts to sustain or enhance their motivation in order to complete homework assignments that might be boring or difficult). Yet, in spite of research showing that homework motivation has a powerful influence on homework performance and academic achievement, there have been few attempts to systematically investigate models of factors that influence homework motivation management.

Purpose: The current investigation examines empirical models of motivation management for Chinese students in the context of math homework, with models informed by key motivation theories, including self-regulation of motivation, expectancy-value, and volitional control.

Research Design: The study reported here used cross sectional survey data.

Participants: The participants were 1,799 high school students from 46 classes in China.

Results: Results from multilevel analyses indicated that, at the class level, motivation management was positively associated with homework interest, but was negatively associated with teacher feedback. At the student level, motivation management was positively related to managing emotion, managing time, students' interest in homework, cognitive reappraisal, expectancy belief, and time on sports. Meanwhile, motivation management was negatively associated with value belief and time on TV.

Conclusion: The current investigation found that some results were in line with key motivation theories and related findings with U.S. students (e.g., managing time, and homework interest at the student level). In addition, it suggests that other results may be influenced by cultural and societal differences (e.g., gender, value belief, and teacher feedback). Consequently, it would be informative to pursue this line of research in cross-cultural settings. With respect to promoting student motivation in the homework process, it would be beneficial to pay more close attention to the role of homework interest, expectancy belief, and student initiatives (e.g., managing time, managing emotion, and cognitive reappraisal).



While it is generally a challenge to keep students motivated throughout academic tasks, it becomes more of a challenge when students need to complete academic tasks outside school hours (Wolters, 2011). This is especially the case with homework. First, homework frequently involves assignments that many students consider as unimportant, irrelevant, or boring (Warton, 2001; Xu, 2008a). Second, it occurs in the midst of competing and often more attractive after-school activities (e.g., sports and television; Verma, Sharma, & Larson, 2002; Xu & Corno, 1998). Third, it places more demands on students' self-regulation (e.g., keeping themselves motivated in the process; Fries, Dietz & Schmid, 2008; Wolters, 2011), as they need to follow through assignments with less structure, monitoring, and time constraints than classwork (Cooper, Robinson, & Patall, 2006; Corno & Xu, 2004), and as increased autonomy fosters many obstacles that can disrupt students’ motivation (Wolters, 2011).


Motivation can be defined as individuals’ willingness to engage in and persist in a task as well as the processes that govern their choice, effort, and persistence (Winne & Marx, 1989; Wolters, 2003). As students often encounter motivational obstacles relating to homework completion (Cooper & Corpus, 2009; Wolters, 2003), it would be important to pay close attention to motivation management in homework, which can be referred as to students’ efforts to address motivational obstacles and to keep themselves motivated in the homework process (i.e., to maintain a productive level of engagement while doing homework). Specifically, homework motivation management may include to students’ efforts to influence, maintain, or enhance their motivation to follow through homework that might be viewed as boring or difficult, by making homework assignments more interesting for themselves, and by reassuring themselves about their ability to complete homework assignments successfully. Yet, in spite of research showing that homework motivation has a powerful influence on homework performance (e.g., Iflazoglu & Hong, 2012) and academic achievement (e.g., Patall, Cooper, & Wynn, 2010), there have been few attempts to systematically investigate empirical models of factors that influence students' efforts to manage their homework motivation.


The current investigation attempts to fill this gap by examining empirical models of motivation management in the context of math homework for Chinese students. A study such as this may shed new light on factors that keep students motivated in math homework, because Chinese students often hold more positive attitudes about homework (e.g., Cai, 2003; Hong, Wan, & Peng, 2011), and because they tend to surpass students in many other countries in a series of international assessments of math achievement (e.g., TIMSS; Mu, 2014; Sellar & Lingard, 2013; Wang, 2004).


THEORETICAL FRAMEWORKS


Viewed as a critical and distinct aspect of self-regulation (Boekaerts, 1997; Corno, 1993, 2001; Winne, 2001; Wolters, 2011; Zimmerman, 2008), regulation of motivation is typically viewed as individuals' efforts to change, control, or manage their motivation (e.g., Pintrich, 2004; Wolters, 2011). Pintrich (2004) explicitly conceptualized motivation as one of four areas of academic functions for regulation, the others being cognition, behavior, and context. For this model, each of these four academic functions has four phases, including forethought, monitoring, control, and reflection. Thus, “students can have an awareness of their motivation, can be unsatisfied with it, and can take steps to intervene and improve this aspect of their academic functioning” (Wolters, 2011, p. 269).


Regulation of motivation is also frequently discussed under the umbrella of volitional control (Boekaerts & Corno, 2005; Kuhl, 2000). Volitional control emphasizes issues of implementation that takes place after a goal is determined, typified by self-regulatory activities such as persistent and purposive striving. In his taxonomy of volitional strategies, Kuhl (1985) conceptualized motivation control as one important covert strategy; it involves strengthening or maintaining the motivation when the intention is weak compared with other rival intentions. Motivation control or regulation of motivation bears direct relevance to homework completion, as students are required to monitor motivation, such as sustaining or enhancing mo­tivation to follow through homework when faced with various motivational obstacles (e.g., appealing temptations or competing personal strivings).


Whereas no specific model of regulation of motivation has emerged, relevant literature implies that it may be affected by a range of factors. First, self-regulation suggests that biological, individual, and developmental differences may influence students’ attempts at regulation (Pintrich, 2004; Wolters, 2011). Thus, regulation of motivation is likely to be affected by background variables (e.g., gender and academic performance) and by adult monitoring (e.g., teachers and parents). Students with higher academic performance exhibit more self-regulatory skills (Zimmerman & Martinez-Pons, 1990). Females are more self-reliant and self-disciplined (Duckworth & Seligman, 2006; Xu, 2006), exhibit more planning skills (Zimmerman & Martinez-Pons, 1990), and have stronger effort management (Pokay & Blumanfeld, 1990).


Pintrich’s (2004) model implies that regulation of motivation is affected by students’ efforts to control their own behaviors, such as time regulation (e.g., planning ahead to meet deadlines) and environment regulation (e.g., arranging an organized environment for studying). This is consistent with related literature that behavioral, contextual, and cognitive factors may influence self-regulation (Eccles & Wigfield, 2002; Zimmerman, 2008) and that students' efforts to personalize studying environment and to manage time may support their motivation to work (Xu & Corno, 2003).


Furthermore, regulation of motivation is likely to be influenced by task interest and task value. As an individual showing an interest in a task and considering the task as valuable is more likely to employ desirable self-regulation strategies (Pintrich & Zusho, 2002), task interest and task value may affect regulation of motivation (Schunk, 2005). Similarly, consistent with the expectancy-value theory (Eccles, 1983), Warton (2001) stated the critical role of the following factors in task completion, including (a) task interest, (b) task utility, and (c) task cost (i.e., opportunity costs resulting from doing homework, such as restricting time available for sports, TV, and extracurricular activities).


In addition, based on the expectancy-value theory (Eccles, 1983; Eccles & Wigfield, 2002), individuals are more likely to engage and persist in an academic task if they think they can be successful in performing the task (i.e., expectancy belief). Thus, students’ efforts to regulate their motivation may be influenced by their expectancy for successfully completing homework.


In his taxonomy of volitional strategies, Kuhl (1985) elaborated another volitional strategy called emotion control (i.e., efforts to keep inhibiting emotional states in check). As those individuals who take efforts to manage unpleasant emotion are more likely to strengthen their intention to deal with motivational obstacles, regulation of motivation may be positively associated with emotion control. Similarly, as cognitive reappraisal as a form of cognitive change that may involve changing or reframing a situation's meaning to alter its emotional impact (Gross & Thompson, 2007), regulation of motivation may be further positively related to cognitive reappraisal.


Taken together, regulation of homework motivation may be affected by a number of factors (e.g., background variables, take interest, expectancy belief, and students’ efforts to regulate time, environment, and emotion). Consequently, there is a need to include these variables in models of regulation of homework motivation.


STUDIES PERTAINING TO REGULATION OF MOTIVATION IN HOMEWORK


Previous studies have implied a number of factors may affect students’ effort to regulate homework motivation, including family homework help, gender, and student attitudes toward homework. For example, Xu and Corno (1998) investigated six families involving third-grade homework. Whereas children liked some assignments, compared with other after-school activities, completing homework was not their favorite activity. As a result, their parents devoted much of the attention to motivate their children to follow through homework assignments, by promoting their confidence that they could do well with their work, and by helping to make an assignment more interesting. In addition to family help, another study (Xu & Corno, 2006) explicitly linked gender to homework motivation management. Data revealed that those students who received family help took more initiatives to keep themselves motivated in the homework process. In addition, females stated that they took more initiatives to keep themselves motivated.


Meanwhile, the study by Trautwein, Ludtke, Schnyder, and Niggli (2006) has implied that teachers may influence motivation regulation in homework. Their study found that teacher control was associated with homework effort at the individual level, suggesting that teacher may affect students' effort in homework, with homework motivation in particular.


Other studies further imply that student attitudes may influence homework motivation management. Xu and Yuan (2003) found that many students complained that homework assignments were often uninteresting or irrelevant to their lives. One group of the students took a more matter-of-fact approach to homework (e.g., “I’m just used to doing my homework and that’s it.”) Another group took a more negative approach to homework (e.g., “I don’t like doing it. It makes me upset, and I don’t want to do it.”) These findings suggested that attitudes toward homework affected the way that students approached homework, and with homework motivation management in particular.


Much of what we have learned relating to regulation of homework motivation, however, (a) was drawn from qualitative studies (e.g., Xu & Yuan, 2003) or studies that were not designed to examine regulation of motivation in homework (e.g., Trautwein et al., 2006), and (b) had not incorporated a multilevel perspective to differentiate between individual- and class-level influences (e.g., Xu & Corno, 2006).


To address these gaps, Xu (2014) investigated empirical models of motivation management in homework, as reported by 866 students in grade 8 (61 classes) and 745 students in grade 11 (46 classes). At the individual level, homework motivation management was positively related to family homework help, learning- and peer-oriented reasons, homework interest, and students' initiatives in managing time and arranging studying environment. Compared with males, females reported that they took more initiatives to manage motivation. At the class level, parent education was positively associated with homework motivation management. Overall, the study explained 39.9% of the variance in motivation management at the individual level, and 89.0% of the variance at the class level. Although these findings were interesting and extended the previous research on regulation of homework motivation, the study by Xu (2014) was limited to U.S.  secondary school students.


CULTURAL INFLUENCES


As a Confucian heritage culture, China attaches great importance to education and educational activities (Ho, 1994; Wang, 2004). It believes more in effort exerted (rather than ability inherited) as the route for educational success (Chen & Uttal, 1988; Rao, Moely, & Sachs, 2000; Salili, Zhou, & Hoosain, 2003), emphasizing the value of diligence, endurance of hardship, steadfastness, and concentration (Li, 2001). In addition, Chinese culture places specific emphasis on math learning; doing well in math is embedded with being "Chineseness" (Mu, 2014), which can be traced back in ancient Chinese classics (e.g., one of the six important skills; Siu, 1995). Not surprisingly, these values affect how Chinese approach homework (with math homework in particular) as one of the major after-school activities.


Several studies have investigated the China–U.S. differences in homework attitudes and practices. For example, Cai (2003) examined the differences between Chinese parents and U.S. parents in their children's math learning. Data revealed that Chinese parents were more likely to encourage children to do well in math, to monitor time spent on math learning, and to check their homework. Similarly, the study by Chen and Stevenson (1989) indicated that, compared with U.S. counterparts, Chinese teachers considered homework more important and assigned more homework. In addition, Chinese children (compared with U.S. counterparts) held more positive attitudes toward homework. The authors found that, compared with U.S. children, "the motivation of Chinese children is especially interesting," in the sense that “despite the large amounts of homework they were assigned, they did not develop a negative attitude about homework” (pp. 557–558). These results are consistent with related findings that, compared with U.S. counterparts, Chinese students tend to be more positive about homework and spend more time on homework (Cai, 2003; Peng, Hong, Li, Wan, & Long, 2010; Tam, 2009).


On the other hand, some studies (e.g., Lin & Chen, 1995) argued that constant and excessive pressure for high academic achievement from parents, teachers, and the society may make Chinese students passive learners. In addition, Chinese students are likely to have high academic burden and stress, due to high expectations of their families and intense competitions with their peers, particularly for those students whose test scores cannot live up to high performance expectations (Sun, Dunne, & Hou, 2012). These studies imply that some Chinese students may be less likely to take initiatives to sustain their homework motivation.


Taken together, Chinese parents, students, and teachers (compared with U.S. counterparts) tend to hold different attitudes about homework (e.g., Cai, 2003; Peng et al., 2010). Because attitudes toward homework may influence regulation of homework motivation (Xu, 2014; Xu & Yuan, 2003), there is a critical need to investigate empirical models of factors affecting Chinese students' motivation management in homework.


THE PRESENT INVESTIGATION


The present investigation seeks to address several gaps in previous research on homework motivation. First, regulation of homework motivation is markedly absent from much of the research on homework (Xu, 2014). This is perhaps not surprising, as "until recently, regulation of motivation was not often disaggregated from other aspects of self-regulation" (Wolters, 2011, p. 271). Although the study by Xu (2014) extended the previous homework research, it was limited to U.S. students. Thus, it would be important to study regulation of homework motivation in China, as regulation of motivation can be influenced by cultural differences (Boekaerts, 2011; Wolters, 2011), and as China and the United States represent two countries in East and West that differ regarding homework motivation (e.g., Cai, 2003).


Second, the study by Xu (2014) examined motivation management in homework across different school subjects. Given that others studies have examined domain-specific aspects of homework motivation (Hong, Peng, & Rowell, 2009; Trautwein & Lüdtke, 2009), there is a need to investigate regulation of motivation in math homework. A study such as this would be important, as math is a main achievement domain with normally high demands for homework (e.g., students typically spend about one-fifth to two-fifths of their homework time on math; Kitsantas, Cheema, & Ware, 2011; Pezdek, Berry, & Renno, 2002).


Third, the previous research in the field did not incorporate several important variables that is likely to affect homework motivation management (e.g., expectancy belief, value belief, managing emotion, and cognitive reappraisal). Thus, it would be informative to incorporate these variables in the present investigation, informed by the expectancy-value theory (Eccles & Wigfield, 2002) and regulation of emotion (Gross & Thompson, 2007).


To address these gaps, the current investigation examined empirical models of motivation management for Chinese students in the context of math homework. Model 1 included 16 variables at the individual level. Consistent with related theoretical frameworks (Eccles, 1983; Pintrich, 2004; Schunk, 2005) and the previous results with U.S. students (Xu, 2014), it was hypothesized that motivation management was positively associated with homework interest, homework environment, and managing time. Furthermore, based on the expectancy-value theory (Eccles & Wigfield, 2003; Warton, 2001), it was hypothesized that motivation management was positively associated with expectancy belief and value belief, but negatively associated with several variables relating to task cost (i.e., opportunity cost resulting from time on TV, sports, and extracurricular activities). Moreover, in light of related of literature on emotion control (Kuhl, 1985) and cognitive reappraisal (Gross & Thompson, 2007), it was hypothesized that motivation management was positively associated with emotion management and cognitive reappraisal.


As for two variables regarding adult monitoring (teacher feedback and family help), it was hypothesized that motivation management was positively associated with family help, consistent with related theoretical propositions (e.g., the role modeling, scaffolding, and direct instruction on regulation of motivation; Pintrich, 2004; Wolters, 2011) and the previous finding with U.S. students (Xu, 2014). Meanwhile, given that motivation management was not associated with teacher feedback with U.S. students (Xu, 2014), no hypothesis was posed regarding these two variables.


With respect with background variables, it was hypothesized that females made more efforts in motivation management, in line with the self-regulation literature (Pokay & Blumanfeld, 1990) and the previous results with U.S. students (Xu, 2014). Meanwhile, although prior academic achievement may be positively associated with self-regulation strategies (Zimmerman & Martinez-Pons, 1990), homework motivation management was not related to academic achievement with U.S. students (Xu, 2014). Thus, it would be important to control this variable, along with parent education, in the present investigation.


Model 2 incorporated four additional variables at the class level, including parent education, grade level, homework interest, and teacher feedback. It would be important to include these variables at the class level, as regulation of homework motivation may be affected by the academic and social contexts surrounding homework completion, such as the norms, expectations, and social resources available in the classroom (e.g., modeling and scaffolding; Boekaerts, 2011; Corno & Mandinach, 2004; Wolters, 2011).


METHOD


PARTICIPANTS AND PROCEDURE


The participants in the present investigation consisted of 1,799 high school students in China, including 915 students in grade 10 (23 classes) and 884 students in grade 11 (23 classes). Particularly, among these students, 44.9% were male and 55.1% were female.


Nearly all students (97.3%) were assigned math homework at least five days a week. In addition, the participants stated spent about 67 minutes doing math homework daily (SD = 33). These information concerning the amount of and frequency of math homework is similar to relevant findings from recent homework studies in China (OECD, 2010).


Before administrating the survey, research assistants contacted math teachers and obtained standardized math test scores from them. The students were then assigned an identification number to ensure confidentiality and to link prior math achievement to the survey conducted several months later.


MEASURES


The participants were asked about their parents' educational level (father or guardian and mother or guardian), from elementary school (6 years) to graduate degree (19 years). The variable regarding parent education was then formed by averaging the educational levels for the parents. They also reported the extent to which they received family help, from never (scored 1) to routinely (scored 5). In addition, they reported the amount of time spent on homework, sports (e.g., basketball and band), extracurricular activities (e.g., math, science, and computer clubs), and TV.


Nine scales were applied in the current investigation. In Table 1, we have included sample items for these scales, along with the reliability information from the present investigation.


Table 1. Reliability Estimates of Multi-item Scales

Scales

Sample Items

 α (CI)

 Teacher feedbacka

How much of your math HW is checked by math teacher?

.69 (.67-.71)

How much of your math HW is graded by math teacher?

 HW interestb

I look forward to math HW

.94(.94-.95)

I enjoy math HW

 HW environmentc

Find a quiet area

.72 (.70-.74)

Make enough space for me to work

Managing timec

Set priority and plan ahead

.76(.74-.78)

Tell myself to work more quickly when I lag behind

Managing emotionc

Tell myself not to be bothered with previous mistakes

.82 (.81-.83)

Cheer myself up by telling myself that I can do it

Cognitive reappraisalc

I think that I can learn something from the situation

.82 (.81-.83)

I think that it's not all bad

Value beliefd

I don't learn much from our math homeworke

.83(.82-.84)

There is no point in my doing math HWe

Expectancy beliefd

I often feel completely lost in my math homeworke

.80(.79-.82)

If I don't understand something in math, I know where to look it up

Motivation managementc

Reassure myself that I am able to do math homework when it is hard

.82 (.80-.83)

Find ways to make math HW more interesting

Note. HW = homework.

a Rating: 1 = None, 2 = Some, 3 = About half, 4 = Most, 5 = All.

b Rating: 1 = Strongly disagree, 2 = Disagree, 3 = Neither disagree nor agree, 4 = Agree, 5 = Strongly agree.

c Rating: 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Routinely.

d Rating: 1 = Strongly disagree, 2 = Disagree, 3 = Agree, 4 = Strongly agree.

d Rating: The item was reverse scored.


Teacher Feedback. Based on related literature (Trautwein et al., 2006; Xu, 2008a), this scale consisted of three items to assess the extent to which math homework was monitored by math teachers (α = .69; the amount of math assignments being collected and checked).


Homework Interest. Five items assessed students' interest in math homework (α = .94), based on literature on interest (Denissen, Zarrett, & Eccles, 2007; Eccles & Wigfield, 2002) as well as homework interest (Cooper, Lindsay, Nye, & Greathouse, 1998; Xu, 2008a). The scale assessed the extent to which students enjoyed doing math homework.


Homework Environment. Five items measured students' efforts in selecting and structuring their homework environment (Xu, 2008b, 2008c; Xu, Fan, & Du, 2015). This scale was developed, based on the self-regulation literature (Wolters, 2003; Zimmerman & Martinez-Pons, 1990). It ranged from finding a conducive workspace to locating materials for math homework (α = .72).


Managing Time. Based on the literature relating to time management (Pintrich, Smith, Garcia, & McKeachie, 1993; Xu & Corno, 1998), this scale consisted of four items to assess students' initiatives to set priorities, plan ahead, and to monitor time available for math homework (α = .76; Xu, 2008b, 2010; Yang & Xu, 2015).


Emotion Management. This scale included three items (Xu, 2011; α = .82), ranging from up-regulating positive emotions (e.g., cheering myself up) to down-regulating unpleasant emotions (e.g., not worrying about prior errors or mistakes).


Cognitive Reappraisal. Based on the literature on cognitive reappraisal (e.g., I can control my emotions by changing the way I think about my situation; Gross, 2002; McRae, Ochsner, Mauss, Gabrieli, & Gross, 2008) and empirical studies on homework (e.g., Xu & Corno, 1998), this scale included three items (α = .89), with regard to reframing or rethinking of a negative stimulus in less emotional ways (e.g., "I think that it's not all bad").


Value Belief. This scale incorporates six items to assess students’ perceived value of math homework, adapted from the work by Trautwein et al. (2006). These items focused on utility and cost of doing math homework (α = .83).


Expectancy Belief. Adapted from the work by Trautwein et al. (2006), this scale incorporates 10 items to measure students’ expectancy belief regarding math homework (e.g., their confidence to complete math homework correctly; α = .80).


Monitoring Motivation. This scale measured students' efforts to sustain or enhance motivation in math homework, based on the literature on regulation of motivation (Kuhl, 1985; McCann & Garcia, 1999; Wolters, 2003) as well as videotaped homework observations (Xu & Corno, 1998). This scale included four items (α = .82), relating to interest enhancement (e.g., Wolters, 2003), self-consequating (Wolters, 2003; Xu & Corno, 1998), and efficacy self-talk (e.g., McCann & Garcia, 1999).


STATISTICAL MODELING ANALYSES


Due to the nested data (individuals nested within class), we performed multilevel modeling analysis to appropriately address several major issues associated with such data structure (e.g., underestimation of standard errors and aggregation bias). Multilevel modeling can include variables at different levels (e.g., at the individual and class level), by taking into consideration the nonindependence of observations (Raudenbush & Bryk, 2002).


To help interpret the obtained regression coefficients, all continuous variables were standardized prior to conducting the multilevel analyses (M = 0, SD = 1). Consequently, the regression weights for all variables (except the dummy-coded variables) were largely equivalent with the standardized weights derived from multiple regression analysis (Xu, 2008a).


Model 1 included 16 variables at the individual level (gender, prior math standardized score, parent education, family help, teacher feedback, homework interest, homework environment, managing time, managing emotion, cognitive reappraisal, expectancy belief, value belief, time on homework, sports, extracurricular activities, and TV). Model 2 included four variables at the class level (i.e., teacher feedback, parent education, grade level, and homework interest).


Because we had no a priori hypotheses about if or how the predictive power of the level-1 predictor variables would differ across the classes, all models implemented in the current investigation were random-intercept models (Raudenbush & Bryk, 2002). Teacher feedback, parent education, and homework interest were centered at the group mean. We used full maximum likelihood in the multilevel models. Missing values for the present investigation ranged from 0.06% to 3.34%, and they were imputed through the use of the expectation-maximization approach.


RESULTS


To obtain an initial impression of the data, Table 2 includes the descriptive statistics regarding all independent variables and the outcome variable of motivation management in math homework. In addition, it includes zero-order correlations among all of the study variables. Motivation management was significantly associated with all but three variables at the individual level (gender, parent education, and prior math achievement) and three variables at the class level (parent education, grade level, and teacher feedback).


Table 2. Descriptive Statistics and Correlations

 Variables

M

SD

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

 1 Gender (male: 1)

.45

.50

---

                   

 2 Prior math achievement

70.48

13.15

 .06†

---

                  

 3 Parent education

15.17

2.47

-.03

.02

---

                 

 4 Family HW help

2.02

.93

-.02

-.09†

.13†

---

                

 5 Teacher feedback

3.86

   .90

-.07†

-.05*

-.02

.03

---

               

 6 HW interest

2.87

.90

-.05*

.06*

.01

.13†

.09†

---

              

 7 HW environment

3.69

.86

-.09†

-.12†

-.01

.11†

.12†

.16†

---

             

 8 Managing time

3.03

.96

-.10†

-.03

.02

.15†

.09†

.19†

.36†

---

            

 9 Managing emotion

3.53

.88

-.01

.08†

.04

.07†

.12†

.35†

.27†

.35†

---

           

10 Cognitive reappraisal

3.29

1.05

-.01

.02

.03

.08†

.09†

.25†

.18†

.29†

.61†

---

          

11 Expectancy belief

2.89

.48

.06*

.16†

.09†

.05*

.09†

.42†

.08†

.16†

.44†

.34†

---

         

12 Value belief

3.16

.50

-.15†

.01

.01

-.03

.22†

.32†

.19†

.15†

.30†

.21†

.31†

---

        

13 Time on HW

67.08

32.57

.07†

-.02

.01

-.05*

-.04

-26†

.02

.04

-.08†

-.05

-.26†

-.17†

---

       

14 Time on sports

41.94

42.66

.22†

-.07†

-.02

.04

-.03

.05*

-.02

.02

.04

.07†

.07†

-.07†

.01

---

      

15 Time on extracurricular

41.43

46.23

.06†

-.02

.05*

.04

.00

.03

-.05*

.02

.05*

.10†

.03

-.09†

.00

.40†

---

     

16 Time on TV

21.29

39.86

.10†

.01

-.02

.06*

-.04

-.06*

-.14†

-.06*

-.06*

.01

-.05

-.17†

.04

.30†

.35†

---

    

17 Grade level (11th: 1)

.50

.51

.04

.04

-.05*

-.04

-.03

.04

.01

-.06*

-.03

-.02

.00

-.05*

.09†

-.01

-.03

.03

---

   

18 Parent education - class

15.16

.50

.01

.10†

.20†

-.04

-.10†

-.15†

-.05*

.05*

-.02

.01

-.07†

-.08†

.33†

.00

.02

.01

-.24†

---

  

19 Teacher feedback - class

3.86

.35

-.05

-.05*

-.05*

.02

.38†

.11†

.04

.01

.02

.00

.03

.14†

-.17†

-.03

-.01

-.03

-.07†

-.26†

---

 

20 HW interest - class

2.88

.29

-.03

-.03

-.10†

.08†

.13†

.31†

.15†

.06*

.18†

.11†

.21†

.26†

-.45†

-.02

.01

-.07†

.13†

-.49†

.34†

---

21 Motivation management

2.94

.94

.00

.03

.05

.11†

.05*

.36†

.22†

.41†

.53†

.44†

.34†

.15†

-.07†

.09†

.09†

-.06*

-.04

-.01

-.01

.15†

Note. HW = homework. N = 1,799. * p < .05. †p < .01.


The fully unconditional model revealed that, with 3.5% of the variance in motivation management located at the class level, thus the nested data has ICC of approximately .04. As is commonly argued in the literature, with ICC at this level, there is a need to conduct multilevel analysis to appropriately address relevant issues associated with such a nested data structure (Kreft & de Leeuw, 1998; Von Secker, 2002). For this model, the deviance statistics and the associated number of estimated parameters were 5078.465 and 3, respectively.


Model 1 included 16 variables at the individual level (gender, prior math achievement, parent education, teacher feedback, family homework help, homework interest, homework environment, managing time, managing emotion, cognitive reappraisal, expectancy belief, value belief, time on homework, sports, extracurricular activities, and TV). For this model, the deviance statistics and the associated number of estimated parameters were 4202.859 and 19, respectively. As hypothesized, Model 1 provided a statistically significantly better fit than the fully unconditional model (χ2(df = 16) = 875.606; p < .001). As shown in Table 3, Model 1 (i.e., with 16 individual-level variables) explained 37.4% of the variance in motivation management at the individual level as well as 90.5% of the variance in motivation management at the class level.


Table 3. Multilevel Results for Motivation Management in Math Homework



Model Predictor

Model 1

Model 2

b

SE

b

SE

Individual level

   
 

Gender (female: 0, male: 1)

.03

.04

.03

.04

 

Prior math achievement

-.01

.02

-.01

.02

 

Parent education

.01

.02

.01

.02

 

Family HW help

.02

.02

.02

.02

 

Teacher feedback

-.01

.02

-.01

.02

 

HW interest

 .14†

.03

 .15†

.03

 

HW environment

.02

.02

.01

.02

 

Managing time

 .22†

.03

 .22†

.03

 

Managing emotion

 .30†

.03

 .29†

.03

 

Cognitive reappraisal

 .14†

.02

 .14†

.02

 

Expectancy belief

 .08†

.03

 .08†

.03

 

Value belief

-.07†

.03

-.08†

.03

 

Time on HW

-.02

.02

.01

.02

 

Time on sports

 .04*

.02

 .04*

.02

 

Time on extracurricular

.04

.03

.04

.03

 

Time on TV

-.06*

.02

-.06*

.02

Class level

  
 

Grade level (10th: 0, 11th: 1)

  

-.06

.04

 

Parent education

  

.06

.11

 

Teacher feedback

  

-.11*

.04

 

HW interest

  

.31†

.08

R2 individual level

      .374

      .377

R2 class level

      .905

      .997

R2 total

      .393

      .399

Deviance statistics

  4202.859

  4186.193

Number of estimated parameters

   19

   23

Note. HW = homework. N = 1,799 (46 classes). b = unstandardized regression coefficient. SE = standard error of b. R2 = amount of explained variance.  *p < .05.  †p < .01.


Model 2 further incorporated four variables at the class level (i.e., grade level, teacher feedback, parent education, and homework interest). For this model, the deviance statistics and the associated number of estimated parameters were 4186.193 and 23, respectively. We employed the likelihood ratio test to compare Model 2 (i.e., with four class-level variables) to Model 1 (i.e., without four class-level variables). As hypothesized, the results revealed that, compared with Model1, Model 2 provided a statistically significantly better fit (χ2 (df=4) = 16.666, p < .01). Data further revealed that Model 2 explained an additional 9.2 % of the variance in motivation management at the class level. Overall, Model 2 accounted for 39.9% of the total variance in motivation management.


As s in Table 3, motivation management was positively with six variables at the individual level, including managing emotion (b = .29, p < .01), managing time (b = .22, p < .01), homework interest (b = .15, p < .01), cognitive reappraisal (b = .14, p < .01), expectancy belief (b = .08, p < .01), and time on sports (b = .04, p < .05). Meanwhile, motivation management in math homework was negatively related to value belief (b = -.08, p < .01) and time on TV (b = -.06, p < .05).


Finally, at the class level, motivation management in math homework was positively related to homework interest (b = .31, p < .01). Meanwhile, it was negatively related to teacher feedback (b = -.11, p < .01).


DISCUSSION


The current investigation tested empirical models of motivation management in math homework for Chinese students. At the class level, motivation management was positively associated with homework interest, but was negatively associated with teacher feedback. At the student level, motivation management was positively related to managing emotion, managing time, students' interest in homework, cognitive reappraisal, expectancy belief, and time on sports. Meanwhile, motivation management was negatively associated with value belief and time on TV.


The finding that managing time was positively related to motivation management in math homework is consistent with (a) the self-regulation literature that regulation of motivation is likely to be affected by students' efforts to regulate time use (e.g., Pintrich, 2004; Wolters, 2011), and (b) the previous finding with U.S. students (Xu, 2014). Similarly, the finding that homework interest was positively related to motivation management is consistent with (a) the self-regulation literature that those students who show interest in a task are more likely to use self-regulatory strategies (Pintrich & Zusho, 2002; Schunk, 2005), and (b) the previous finding with U.S. students (Xu, 2014). Furthermore, the current investigation extends previous homework research, by indicating that homework interest in a given class has a positive influence on motivation management above and beyond the positive influence of homework interest at the individual level. As homework interest at the class level was not associated with motivation management for US students (Xu, 2014), the present study implies that homework interest plays a more prominent role for Chinese students in particular.


How do we explain the prominence of homework interest on motivation management for Chinese students? One possible explanation is that there is a difference in cultural beliefs about learning in that, compared with US view of learning, Chinese view of learning evokes much passionate affect and desire (Li, 2003). In addition, given that Chinese students are assigned and spent more time on homework than U.S. students (Chen & Stevenson, 1989; Tam, 2009), it is not surprising that homework interest takes on added importance for Chinese students’ motivation management. This explanation is, to some degree, further substantiated by the finding from the present study that there was a negative zero-order correlation between time on homework and motivation management (see Table 2).


Meanwhile, what do we make out of the findings that motivation management was not associated with homework environment and family homework help? These findings are not in line with the theoretical propositions relating to the positive influence of the study environment (Pintrich, 2004) and adult monitoring (Wolters, 2011), as well as previous findings with U.S. students (Xu, 2014). One likely explanation is that the influence of family help on motivation management is mediated by homework interest and students' initiatives (e.g., managing time). This explanation is substantiated by previous findings that Chinese students (compared with U.S. counterparts) are more positive about homework and are more motivated in following through their assignments (Cai, 2003; Peng et al., 2010).

 

Regarding homework environment, Chinese parents place more emphasis on education and are more involved in their children's homework (Chen & Stevenson, 1989; Wang, 2004), and with homework environment in particular. As a result, there is less a need for Chinese students (compared with U.S. students) to take initiatives to arrange a conducive homework environment. Furthermore, due to "the family planning policy" (known as the one-child policy in the West), Chinese students do not have siblings or small crying babies at home during homework sessions. Taken together, it makes sense that the role of homework environment on motivation management is less pronounced for students in China than for their U.S. counterparts.


Meanwhile, what do we make out of the finding that gender was not associated with motivation management in math homework in the present investigation? This finding is not consistent with related literature favoring females (e.g., stronger self-discipline and effort management, and higher levels of persistence; Duckworth & Seligman, 2006; Honigsfeld & Dunn, 2003; Pokay & Blumanfeld, 1990) and the previous finding with U.S. students (Xu, 2014). Part of this may be due to the domain-general nature of the U.S. results and the domain-specific nature (i.e., math) of the China results. Another part of this may be due to the influence of culture on Chinese students’ approaches to learning, in the sense that Chinese cultural model of learning places a strong emphasis on effort (Rao et al., 2000; Salili et al., 2003) and on diligence, enduring hardship, steadfastness, and concentration (Li, 2001, 2002). Thus, the gender difference in motivation management may be less pronounced for Chinese students than for U.S. students.


Consistent with previous finding with U.S. students (Xu, 2014), teacher feedback at the individual level was not associated with motivation management. On the other hand, it is intriguing to note that teacher feedback at the class level was negatively associated with motivation management, whereas motivation management was not related to teacher feedback at the individual or class level in the previous study with U.S. students (Xu, 2014)? As Chinese teachers (compared with U.S. teachers) consider homework more important and assign more homework (Chen & Stevenson, 1989; Hong et al., 2011), one likely explanation is that Chinese students in a class with higher aggregated teacher feedback are more likely to feel external pressure from their teachers to do well in math homework. Such external pressure may adversely affect them (e.g., higher academic stress), leading them to take less initiatives to keep themselves motivated in the homework process. This explanation is, to some extent, supported by previous research that achievement pressure can negatively influence students' motivation in school math (Eccles & Midgley, 1989).


Our investigation represents a novel empirical contribution, by further examining the role of managing emotion and cognitive reappraisal on homework motivation management. Consistent with our hypotheses, as informed by related literature on emotion control (Kuhl, 1985) and cognitive reappraisal (Gross & Thompson, 2007), homework motivation management was positively related to emotion management and cognitive reappraisal. What is notable about these findings is that they are derived from a large sample of Chinese high school students while controlling for other variables in multilevel analyses.


The finding that expectancy belief was positively associated with motivation management is consistent with the expectancy-value theory (Eccles & Wigfield, 2002). Meanwhile, what do we make out of the finding that value expectancy was negatively associated with motivation management? As math is viewed as critically important in Chinese society in that (a) math learning is rooted in Confucian culture and embedded with being "Chineseness" (Mu, 2014) and (b) math is a compulsory element in the National College Entrance Examination (Sun & Wong, 2005), one likely explanation is that those Chinese students who rated higher in value belief in doing math homework may have less a need to keep themselves motivated in doing math homework.


Finally, it is interesting to state that motivation management was positively associated with time on sports, negatively related to time on TV, and unrelated to time on homework and other extracurricular activities. These findings suggest that students' involvement in other after-school activities may have differential effects on motivation management for Chinese students in the current investigation. It is not surprising that time on TV was negatively associated with motivation management, as students tend to admit that TV interferes with their studying and homework completion (e.g., Pool, van der Voort, Beentjes, & Koolstra, 2000). On the other hand, what do we make out of the finding that time on sports was positively associated with motivation management? As participating sports events is likely to take time away from doing homework, those students who spent more on sports may be more inclined to take initiatives to manage their motivation so that they could complete assignments on time. This explanation is, to some extent, supported by the literature on student athletes in United States which has shown that sports participations leads or forces students to manage their academic time efficiently (Holland & Andre, 1987; Larson, Hansen, & Monela, 2006; Parkerson, 2001). For example, in one study (Parkerson, 2001), students commented that sports had forced them to use their time well (e.g., “Once I got home from practice . . . I had to do my homework . . . and I knew that in order to play sports I had to keep my grades up.”).


Meanwhile, if that is the case, how do we make out of the finding that time on extracurricular activities was unrelated to motivation management? One likely explanation is that extracurricular activities (compared with sports) are categorically more similar to homework, in the sense that they have more overlap with their homework assignments (e.g., after-school math or computer club). This explanation is somewhat substantiated by another finding from the current investigation that time spent on homework was unrelated to motivation management.


LIMITATIONS AND IMPLICATIONS FOR RESEARCH


The current investigation has some limitations that need to be acknowledged when interpreting its findings. Although self-report data are commonly employed in motivation research (Fulmer & Frijters, 2009), with research on student motivation in particular (Pintrich, 2004; Wolters, 2011), it would be important to replicate these results in future research that incorporates other measures (e.g., behavioral measures or data reported by other sources).


Another limitation concerns the issue of causation, a challenge that typically faces many nonexperimental studies (Winship & Sobel, 2004). Informed by theoretical frameworks pertaining to regulation of motivation (e.g., Eccles & Wigfield, 2002; Pintrich, 2004; Pintrich & Zusho, 2002), the current investigation attempts to control for potential confounding variables. However, other potential variables may influence motivation management in math homework had they been incorporated.


With respect to research, it would be informative to carry out longitudinal studies to further investigate empirical models of homework motivation management in cross-cultural settings, particularly as some results from the current investigation implies that cultural differences may affect motivation management (e.g., gender, value belief, and teacher feedback). For example, given that value belief was negatively related to manage math homework motivation for Chinese students in the current investigation, it would be intriguing to examine the potential influence of value belief on motivation management for students in different countries. In addition, there is a need to investigate models of motivation management in other school subjects (e.g., physics or foreign language), so that we have a better idea what factors may be domain-specific.


There is also a need to carry out qualitative studies to understand better what keeps students motivated in the homework process. For example, what homework motivation management means to students from different cultural settings? How do they view the role of homework interest and value belief on homework motivation management? How do they adjust relevant strategies they use to regulate their homework motivation across different school subjects, if certain strategies are domain-specific? In addition, it would be informative to examine the complexities involved when good students or good teachers do to sustain, enhance, or modify homework motivation. Furthermore, there is a critical need to examine the causal hypotheses by experimentally manipulating some variables (e.g., time management and homework interest) and by investigating the effects of these influences on subsequent motivation management in homework across different school subjects. Finally, recent advancement in educational technology has enabled teachers to incorporate new media technology to promote student learning and motivation (e.g., web-based homework; Medicino, Razzaq, & Heffernan, 2009). At the same time, it has presented a new challenge of shielding academic goal striving (e.g., completing homework) from unwanted tech-related distractions (e.g., Calderwood, Ackerman, & Conklin, 2014; Xu, 2015), thereby presenting a new motivational obstacle for following through homework. Consequently, it would be intriguing to examine the role of new media technology on homework motivation management in future investigations.


IMPLICATIONS FOR PRACTICE


Although it seems commonsensical that teachers need to make homework more interesting for students, it is important to note that this recommendation is substantiated by the present finding in the context of Chinese students’ motivation management in math homework. In addition, this recommendation is line with the previous finding with U.S. students’ motivation management in homework in general (Xu, 2014). Taken together, these findings suggest that the positive influence of homework interest on motivation management may be applicable across different countries (China vs. United States) and across different school subject areas (math vs. homework in general).


Thus first, to help students to better address motivational obstacles and to keep themselves motivated in the homework process, it would be beneficial to pay more close attention to students' interests (e.g., activity interests and content interests; Corno & Xu, 2004) when designing and developing homework assignments. Second, as students in a given class are unlikely to find their homework assignments equally interesting, as managing homework motivation involves making homework assignments more interesting for themselves, it would be important for teachers to help students how to make homework more interesting and engaging in the homework process (e.g., through personalization and visualization).


Third, as students who successfully sustain or improve their motivation can serve as models for their classmates (Wolters, 2011), it would also be important to encourage students to share their strategies with peers about how to make homework assignments more engaging and interesting for themselves. Fourth, as "students’ lukewarm interest in homework is its relative lack of appeal compared to other after-school activities" (Xu, 2008a, p. 1198), it would be beneficial for teachers and parents to help students to identify and schedule their attractive after-school activities on a weekly basis, so that they are less likely to be distracted by competing activities during homework sessions, thereby viewing their homework relatively more appealing and interesting.


The finding regarding the importance of expectancy belief on motivation management in math homework suggests that, to keep students motivated in math homework, teachers and parents need to make more coordinated efforts to promote their expectancy belief. They may make math assignments more manageable for students (e.g., appropriate level of difficulty), by modeling challenging tasks in class prior to homework assignments, by providing individual support for math learning that is adjusted sensitively to his or her skill level, and by offering timely and ongoing opportunities to help build math skills. Furthermore, as managing homework motivation involves reassuring students that they are capable of completing homework assignments successfully, it would be important for teachers to help reinforce students' belief in their ability to solve math problems in class and at home, to learn to engage in positive self-talk, and to make appropriate self-attributions for successful homework completion.


Meanwhile, what can we take away from the present finding that there was no gender difference in motivation management with Chinese students, whereas there was a gender difference favoring girls in the previous study with U.S. students (Xu, 2014)? As discussed in the previous section, one possible explanation is that Chinese cultural model of learning places more emphasis on effort (e.g., Rao et al., 2000), with diligence, enduring hardship, steadfastness, and concentration in particular (e.g., Li, 2001). Thus, to address the gender gap with U.S. students, it would be beneficial to pay more attention to the role of effort in homework motivation management. This recommendation is further supported by our findings that student initiatives (i.e., managing time, managing emotion, and cognitive reappraisal) played a dominant role in maintaining homework motivation. Specifically, it would be important for educators and parents to encourage and support students to better manage homework time, by setting priorities, planning ahead, and monitoring time on homework. In addition, they may encourage students to up-regulate positive homework emotions (e.g., to cheer themselves up), to down-regulate unpleasant homework emotions (e.g., not to be upset with previous errors or mistakes), or to reframe a negative stimulus to change its emotional impact (e.g., to look at the bright side). Finally, it would be beneficial to encourage and support students to make a more deliberate effort to develop their own implicit models of managing homework motivation (i.e., what takes to keep themselves motivated while doing homework).


Acknowledgment


This research was supported by a grant from Peak Discipline Construction Project of Education at East China Normal University, China. The perspectives expressed here represent the authors’ views.


References


Boekaerts, M. (1997). Self-regulated learning: A new concept embraced by researchers, policy makers, educators, teachers, and students. Learning and Instruction, 7, 161–186.


Boekaerts, M. (2011). What have we learned about the social context-student engagement link? Teachers College Record, 113, 375–393.


Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology: An International Review, 54, 199–231.


Cai, J. (2003). Investigating parental roles in students' learning of mathematics from a cross-national perspective. Mathematics Education Research Journal, 15(2), 87–106.


Calderwood, C., Ackerman, P. L., & Conklin, E. M. (2014). What else do college students “do” while studying? An investigation of multitasking. Computers and Education, 75, 19–29.


Chen, C., & Stevenson, H. W. (1989). Homework: A cross-cultural examination. Child Development, 60, 551–561.


Chen, C., & Uttal, D. H. (1988). Cultural values, parents' beliefs, and children's achievement in the United States and China. Human Development, 31, 351–358.


Cooper, C. A., & Corpus, J. H. (2009). Learners’ developing knowledge of strategies for regulating motivation. Journal of Applied Developmental Psychology, 30, 525–536.


Cooper, H., Lindsay, J. J., Nye, B., & Greathouse, S. (1998). Relationships among attitudes about homework, amount of homework assigned and completed, and student achievement. Journal of Educational Psychology, 90, 70–83.


Cooper, H., Robinson, J. C., & Patall, E. A. (2006). Does homework improve academic achievement? A synthesis of research, 1987–2003. Review of Educational Research, 76, 1–62.


Corno, L. (1993). The best-laid plans: Modern conceptions of volition and educational research. Educational Researcher, 22(2), 14–22.


Corno, L. (2001). Volitional aspects of self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed., pp. 191–225). Mahwah, NJ: Erlbaum.


Corno, L., & Mandinach, E. B. (2004). What we have learned about student engagement in the past twenty years. In D. M. McInerney & S. V. Etten (Eds.), Big theories revisited: Vol 4. Research on sociocultural influences on motivation and learning (pp. 299–328). Greenwich, CT: Information Age.


Corno, L., & Xu, J. (2004). Doing homework as the job of childhood. Theory into Practice, 43, 227–233.


Denissen, J. J., Zarrett, N. R., & Eccles, J. S. (2007). I like to do it, I'm able, and I know I am: Longitudinal couplings between domain specific achievement, self-concept, and interest. Child Development, 78, 430–447.


Duckworth, A. L., & Seligman, M. E. (2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 98, 198–208.


Eccles, J. S. (1983). Expectancies, values and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–146). San Francisco: Freeman.


Eccles, J. S., & Midgley, C. (1989). Stage/environment fit: Developmentally appropriate classrooms for early adolescents. In R. Ames & C. Ames (Eds.), Research on motivation in education (Vol. 3, pp. 139–181). New York: Academic Press.


Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.


Fries, S., Dietz, F., & Schmid, S. (2008). Motivational interference in learning: The impact of leisure alternatives on subsequent self-regulation. Contemporary Educational Psychology, 33, 119–133.


Fulmer, S. M., & Frijters, J. C. (2009). A review of self-report and alternative approaches in the measurement of student motivation. Educational Psychology Review, 21, 219–246.


Gross, J. J. (2002). Emotion regulation: Affective, cognitive, and social consequence. Psychophysiology, 39, 281–291.


Gross, J. J., & Thompson, R. A. (2007). Emotion regulation: Conceptual foundations. In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 3–24). New York: Guilford Press.


Ho, D. Y. F. (1994). Cognitive socialization in Confucian heritage cultures. In P. Greenfield & R. Cocking (Eds.), The development of minority child: Culture in and out of context (pp. 285–313). Hillsdale, NJ: Erlbaum.


Holland, A., & Andre, T. (1987). Participation in extracurricular activities in secondary school: What is known, what needs to be known? Review of Educational Research, 57, 437–466.


Hong, E., Peng, Y., & Rowell, L. L. (2009). Homework self-regulation: Grade, gender, and achievement-level differences. Learning and Individual Differences, 19, 269–276.


Hong, E., Wan, M., & Peng, Y. (2011). Discrepancies between students' and teachers' perceptions of homework. Journal of Advanced Academics, 22, 280–308.


Honigsfeld, A., & Dunn, R. (2003). High school male and female learning-style similarities and differences in diverse nations. Journal of Educational Research, 96, 195–206.


Iflazoglu, A., & E. Hong, E. (2012). Relations of homework motivation and preferences to homework achievement and attitudes in Turkish students. Journal of Research in Childhood Education, 26, 57–72.


Kitsantas, A., Cheema, J., & Ware, H. W. (2011). Mathematics achievement: The role of homework and self-efficacy beliefs. Journal of Advanced Academics, 22, 310–339.


Kreft, I., & de Leeuw, J. (1998). Introducing multilevel modeling. London: Sage.


Kuhl, J. (1985). Volitional mediators of cognition-behavior consistency: Self-regulatory processes and action versus state orientation. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 101–128). New York, NY: Springer-Verlag.


Kuhl, J. (2000). The volitional basis of personality systems interaction theory: Applications in learning and treatment contexts. International Journal of Educational Research, 33, 665–704.


Larson, R. W., Hansen, D. M., & Moneta, G. (2006). Differing profiles of developmental experiences across types of organized youth activities. Developmental Psychology, 42, 849–863.


Li, J. (2001). Chinese conceptualization of learning. Ethos, 29, 111–137.


Li, J. (2002). A cultural model of learning: Chinese “heart and mind for wanting to learn.” Journal of Cross-Cultural Psychology, 33, 246–267.


Li, J. (2003). U.S. and Chinese cultural beliefs about learning. Journal of Educational Psychology, 95, 258–267.


Lin, J., & Chen, Q. (1995). Academic pressure and impact on students’ development in China. McGill Journal of Education, 30, 149–168


McCann, E., & Garcia, T. (1999). Maintaining motivation and regulating emotion: Measuring individual differences in academic volitional strategies. Learning and Individual Differences, 3, 250–279.


McRae, K., Ochsner, K. N., Mauss, I. B., Gabrieli, J. J. D., & Gross, J. J. (2008). Gender differences in emotion regulation: An fMAI study of cognitive reappraisal. Group Process and Intergroup Relations, 11, 143–162.


Mendicino, M., Razzaq, L., & Heffernan, N. T. (2009). A comparison of traditional homework to computer-supported homework. Journal of Research on Technology in Education, 41, 331–359.


Mu, G. M. (2014). Chinese Australians’ Chineseness and their mathematics achievement: The role of habitus. Australian Educational Researcher, 41, 1–18.


OECD. (2010). Mathematics teaching and learning strategies in PISA. Paris: OECD.


Parkerson, A. H. (2001). Young Women's Career and Educational Development through Extracurricular Activity Participation: A Qualitative Study. (ERIC Document Reproduction Service No. ED457409)


Patall, E. A., Cooper, H., & Wynn, S. R. (2010). The effectiveness and relative importance of choice in the classroom. Journal of Educational Psychology, 102, 896–915.


Peng, Y., Hong, E., Li, X., Wan, M., & Long, Y. (2010). Homework problems: Do students from rural and urban schools perceive diferently. International Journal of Learning, 17(3), 81–95.


Pezdek, K., Berry, T., & Renno, P. A. (2002). Children's mathematics achievement: The role of parents' perceptions and their involvement in homework. Journal of Educational Psychology, 94, 771–777.


Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385–407.


Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MLSQ). Educational and Psychological Measurement, 53, 801–813.


Pintrich, P. R., & Zusho, A. (2002). The development of academic self-regulation: The role of cognitive and motivational factors. In A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation (pp. 249–284). San Diego, CA: Academic.


Pokay, P., & Blumenfeld, P. C. (1990). Predicting achievement early and late in the semester: The role of motivation and use of learning strategies. Journal of Educational Psychology, 82, 41–50.


Pool, M. M., van der Voort, T. H. A., Beentjes, J. W. J., & Koolstra, C. M. (2000). Background television as an inhibitor of performance on easy and difficult homework assignments. Communication Research, 27, 293–326.


Rao, N., Moely, B. E., & Sachs, J. (2000). Motivational beliefs, study strategies, and mathematics attainment in high- and low-achieving Chinese secondary school students. Contemporary Educational Psychology, 25, 287–-316.


Raudenbush, S., & Bryk, A. S. (2002). Hierarchical linear models Applications and data analysis (2nd ed.). Thousand Oaks, CA: Sage.


Salili, F. Zhou, H., & Hoosain, R. (2003). Adolescent education in Hong Kong and Mainland China: Effects of culture and context of learning on Chinese adolescents. In F. Pajares & T. Urdan (Eds.), Adolescence and education, Vol. III: International perspectives on adolescence (pp. 277–302).

Greenwich, CT: Information Age.


Schunk, D. H. (2005). Self-regulated learning: The educational legacy of Paul R. Pintrich. Educational Psychologist, 40, 85–94.


Sellar, S., & Lingard, B. (2013). Looking East: Shanghai, PISA 2009 and the reconstitution of reference societies in the global education policy field. Comparative Education49, 464–485.


Siu, M. K. (1995). Mathematics education in ancient China: What lesson do we learn from it? Historia Scientiarum, 4, 223–232.


Sun, J., Dunne, M. P., & Hou, X. Y. (2012). Academic stress among adolescents in China. Australasian Epidemiologist19(1), 9–12.


Sun, X., & Wong, N. Y. (2005). A culture-bound look on mathemtics beliefs: Differences between China and U.S.A. students. Mathematics Educator, 8(2), 95–105.


Tam, V. C. W. (2009). Homework involvement among Hong Kong primary school students. Asia Pacific Journal of Education, 29, 213–227.


Trautwein, U., & Lüdtke, O. (2009). Predicting homework motivation and homework effort in six school subjects: The role of person and family characteristics, classroom factors, and school track. Learning and Instruction, 19, 243–258.


Trautwein, U., Ludtke, O., Schnyder, I., & Niggli, A. (2006). Predicting homework effort: Support for a domain-specific, multilevel homework model. Journal of Educational Psychology, 98, 438–456.


Verma, S., Sharma, D., & Larson. R. (2002). School stress in India: Effects on time and daily emotions. International Journal of Behavior Development, 26, 500–508.


Von Secker, C. (2002). Effects of inquiry-based teacher practices on science excellence and equity. Journal of Educational Research, 95, 151–160.


Wang, D. B. (2004). Family background factors and mathematics success: A comparason of Chinese students. International Journal of Educational Research, 41, 40–54.


Warton, P. M. (2001). The forgotten voices in homework: Views of students. Educational Psychologist, 36, 155–165.


Winne, P. H. (2001). Self-regulated learning viewed from models of information processing. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed., pp. 153–189). Mahwah, NJ: Lawrence Erlbaum Associates.


Winne, P. H., & Marx, R. (1989). A cognitive-processing analysis of motivation with classroom tasks. In C. Ames & R. Ames (Eds.), Research on motivation in education (Vol. 3, pp. 223–257). Orlando, FL: Academic.


Winship, C., & Sobel, M. E. (2004). Causal inferences in sociological studies. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 481–503). Thousand Oaks, CA: Sage.


Wolters, C. (2003). Regulation of motivation: Evaluating an underemphasized aspect of self-regulated learning. Educational Psychologist, 38, 189–204.


Wolters, C. (2011). Regulation of motivation: Contextual and social Aspects. Teachers College Record, 113, 265–283.


Xu, J. (2006). Gender and homework management reported by high school students. Educational Psychology, 26, 73–91.


Xu, J. (2008a). Models of secondary students’ interest in homework: A multilevel analysis. American Educational Research Journal, 45, 1180–1205.


Xu, J. (2008b). Validation of scores on the homework management scale for high school students. Educational and Psychological Measurement, 68, 304–324.


Xu, J. (2008c). Validation of scores on the homework management scale for middle school students. Elementary School Journal, 109, 82–95.


Xu, J. (2010). Predicting homework time management at the secondary school level: A multilevel analysis. Learning and Individual Differences, 20, 34–39.


Xu, J. (2011). Homework emotion management at the secondary school level: Antecedents and homework completion. Teachers College Record, 113, 529–560.


Xu, J. (2014). Regulation of motivation: Predicting homework motivation management at the secondary school level. Research Papers in Education, 29, 457–478.


Xu, J. (2015). Investigating factors that influence conventional distraction and tech-related distraction in math homework. Computers and Education, 81, 304–314


Xu, J., & Corno, L. (1998). Case studies of families doing third grade homework. Teachers College Record, 100, 402–436.


Xu, J., & Corno, L. (2003). Family help and homework management reported by middle school students. Elementary School Journal, 103, 503–518.


Xu, J., & Corno, L. (2006). Gender, family help, and homework management reported by rural middle school students. Journal of Research in Rural Education, 21(2). Retrieved from http://jrre.vmhost.psu.edu/wp-content/uploads/2014/02/21-2.pdf


Xu, J., Fan, X., & Du, J. (2015). Homework Management Scale: Confirming the factor structure with middle school students in China. Psychology in the Schools, 52, 419-429.


Xu, J., & Yuan, R. (2003). Doing homework: Listening to students’, parents’, and teachers’ voices in one urban middle school community. School Community Journal, 13(2), 25–44.


“Yang, F., & Xu, J. (2015). Examining the psychometric properties of the Homework Management Scale for high school students in China. Journal of Psychoeducational Assessment, 33, 268-277.”


Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 166–183.


Zimmerman, B. J., & Martinez-Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex, and giftedness to self-efficacy and strategy use. Journal of Educational Psychology, 82, 51–59








Cite This Article as: Teachers College Record Volume 118 Number 8, 2016, p. 1-26
https://www.tcrecord.org ID Number: 21367, Date Accessed: 3/1/2021 4:46:06 PM

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About the Author
  • Fuyi Yang
    East China Normal University
    E-mail Author
    FUYI YANG is an associate professor in the Department of Special Education at East China Normal University. His research interests focus on cognitive and social development of children with special needs, assessments for children with special needs, and early intervention. Recent publications include one article in Journal of Psychoeducational Assessment, titled “Examining the Psychometric Properties of the Homework Management Scale for High School Students in China” and one article in Research in Autism Spectrum Disorders, titled “The Roles of Cortisol and Pro-inflammatory Cytokines in Assisting the Diagnosis of Autism Spectrum Disorder.”
  • Jianzhong Xu
    Mississippi State University
    E-mail Author
    JIANZHONG XU is a professor in the Department of Counseling, Educational Psychology, and Foundations at Mississippi State University. His research interests focus on teaching and learning in the school and home setting, in home-school relationships, and in partnerships with culturally diverse families. Recent publications include two articles in American Educational Research Journal titled “Models of Secondary School Students’ Interest in Homework: A Multilevel Analysis” and “Promoting Student Interest in Science: The Perspectives of Exemplary African American Teachers" (with L. T. Coats and M. L. Davidson).
  • Heping Tan
    East China Normal University
    E-mail Author
    HEPING TAN is an associate professor in the Department of Special Education at East China Normal University. His research interests focus on cognitive development of children with special needs and psychological counseling for children with special needs. Recent publication includes one article in Neuroscience titled “The Developmental Disruptions of Serotonin Signaling May Be Involved in Autism During Early Brain Development” and one article in Journal of Schooling Studies, titled “An Investigation on the Learning Adaptability of Students with Hearing Impairments.”
  • Ningjian Liang
    East China Normal University
    E-mail Author
    NINGJIAN LIANG is a professor in the School of Psychology and Cognitive Science at East China Normal University. His research interests focus on cognitive psychology, social cognition and personality. Recent publications include one article in Psychological Science titled “The Nature of Implicit Self-esteem: Evidence from Extrinsic Affective Simon Task (EAST)” and one article in Studies of Psychology and Behavior titled “Using the Multifactor Traits Implicit Association Test (MFT-IAT) to Measure Multidimensional Implicit Self-concepts.”
 
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