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Abstract

Self-regulated learning (SRL) is essential for academic success. Research shows that culture influences the engagement in SRL processes. However, there are limited cross-cultural studies investigating SRL; therefore, we aimed to validate an SRL measurement for Chinese international and Canadian domestic undergraduate students and to examine if the relationships between SRL engagement and academic performance differ across the two groups. First, we investigated the measurement invariance of the Regulation of Learning Questionnaire for both groups. Our findings confirmed the metric invariance across these cultural groups and identified items contributing to the scalar non-invariance. Additionally, we found that goal management was significantly associated with Canadian students’ course grades only, at the middle and end of a 4-month academic semester. In contrast, monitoring was significantly associated with semester GPA for both groups at the middle of the semester. Our findings emphasize the significance of evaluating measurement invariance across cultures and contribute to the extant literature on the relationship between culture and SRL.

Keywords

culture self-regulated learning measurement measurement invariance academic performance

Introduction

Decades of research on self-regulated learning (SRL) have demonstrated its significance for students’ academic success (Hadwin & Winne, 2012; Theobald, 2021; Winne, 2015; Zimmerman, 2000, 2002). Self-regulated learners strategically and metacognitively plan, monitor, and adapt their learning processes to achieve their goals (Winne, 2015; Winne & Hadwin, 1998, 2008; Zimmerman, 2002), thereby promoting academic success. Consistent with their social cognitive roots, SRL models acknowledge the reciprocal role played by social context in SRL (Schunk & Zimmerman, 1997; Winne & Hadwin, 1998; Zimmerman, 1989). Furthermore, self-regulatory competence initially develops from social sources of academic skills and gradually transforms into individual sources through modeling (Schunk & Zimmerman, 1997). For example, the development of self-regulatory competency is shaped by social influences, including social models (e.g., teachers), verbal descriptions, and social guidance and feedback through observations and imitations (Schunk & Zimmerman, 1997). In other words, SRL is socio-historically situated in the experiences and interactions of learners as they grapple with academic work in a rich social milieu. Culture provides a platform for individuals to engage in cultural learning and acquire knowledge through social interactions (Greenfield et al., 2003). That is, the way individuals behave, think, and feel can be different across cultural contexts. Therefore, culture could play an essential role in shaping how individuals develop and gain self-regulatory competency. For example, research shows that Asian learners tend to emphasize more social-oriented SRL strategies, whereas Western learners tend to emphasize more individual-oriented SRL strategies (Shi et al., 2013).

Despite the potential influence of culture on learners’ development of self-regulatory competency (e.g., Greenfield et al., 2003), cross-cultural studies on SRL are rare in the extant literature. Indeed, predominant models of SRL used in cross-cultural studies have been based on Western theoretical frameworks (McInerney, 2011; McInerney & King, 2017). Therefore, considering the Western SRL models as universal across cultures in research may pose challenges in understanding cross-cultural similarities and differences in SRL. In fact, such challenges have been evidenced in the paradox of Chinese learners who have been historically portrayed as placing more emphasis on passive and rote learning (e.g., memorization and repetition; Pratt et al., 1999) but tended to perform well academically, particularly in the Western countries (Kember, 2000; Li, 2012; Marton et al., 1999). Therefore, it is critical to understand whether an SRL theoretical model that has been developed in the West can be applied to other cultures by examining the measurement equivalence of an SRL assessment used to examine various phases of SRL for distinct cultural groups. Specifically, the present study examined cultural differences in SRL, focusing on Chinese international and Canadian domestic students in a Canadian university by examining whether an SRL assessment can be equivalently used to examine the psychological construct of SRL for these two groups.

Literature Review

Self-regulation is essential for learners to develop lifelong learning skills to succeed in a wide range of settings, including education and employment (Sitzmann & Ely, 2011; Zimmerman, 2002). Specially, it is crucial for students to plan, monitor, evaluate, and adapt their learning processes to achieve their academic goals (Schunk & Zimmerman, 1997; Zimmerman & Schunk, 2001). Prior work has provided compelling evidence that these regulatory processes in learning help students build strong metacognitive knowledge (Pintrich, 2000), thereby allowing students to strategically decide how to manage their learning processes (Winne, 2015). Indeed, a recent meta-analysis reported that SRL training programs with a strong emphasis on metacognition improved university students’ academic performance, cognitive and metacognitive strategy use, and motivation (Theobald, 2021).

Several SRL models have been proposed with a consolidated theoretical background and empirical support (see Panadero, 2017 for a review). All models consider SRL as a cyclical process comprising various phases and subprocesses (e.g., planning, monitoring, and adapting; Panadero, 2017) and recognize learners as actively and constructively engaging in their learning process and being able to monitor, evaluate, and regulate many aspects of their own cognition, behavior, motivation, and emotion (Pintrich, 2000). Particularly, Winne and Hadwin’s (1998) model describes four loosely sequenced and recursively linked phases. In the first phase, learners generate a task understanding about features of academic tasks by evaluating personal and task characteristics (e.g., self-efficacy and domain knowledge). In the second phase, goal setting and planning, learners set goals for the task and construct plans to accomplish the established goals. Particularly, goals serve as standards for learners to monitor their regulation of learning (Winne, 2015). In the third phase, strategy enactment is where learners engage in tactics and strategies to achieve goals created in previous phases. In the fourth phase, adaptation, learners make purposeful changes to approach their future learning (Winne & Hadwin, 1998, 2008). As SRL is a recursive cycle, learners may revisit phases in any order (Winne, 2015).

Moreover, many SRL models acknowledge the reciprocal role that social context plays (Schunk & Zimmerman, 1997; Winne & Hadwin, 1998). For example, this social influence within SRL is apparent in Zimmerman’s (1989) cyclical phases model, in which learners evaluate their performance by comparing it with that of others. As Martin (2007) noted, it is critical to understand how people express themselves, engage in strategic planning, and pursue various goals through their relationships with others. Therefore, through social modeling, guidance, and interactions, the development of SRL can be fostered. As an essential component of learning and socialization, culture is defined as “an advanced form of social life based on shared understandings and the use of meaning for processing information collectively” (Baumeister, 2011, p. 6; Greenfield et al., 2003). Repeated and continuous engagement in specific cultural contexts or practices often leads to automatized patterns of psychological responses (Markus & Kitayama, 2010). Thus, culture significantly and unobtrusively influences every aspect of people’s lives. Like other scholars (Helmke & Tuyet, 1999; Li et al., 2018; McInerney & King, 2017; Purdie & Hattie, 1996; Shi et al., 2013), we acknowledge that the socially situated nature of SRL implies that culture can influence its development and foster different perspectives about SRL across distinct cultures. This notion then leads to the question of how learners with culturally different learning experiences engage in distinct aspects of SRL.

Extant studies often use mono-cultural research samples, despite the rapid growth of SRL research in educational psychology (McInerney & King, 2017). One meta-analysis study found that primary and secondary Chinese students in China tend to emphasize the importance of self-efficacy, strategy enactment, and self-evaluation during learning (Li et al., 2018). Unfortunately, only a limited number of cross-cultural SRL studies have been conducted (e.g., Lopez et al., 2013; Shi et al., 2013; Tong et al., 2019; Zhu et al., 2008). These few available studies found that students from Asian and Western contexts exhibit significant differences in their SRL. A cross-cultural study examined the learning conceptions and approaches to learning used by first-year Flemish and Chinese university students in Flanders and China (Zhu et al., 2008). Their results revealed that Chinese students’ conceptions of learning emphasized understanding, personal change, and the development of social competence more so than those of their Flemish counterparts.

In addition to studies on the effects of students’ previous sociocultural learning experiences on SRL, Shi et al. (2013) examined how Chinese international and Canadian domestic students engaged in SRL actions within a multicultural collaborative learning context at a Canadian university. They found that, compared with Chinese international students, Canadian domestic students demonstrated more individually oriented SRL actions (e.g., emphasizing individualistic actions and maintaining one’s own goals) relative to more socially oriented actions (e.g., emphasizing others’ interest and benefits and meeting the needs of others or the group). Lastly, a cross-cultural study investigating the relationships between perseverance, SRL, motivation, and achievement for 15-year-old students from Hong Kong, the Republic of Korea, Australia, New Zealand, Scotland, and the US found that SRL strategy use served as a mediator for perseverance predicting students’ achievement for all countries (Xu et al., 2021). Specifically, Xu et al. (2021) found that control strategy as an SRL strategy had stronger impacts on achievement for students from Anglo-Saxon countries than those from East Asian countries. Also, memorization had a negative effect on achievement, but this effect was weaker in East Asian countries than in Western countries (Xu et al., 2021). Overall, the studies presented thus far provide evidence that culture influences how students engage in SRL.

Although prior research has revealed cross-cultural differences in SRL, scholars have failed to recognize that culturally specific learning approaches are often not stressed in SRL models. According to McInerney and King (2017), most cross-cultural studies utilize SRL theoretical models and measurements that were developed and validated in the West. Further, utilizing the WEIRD (i.e., Western, Educated, Industrial, Rich, and Democratic) population, which represents only 12% of the world’s population, is still common in psychological research (Henrich et al., 2010). Thus, a Western SRL theoretical framework may not fully capture the unique ways in which non-WEIRD learners engage in SRL, and its corresponding measures may fail to reflect observable SRL behaviors in non-Western cultural contexts. Subsequently, the observed cross-cultural differences in the extant SRL literature could be explained by a lack of theoretical precision and operational explicitness of the related constructs. Findings from studies that adopt a Western theoretical framework and use an underrepresented population cannot be generalized to other non-WEIRD populations. Without testing the invariance of the measurement used across groups, studies that use the same measurement to examine group differences lack measurement comparability, making their conclusions or findings ambiguous and problematic (Boer et al., 2018). Indeed, Tong and Colleagues (2019) investigated the psychometric properties of the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., 1993) in a Chinese cultural context. MSLQ is one of the most widely used SRL questionnaires for assessing the process of self-regulation, developed by Pintrich et al. (1993). Of the original 81-item MSLQ, only 52 items were retained for Chinese undergraduate students, with modifications to some SRL constructs, including reasons for engagement in academic tasks and beliefs about accomplishing them (Tong et al., 2019). This study provided evidence that cross-cultural adaptation of a well-established SRL instrument was necessary to apply SRL models developed in Western cultural contexts to Eastern cultural contexts. Therefore, it is critical to evaluate whether an SRL measurement developed and validated in the West could be equivalently applied to measure SRL among non-Western populations.

The present study adopted the Regulation of Learning Questionnaire (RLQ; Hadwin, 2009; McCardle & Hadwin, 2015), which was based on Winne and Hadwin’s (1998) SRL model and designed to capture dynamic SRL processes as they unfold over time in various contexts. Specifically, the RLQ prompts learners to respond to items about each phase of SRL in relation to a recent and specific study event. Learners report contextual details about the course, its tasks, and the challenges that they encountered. To advance current understandings of cross-cultural differences using an existing SRL measurement, it is important to first establish the compatibility of the adopted measure by examining the measurement invariance of the RLQ. Evaluating the cultural invariance of an SRL measure is crucial for developing data-driven understandings of Chinese international students’ SRL engagement, shaped by their previous learning experiences in China. This approach, in turn, can inform culturally responsive approaches to support their academic success while studying in Canada.

Given that SRL is a dynamic process unfolding over time across contexts, learners may exhibit different patterns of engagement as they navigate various learning tasks. The present study examines the cultural invariance of the RLQ at three separate time points, which are the beginning, middle, and end of a 4-month academic semester. Considering that different phases of the semester demand various levels of cognitive, behavioral, and emotional engagement with learning, university students may demonstrate distinct patterns of SRL engagement, such as developing adequate task understanding of academic assignments, choosing effective learning strategies, and evaluating their adaptive or maladaptive learning patterns. Adopting longitudinal data to examine the measurement invariance at different time points of the semester can provide evidence about the validity of the RLQ at different time points as Chinese international and Canadian domestic students engage in SRL processes. As a result, this study aimed to (a) evaluate the measurement invariance of SRL constructs from the RLQ (McCardle & Hadwin, 2015) between Chinese and Canadian students across three time points over one university semester and (b) examine the relationship between participating students’ self-reported engagement in SRL processes and their academic performance.

Methods

Participants

The study participants included undergraduate students enrolled in a face-to-face undergraduate educational psychology course on learning strategies and SRL at a Canadian public university (N = 586) during both the 2013–2014 and 2014–2015 academic years (Fall 2013 n = 166, Spring 2014 n = 108, Fall 2014 n = 162, and Spring 2015 n = 150). Their ages ranged from 17 to 44 years (M = 19.12, SD = 2.13). Of the 562 participants reporting their year of education, 68.5% were in their first year of and 22.8% were in their second year of university, and 56.9% were female. A subset of Chinese international students (n = 151; 95 female, 52 male, and 3 unidentified) included participants who held Chinese citizenship and study permits and self-reported that English was not their first language. Of the 143 Chinese international students reporting their year of education, 71.3% were in their first year and 20.3% were in their second year of the university. Canadian domestic students (n = 435; 239 female, 193 males, and 4 unidentified) included those who held Canadian citizenship, self-reported that English was their first language, and had attended a Canadian high school. Of the 419 Canadian domestic students reporting their year of education, 67.5% were in their first year and 23.6% were in their second year of university.

Procedure

The university’s Human Research Ethics Board approved this study’s procedures. Participants who were enrolled in this course were automatically included in this research and provided with a consent information letter at the beginning of the semester. They were given multiple opportunities to withdraw from participating over the semester. Prior to the final analysis, data were removed for students who declined to participate prior to the final analysis. Participants completed the RLQ online as a class activity in week 2 (time 1), week 6 (time 2), and week 11 (time 3) of the semester. Of the participants, 91% completed the RLQ at three time points, 7% completed it twice, and about 2% completed it once. Completion of the RLQ was required for a participation mark; however, responses were not graded. Feedback was provided to students in the form of a profile of a-priori subscale scores each time they completed the survey.

Measures

Self-Regulated Learning

The RLQ (Hadwin et al., 2022; McCardle & Hadwin, 2015) assessed participants’ self-reported actions and strategies specific to key SRL processes. The RLQ comprised three sections. In the first section, students were instructed to think about a recent challenge that they had faced during their academic learning. They were asked to (a) provide the name of the course for which they had been studying, (b) describe the task, and (c) explain the challenge they encountered in an open-ended text field. In the second section, students rated their intentions to engage in specific SRL processes during their study session and respond to five items (i.e., thoroughly understand the task, set good quality goals, monitor my progress, evaluate my progress, and adapt my learning) on a 4-point Likert scale (1 = not at all, 2 = not really, 3 = sort of, and 4 = yes).

In the third section, students were instructed to “Think about the study session when you encountered a challenge you had faced, specifically in your academic learning. Rate the extent to which you actually did each of the following from 1 (not at all) to 4 (yes). Before I got started, I….”. Four subscales of the RLQ reflecting the essential phases of SRL processes were adopted, including Task Analysis (3 items), Goal Setting & Management (7 items), Monitoring (3 items), and Adaptation (6 items). Students responded to the items on a 4-point Likert scale (1 = not at all, 2 = not really, 3 = sort of, and 4 = yes). Higher scores indicate a higher self-reported engagement in the respective phase of SRL processes.

Academic Performance

Students’ final course grades and semester registry GPA were obtained as measures of their academic performance. Specifically, GPA is reported on a 9-point scale, where 0 = F (0–48%), 1 = D (50–59%), 2 = C (60–64%), 3 = C+(65–69%), 4 = B-(70–72%), 5 = B (73–76%), 6 = B+(77–79%), 7 = A-(80–84%), 8 = A (85–89%), and 9 = A+(90–100%). Because this undergraduate educational psychology course teaches students about SRL, final course grades were considered to reflect a student’s own self-regulatory knowledge and skills, whereas the GPA reflected their university performance.

Statistical Analyses

This study conducted a Multiple-Group Confirmatory Factor Analysis (MG-CFA) using AMOS version 30. Prior to the main analysis, we examined measurement invariance across three time points separately for the Canadian and Chinese samples. Then, we examined a four-factor measurement model for the RLQ represented by task analysis, goal setting and management, monitoring, and adaptation. MG-CFAs were conducted to assess the measurement invariance between the two groups across time. Three hierarchical steps (i.e., configural, metric, and scalar invariance) were adopted in testing measurement invariance (Byrne, 2016; Vandenberg & Lance, 2000). Due to the commonly recognized restrictiveness of the chi-square statistic, as well as its sensitivity to sample size, four additional model fit indices were consulted (Bentler, 1990; Hu & Bentler, 1999; Ponterotto et al., 2003; Steiger, 1990), including: (a) comparative fit index (CFI) values ≥.90, (b) the standardized root mean square residual (SRMR) ≤ .08, (c) 90% confidence interval (CI) accompanying the root mean square error of approximation (RMSEA) ≤ .06, and (d) the Tucker–Lewis index (TLI) ≥ .90. The full information maximum likelihood estimation procedure was used to manage missing data.

The first step involved establishing configural invariance. Determination of invariance (equivalence of model form) at this step was based on the indices of goodness-of-fit to the data. Next, the metric invariance model, with factor loadings constrained to be equal across the two groups, was evaluated against the configural model. Following this, scalar invariance was examined by imposing equality constraints on item intercepts across groups. If the full scalar invariance could not be established, the partial scalar invariance would be examined by freely estimating the intercept of the variant items (Byrne et al., 1989). There is a limited discussion of the specific criterion regarding the minimal number of invariant items required to establish partial scalar invariance, and standards of establishing partial measurement invariance vary in the current literature (Byrne et al., 1989; Cheung & Lau, 2012; Putnick & Bornstein, 2016; Vandenberg & Lance, 2000). According to other researchers (see Byrne et al., 1989), a minimum of two items, including the reference indicator, must be invariant before researchers can make meaningful comparisons across groups. Considering that limited studies explored the extent to which the SRL constructs, and their measurement were theoretically and operationally equivalent across cultural groups, this study was rather exploratory in nature. Therefore, investigating RLQ items that failed the measurement invariance was more important than adopting a strict guideline to establish the partial scalar invariance. Such an explorative approach allowed us to investigate cultural differences of the RLQ explicitly at the item level and locate sources of non-invariant items (Byrne et al., 1989; Putnick & Bornstein, 2016). Using changes in CFI and RMSEA values is more appropriate to obtain evidence of non-invariance than chi-square (χ²) difference in value (Leong et al., 2018). Therefore, we examined the difference in CFI (ΔCFI ≤.010) and RMSEA values (ΔRMSEA ≤.015) to determine whether the decrease in fit between the latter and previous models was substantial (Cheung & Rensvold, 2002).

Results

Measurement Invariance

Table 1 provides descriptive statistics of the variables examined in this study, including the mean and standard deviation for each group. As a preliminary analysis, we conducted longitudinal CFA to examine measurement invariance across three time points for Chinese international and Canadian domestic students. Results showed that while configural and metric invariance were established for the Canadian sample, scalar invariance was not supported. In contrast, configural invariance was not established for the Chinese sample, suggesting that SRL constructs were interpreted differently at each time point across the semester. These findings provide evidence that SRL is a dynamic process that unfolds in different contexts and over time. Thus, we proceeded with examining measurement invariance between Chinese international and Canadian domestic university students separately at each time point.

Table 1.

Descriptives Statistics of the Subscales of the RLQ, Course Grade, and Semester GPA

	All			Canadian domestic students			Chinese international students
Variable	N	M	SD	n	M	SD	n	M	SD
Time 1
Task analysis	573	2.87	.70	424	2.78	.71	149	3.14	.59
Goal setting and management	573	2.98	.57	424	2.90	.56	149	3.23	.52
Monitoring	573	3.35	.59	424	3.30	.62	149	3.51	.48
Adaptation	573	2.75	.61	424	2.67	.61	149	2.96	.56
Time 2
Task analysis	558	2.94	.71	413	2.90	.74	145	3.06	.58
Goal setting and management	558	3.07	.58	413	3.03	.60	145	3.20	.51
Monitoring	558	3.38	.56	413	3.39	.60	145	3.37	.53
Adaptation	558	2.78	.60	413	2.70	.59	145	3.03	.55
Time 3
Task analysis	564	2.93	.69	418	2.87	.71	146	3.14	.62
Goal setting and management	564	3.09	.57	418	3.01	.59	146	3.30	.46
Monitoring	564	3.43	.55	418	3.40	.57	146	3.52	.48
Adaptation	564	2.80	.63	418	2.72	.64	146	3.03	.54
Course grade	586	5.32	2.43	435	5.59	2.44	151	4.52	2.21
GPA	586	4.64	1.98	435	4.70	2.00	151	4.45	1.90

Note. GPA = grade point average

Then, we conducted MG-CFAs to examine measurement invariance for Chinese international and Canadian domestic student groups. The configural invariance models demonstrated acceptable fit to the data at all three time points (χ² = 486.101, df = 292, p < .001, CFI = .91, TLI = .90, RMSEA = .034 [.029, .039], SRMR = .05 at time 1; χ² = 466.023, df = 292, p < .001, CFI = .93, TLI = .91, RMSEA = .033 [.027, .038], SRMR = .04 at time 2; χ² = 498.701, df = 292, p < .001, CFI = .92, TLI = .90, RMSEA = .035 [.030, .041], SRMR = .05 at time 3). This finding indicates that the factor structure of the RLQ (i.e., task analysis, goal setting and management, monitoring, and adaptation) was supported in both groups.

Next, the metric invariance model with factor loadings constrained to be equal across the two groups was evaluated. Changes in CFI and RMSEA were minimal (ΔCFI = .006, .003, and .004; ΔRMSEA = .000, 000, and .000 at time 1, time 2, and time 3, respectively). These results suggest that the factor loadings of the four-factor RLQ model were invariant between Chinese international students and Canadian students, supporting metric invariance.

Because metric invariance was established, the final step was to evaluate scalar invariance by constraining all item intercepts to be equal across both groups. Scalar invariance was supported at time 1 but not at time 2 nor time 3 (see Table 2). This finding indicated that at least one item intercept of the RLQ differed across both groups at time 2 and time 3 (Putnick & Bornstein, 2016). Using an explorative approach, we further investigated cultural differences in the RLQ at the item level and identified sources of non-invariant items (Byrne et al., 1989; Putnick & Bornstein, 2016).

Table 2.

Tests for Invariance of the Four-Factor Measurement Model of the RLQ: Goodness-of-Fit Statistics

Model	χ²	df	CFI	TLI	SRMR	RMSEA	90% CI	ΔCFI	ΔRMSEA
Time 1
Configural	486.101^***	292	.909	.894	.051	.034	.029, .039	—	—
Metric	514.132^***	307	.903	.892	.051	.034	.029, .040	.006	.000
Scalar	543.242^***	322	.897	.890	.051	.035	.030, .040	.006	.001
Time 2
Configural	466.394^***	292	.927	.914	.043	.033	.027, .038	—	—
Metric	487.556^***	307	.924	.915	.044	.033	.027, .038	.003	.000
Scalar	541.472^***	322	.908	.902	.044	.035	.030, .040	.016	.002
Partial scalar	500.507^***	317	.923	.917	.044	.032	.027, .038	.001	.001
Time 3
Configural	500.154^***	292	.917	.903	.050	.036	.030, .041	—	—
Metric	524.074^***	307	.913	.903	.051	.035	.030, .041	.004	.000
Scalar	602.298^***	322	.888	.881	.051	.039	.034, .044	.025	.004
Partial scalar	551.602^***	319	.907	.900	.051	.036	.031, .041	.006	.001

Note. CFI = comparative fit index, TLI = Tucker-Lewis index, SRMR = standardized root mean square residual, RMSEA = root mean square error of analysis, 90% CI = 90% confidence interval.

***p < .001.

To identify the sources of non-invariance, the intercept of each item was constrained to be equal across both groups, one at a time, while the remaining items were freely estimated. High ΔCFIs were indicative of non-invariant items and were, thus, considered to be varied (Feitosa et al., 2017). Among the 19 RLQ items, we identified five intercepts at time 2 and three intercepts at time 3 that differed between the groups (see Table 3). Allowing these item intercepts to be freely estimated supported partial scalar invariance (see Table 2). Additionally, we located 11 items that were invariant for both groups across the three time points, including one TA item, six GM items, two MON items, and three AD items (see Table 3). Only GM7 (i.e., evaluated my progress toward my goal) was non-invariant at time 2 and time 3 between the Chinese and Canadian samples.

Table 3.

Non-Invariant Item Intercepts Identified for the Four-Factor Measurement Model of the RLQ at Time 2 and Time 3

Scale	Label	Item	Time 2	Time 3
Task analysis (TA)	TA1	Thought about why I am being asked to know this stuff
	TA2	Reflected on why this task is important	X
	TA3	Made a judgment about the usefulness or value of the content	X
Goal setting and management (GM)	GM1	Set goals for my work
	GM2	Identified specific content, ideas, or terms in my goals
	GM3	Decided on goals that focused on learning, understanding, or remembering
	GM4	Set goals that would be useful for checking on my own progress
	GM5	Considered whether my goals were appropriate for the task
	GM6	Assessed my goal attainment
	GM7	Evaluated my progress toward my goal	X	X
Monitoring (MON)	MON1	Asked myself if I was understanding the task
	MON2	Asked myself if I was remembering		X
	MON3	Asked myself if I was understanding the material
Adaptation (AD)	AD1	Changed my understanding of what the task was
	AD2	Modified my plans for the task	X
	AD3	Switched to a different strategy or approach
	AD4	Changed my feelings about the task		X
	AD5	Altered the level of effort I was putting in	X
	AD6	Modified my beliefs about how well I would do on this task

Note. X refers to non-invariant items across Chinese international and Canadian domestic students.

Relationships Between SRL and Academic Performance

With established metric invariance, correlations of latent factors can be compared across cultures (Boer et al., 2018; Cheung & Lau, 2012). Therefore, we examined how engagement in SRL was related to academic performance for Canadians and Chinese at each time point. Specifically, correlations between the latent constructs of SRL and academic performance (i.e., final course grade and semester GPA) were computed (see Table 4). For Canadian students, goal setting and management at time 2 and time 3 was associated with course grade (r = .11 and r = .12, p < .05, respectively) and significantly correlated with semester GPA (r = .14 and r = .16, p < .01, respectively); monitoring at time 2 was also significantly associated with semester GPA (r = .12, p < .05). However, for Chinese students, only monitoring at time 2 was associated with their semester GPA (r = .19, p < .05).

Table 4.

Correlations between SRL Processes and Academic Performance for Canadian Domestic and Chinese International Students


Variable	Canadian domestic student		Chinese international student
Variable	Course grade	Semester GPA	Course grade	Semester GPA
Time 1
Task analysis	.01	.03	−.16	−.11
Goal setting and management	−.04	−.01	−.02	.08
Monitoring	−.08	−.07	−.04	.02
Adaptation	−.07	.02	−.08	.02
Time 2
Task analysis	−.01	−.00	.03	.09
Goal setting and management	.11 ^*	.14 ^**	.05	.08
Monitoring	.06	.12 ^*	.08	.19 ^*
Adaptation	.01	−.01	−.08	−.03
Time 3
Task analysis	.08	.09	.04	−.02
Goal setting and management	.12^*	.16 ^**	−.08	.03
Monitoring	.03	.06	.15	.15
Adaptation	.08	.01	−.14	−.14

Note. ^*p < .05, ^**p < .01. The significance was indicated in bold.

Discussion

This study evaluated the measurement invariance of the RLQ, assessing the four phases of SRL, between Chinese international students and Canadian domestic students across three time points. Our findings support the configural invariance across Chinese and Canadian students at each time point, indicating that the same factor structure was present in the two groups. Further, the established metric invariance across the Chinese and Canadian students at the three time points may indicate that the psychological meanings of task analysis, goal management, monitoring, and adaptation are all attributed equivalently. However, the present study did not establish the scalar invariance between the Chinese and Canadian samples at the middle and end of the semester (time 2 and time 3). This finding supports Tong et al. (2019), who emphasized the need to translate, adapt, and validate an SRL questionnaire developed in Western contexts to ensure they accurately assess SRL for learners with different cultural learning backgrounds. The present study is exploratory in nature, as few studies have examined the extent to which the SRL construct is operationally equivalent across cultural groups.

Although our findings indicate that scalar invariance was not supported at time 2 and time 3, almost half of the RLQ items were identified as invariant across the three time points. This suggests that the intercepts of SRL constructs, such as goal setting and management, monitoring, and adaptation remain consistent across these two groups at each time point. Although scalar invariance was not fully supported, the identification of invariant RLQ items suggests that Chinese international students and Canadian domestic students may engage in certain aspects of SRL processes in similar ways, such as setting and managing academic goals, monitoring learning progress, and adapting to learning situations. That is, both Chinese international and Canadian university students emphasize SRL as an effortful, goal-oriented, adaptive, and metacognitive process. Because Chinese international and Canadian domestic students studied in the same learning environment, a Western university, and received instruction on SRL in the same undergraduate educational psychological course, their engagement in SRL could be similar. This interpretation warrants further research to investigate whether distinct sociocultural learning environments shape students’ engagement in SRL.

A closer examination of the scalar non-invariant item related to goal setting and management reveals that it tends to focus on goal evaluation (i.e., “evaluated my progress toward my goal”). There are two possible explanations for this lack of invariance across two groups. First, Chinese learners may not set their learning goals explicitly or define them in a measurable format because they emphasize virtues (e.g., sincerity, diligence, and endurance of hardship) in learning as a goal (Li, 2012). For instance, in prior research examining the predictive power of self-efficacy, task values, and growth mindset on Chinese primary school students’ SRL in English writings, task values (interest and utility) were shown to be a weaker predictor of SRL use (Bai et al., 2021). Therefore, displaying virtues, rather than focusing on the utility of learning tasks, may be a highly motivating goal in learning for Chinese students, which influenced how Chinese students evaluated their goals. Second, for Chinese students, learning does not merely represent an academic pursuit but can also serve as moral and social goals that then enable them to achieve self-perfection (Li, 2003, 2012). For instance, in Bempechat et al.’s (2018) study, families’ educational messaging played a critical role in fostering Chinese adolescents’ virtue-oriented learning beliefs and their self-regulated behaviors. Because moral and social objectives are emphasized for Chinese learners, they may reply on social resources, such as peers, teachers, and parents, to evaluate their learning.

Another non-invariant item worth mentioning is one from adaptation (i.e., “altered the level of effort I was putting in”). This SRL item reflects an essential learning component related to effort and perseverance, which are emphasized in Chinese learning philosophy, particularly Confucianism. To illustrate Confucianism in detail, Confucius’s notion of learning addresses effortful regulations to develop learning virtues to perfect oneself morally, intellectually, and mentally. Regardless of how much a person achieves, one should continuously make efforts because learning never ends (Li, 2003). Additionally, Confucius acknowledged that learning requires a sense of integrity and a sense of shame by recognizing one’s wrongdoings or challenges and simultaneously amending oneself (Li, 2003). Because scalar invariance was not established at time 2 and time 3, we postulate that these SRL constructs may have been influenced by students’ sociocultural learning backgrounds, which, in turn, affected how they interpreted RLQ items. That is, students with different sociocultural experiences tend to express differences in their SRL processes at the operational level, which could explain the lack of fit of the RLQ for Chinese international students. To our knowledge, no existing empirical study has yet explored the potential reasons why an SRL instrument might exhibit scalar non-invariance across cultural groups. While our findings suggest that sociocultural learning backgrounds may influence how students interpret RLQ items, further research is needed to empirically test the specific mechanisms underlying these differences.

Lastly, correlation analyses showed that the Canadian students’ engagement in goal setting and management at time 2 (i.e., the middle of the semester) was positively related to their GPA and course grade, while that of the Chinese international students did not. In contrast, monitoring was positively associated with GPA at time 2 for both Canadian domestic and Chinese international students. These findings align with previous studies that found SRL is essential for students’ academic performance in Western learning contexts (Hadwin & Winne, 2012; Theobald, 2021). Notably, items of the monitoring factor, including understanding and remembering, could effectively capture the way of how Chinese students monitored their learning processes. As Kember (2000) stated, Chinese students tended to emphasize memorization and understanding through multiple continuous stages during their learning. However, our findings suggest that the impact of SRL engagement on academic achievement for Chinese learners is small or may be absent (Li et al., 2018). One possible explanation for this is that semester GPA and course grades may not accurately reflect Chinese international students’ learning outcomes, particularly since most of the Chinese participants were in their first year of university transitioning from high school to university and from the Chinese culture to the Canadian culture. Thus, other outcome measurements, such as academic engagement and retention, should be considered within future studies.

Overall, moderate to strong correlations between engagement in SRL processes and academic performance for Chinese and Canadians were absent in this study. While small correlations were found, they were insufficient to conclude that these correlations were meaningful. The process of gaining SRL skills takes time and effort, as it requires learners to proactively cultivate learning skills across various phases, strategically make tactical choices based on task understanding and study goals, and metacognitively monitor, evaluate, and regulate their learning processes (Hadwin & Winne, 2012)—all while controlling and inhibiting undesirable learning behaviors. Students enrolling in an educational psychology course that taught the science of learning and motivation may not effectively and immediately apply what they learned about SRL to other disciplinary courses. It may take more time for students to metacognitively apply the SRL knowledge and skills acquired from this course to their own learning. Research has shown that, although students possess accurate metacognitive knowledge about effective learning strategies, their intention to change is often not strong enough to break old habits and develop new ones (David et al., 2024). Therefore, using longitudinal data extending beyond a single 4-month semester is necessary to determine whether students’ active use of SRL practices could enhance their academic performance. Because of the adaptive nature of SRL as a dynamic process unfolding over time in contexts, using correlational analysis to examine the relation between SRL and academic performance limits understanding of how students’ SRL evolved over time to predict students’ academic performance. More importantly, our study found that four SRL constructs were not measured in the same way over time for Chinese international students, whereas these constructs retained similar psychological meaning for the Canadian sample. Therefore, future research should adopt longitudinal research designs to examine whether SRL constructs evolve or remain stable over time for students from distinct sociocultural learning backgrounds.

Limitations and Future Directions

In this study, the sample size of Chinese international students was small (n = 150). Our small sample could explain why the longitudinal measurement invariance was not supported for the Chinese sample and scalar measurement invariance of the four-factor RLQ model was not achieved between the Canadian and Chinese samples. Therefore, future research with a larger sample size for more rigorous analyses, such as structural equation modeling, is warranted. Moreover, using CFI in testing measurement invariance is less powerful when the sample size of each comparison group is unequal. It has been noted that the larger size group tends to exert a greater influence on the parameter estimation process of the constrained model (Cheung & Lau, 2012). Therefore, obtaining an equal or similar sample size across groups needs to be considered in future studies.

Given that questionnaires are usually structured within the framework of a Likert-type scaling point, it is possible that the item scores rated by some cultural groups could be biased (Byrne & Watkins, 2003). For example, Chinese international students could use their completion of the RLQ to show their active engagement in learning by selecting higher scale points as we found that Chinese international students’ mean scores of four SRL constructs across three time points were slightly higher than Canadian domestic students. Selecting higher points of a Likert scale may affect the factor analysis of an instrument, which could sway the resulting conclusions regarding its cross-cultural invariance (Hui & Triandis, 1989). Thus, it is important for future research to consider the impact of using a Likert-type scale in self-reported SRL measurements while investigating cross-cultural invariance. For example, it warrants further consideration in a cross-cultural study of whether the meaning of the Likert scale points (e.g., 2 = not really and 3 = sort of) is interpreted in the same way across various cultural groups and whether adopting a Likert scale with a neural midpoint (e.g., neither difficult nor easy) is necessary. Further, it might be difficult for Chinese international students to capture the exact meanings of certain items in English although they had met their university’s requirements for English language proficiency. Therefore, a cross-cultural modification of the RLQ’s items to eliminate ambiguous meanings for non-English spoken learners might be necessary in future research.

In addition, this study’s results cannot be generalized to students who have not enrolled in this university course. For example, Chinese international students enrolled in this educational psychology course demonstrated a strong motivation to gain self-regulatory competency and engage in various SRL practices to tackle their academic challenges. These limitations warrant a need to replicate this study with students who have not received any training on SRL to see whether a measurement invariance of the RLQ is then obtained. Additionally, all participants came from a Canadian university, which further limits the generalizability of our findings. Given that learning contexts are significantly different between Canada and China, Chinese students studying in a Canadian university may not be representative of those studying in China. Thus, we suggest that large-scale cultural studies including more diverse samples are needed to address this limitation.

Despite these limitations, the present study contributes to the current literature as no existing empirical study has investigated why an SRL instrument would fail in terms of its measurement invariance across cultural groups. When using SRL models developed and validated in the West, researchers must be cautious about whether indicators (survey items) of the questionnaire reflect the same underlying latent constructs of SRL cross-culturally. Lastly, the importance of this finding outlines the need for educators to be aware of the complex and dynamic interplay between culture and SRL. In other words, meaningful engagement in SRL may appear differently for students with different learning backgrounds. Intervention programs that incorporate culturally relevant curriculum content and foster SRL competency would effectively support diverse learners in educational settings.

Footnotes

Authors’ Note

This paper is based on a master’s thesis and was also presented at the American Educational Research Association.

ORCID iDs

Meng Qi Wu

Sungjun Won

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Social Sciences and Humanities Research Council of Canada (435-2018-0440 awarded to Allyson F. Hadwin and Joseph-Armand Bombardier Canada Graduate Scholarship awarded to Meng Qi Wu).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Due to the sensitive nature of the questions asked in this study, survey respondents were assured raw data would remain confidential and would not be shared.*

References

Bai

Wang

Nie

(2021). Self-efficacy, task values and growth mindset: What has the most predictive power for primary school students’ self-regulated learning in English writing and writing competence in an Asian Confucian cultural context? Cambridge Journal of Education, 51(1), 65–84. https://doi.org/10.1080/0305764X.2020.1778639

Baumeister

R. F.

(2011). The unity of self at the interface of the animal body and the cultural system. Psychological Studies, 56(1), 5–11. https://doi.org/10.1007/s12646-011-0062-5

Bempechat

Ronfard

(2018). Relations among cultural learning beliefs, self‐regulated learning, and academic achievement for low‐income Chinese American adolescents. Child Development, 89(3), 851–861. https://doi.org/10.1111/cdev.12702

Bentler

P. M.

(1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Boer

Hanke

(2018). On detecting systematic measurement error in cross-cultural research: A review and critical reflection on equivalence and invariance tests. Journal of Cross-Cultural Psychology, 49(5), 713–734. https://doi.org/10.1177/0022022117749042

Byrne

B. M.

(2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Routledge.

Byrne

B. M.

Shavelson

R. J.

Muthén

(1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105(3), 456–466. https://doi.org/10.1037/0033-2909.105.3.456

Byrne

B. M.

Watkins

(2003). The issue of measurement invariance revisited. Journal of Cross-Cultural Psychology, 34(2), 155–175. https://doi.org/10.1177/0022022102250225

Cheung

G. W.

Lau

R. S.

(2012). A direct comparison approach for testing measurement invariance. Organizational Research Methods, 15(2), 167–198. https://doi.org/10.1177/1094428111421987

10.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modelling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

11.

David

Biwer

Crutzen

de Bruin

(2024). The challenge of change: Understanding the role of habits in university students’ self-regulated learning. Higher Education, 88(5), 2037–2055. https://doi.org/10.1007/s10734-024-01199-w

12.

Feitosa

Lacerenza

C. N.

Joseph

D. L.

Salas

(2017). Ethnic identity: Factor structure and measurement invariance across ethnic groups. Psychological Assessment, 29(9), 1129–1141. https://doi.org/10.1037/pas0000346

13.

Greenfield

Keller

Fuligni

Maynard

(2003). Cultural pathways through universal development. Annual Review of Psychology, 54(1), 461–490. https://doi.org/10.1146/annurev.psych.54.101601.145221

14.

Hadwin

A. F.

(2009). Strategic learning questionnaire. Unpublished instrument.

15.

Hadwin

A. F.

Sukhawathanakul

Rostampour

Bahena-Olivares

L. M.

(2022). Do self-regulated learning practices and intervention mitigate the impact of academic challenges and COVID-19 distress on academic performance during online learning? Frontiers in Psychology, 13, 813529. https://doi.org/10.3389/fpsyg.2022.813529

16.

Hadwin

A. F.

Winne

P. H.

(2012). Promoting learning skills in undergraduate students. In Lawson

M. J.

Kirby

J. R.

(Eds.), Enhancing the quality of learning: Dispositions, instruction, and learning processes (pp. 201–227). Cambridge University Press. https://doi.org/10.1017/CBO9781139048224.013

17.

Helmke

Tuyet

V. T. A.

(1999). Do Asian and Western students learn in different way? An empirical study on motivation, study time, and learning strategies of German and Vietnamese university students. Asia Pacific Journal of Education, 19(2), 30–44. https://doi.org/10.1080/0218879990190204

18.

Henrich

Heine

S. J.

Norenzayan

(2010). Beyond WEIRD: Towards a broad-based behavioral science. Behavioral and Brain Sciences, 33(2-3), 111–210. https://doi.org/10.1017/S0140525X10000725

19.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modelling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

20.

Hui

C. H.

Triandis

H. C.

(1989). Effects of culture and response format on extreme response style. Journal of Cross-Cultural Psychology, 20(3), 296–309. https://doi.org/10.1177/0022022189203004

21.

Kember

(2000). Misconceptions about the learning approaches, motivation, and study practices of Asian students. Higher Education, 40(1), 99–121. https://doi.org/10.1023/A:1004036826490

22.

Leong

F. T. L.

Byrne

B. M.

Hardin

E. E.

Zhang

Chong

(2018). A psychometric evaluation of the loss of face scale. Psychological Assessment, 30(3), 396–409. https://doi.org/10.1037/pas0000492

23.

(2003). U.S. and Chinese cultural beliefs about learning. Journal of Educational Psychology, 95(2), 258–267. https://doi.org/10.1037/0022-0663.95.2.258

24.

(2012). Cultural foundations of learning: East and West. Cambridge University Press.

25.

Tang

Zhou

(2018). What are the effects of self-regulation phases and strategies for Chinese students? A meta-analysis of two decades research of the association between self-regulation and academic performance. Frontiers in Psychology, 9, 2434. https://doi.org/10.3389/fpsyg.2018.02434

26.

Lopez

E. J.

Nandagopal

Shavelson

R. J.

Szu

Penn

(2013). Self-regulated learning study strategies and academic performance in undergraduate organic chemistry: An investigation examining ethnically diverse students. Journal of Research in Science Teaching, 50(6), 660–676. https://doi.org/10.1002/tea.21095

27.

Markus

H. R.

Kitayama

(2010). Cultures and selves: A cycle of mutual constitution. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 5(4), 420–430. https://doi.org/10.1177/1745691610375557

28.

Martin

(2007). The selves of educational psychology: Conceptions, contexts, and critical considerations. Educational Psychologist, 42(2), 79–89. https://doi.org/10.1080/00461520701263244

29.

Marton

Dall’Alba

Kun

T. L.

(1999). Memorizing and understanding: The keys to the paradox? In Watkins

D. A.

Biggs

J. B.

(Eds.), The Chinese learner: Cultural, psychological, and contextual influences (pp. 69–84). Comparative Education Research Centre.

30.

McCardle

Hadwin

A. F.

(2015). Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning. Metacognition and Learning, 10(1), 43–75. https://doi.org/10.1007/s11409-014-9132-0

31.

McInerney

D. M.

(2011). Culture and self-regulation in educational contexts: Assessing the relationship of cultural group to self-regulation. In Zimmerman

B. J.

Schunk

D. J.

(Eds.), Handbook of self-regulation of learning and performance (pp. 442–464). Routledge.

32.

McInerney

D. M.

King

R. B.

(2017). Culture and self-regulation in educational contexts. In Patricia

P. A.

Schunk

D. J.

Greene

J. A.

(Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 485–502). Routledge. https://doi.org/10.4324/9781315697048

33.

Panadero

(2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422

34.

Pintrich

P. R.

(2000). The role of goal orientation in self-regulated learning. In Boekaerts

Pintrich

P. R.

Zeidner

(Eds.), Handbook of self-regulation (pp. 451–502). Academic Press.

35.

Pintrich

P. R.

Smith

D. A. F.

Garcia

Mckeachie

W. J.

(1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/0013164493053003024

36.

Ponterotto

J. G.

Gretchen

Utsey

S. O.

Stracuzzi

Saya

(2003). The multi-group ethnic identity measure (MEIM): Psychometric review and further validity testing. Educational and Psychological Measurement, 63(3), 502–515. https://doi.org/10.1177/0013164403063003010

37.

Pratt

D. D.

Kelly

Wong

W. S. S.

(1999). Chinese conceptions of “effective teaching” in Hong Long: Towards culturally sensitive evaluation of teaching. International Journal of Lifelong Education, 18(4), 241–258. https://doi.org/10.1080/026013799293739a

38.

Purdie

Hattie

(1996). Cultural differences in the use of strategies for self-regulated learning. American Educational Research Journal, 33(4), 845–871. https://doi.org/10.2307/1163418

39.

Putnick

D. L.

Bornstein

M. H.

(2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004

40.

Schunk

D. H.

Zimmerman

B. J.

(1997). Social origins of self-regulatory competence. Educational Psychologist, 32(4), 195–208. https://doi.org/10.1207/s15326985ep3204_1

41.

Shi

Frederiksen

C. H.

Muis

K. R.

(2013). A cross-cultural study of self-regulated learning in a computer-supported collaborative learning environment. Learning and Instruction, 23, 52–59. https://doi.org/10.1016/j.learninstruc.2012.05.007

42.

Sitzmann

Ely

(2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: What we know and where we need to go. Psychological Bulletin, 137(3), 421–442. https://doi.org/10.1037/a0022777

43.

Steiger

J. H.

(1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2), 173–180. https://doi.org/10.1207/s15327906mbr2502_4

44.

Theobald

(2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, 101976. https://doi.org/10.1016/j.cedpsych.2021.101976

45.

Tong

Guo

Wang

Min

Guo

Yoon

(2019). Examining cross-cultural transferability of self-regulated learning model: An adaptation of the motivated strategies for learning questionnaire for Chinese adult learners. Educational Studies, 46(4), 1–18. https://doi.org/10.1080/03055698.2019.1590183

46.

Vandenberg

R. J.

Lance

C. E.

(2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. https://doi.org/10.1177/109442810031002

47.

Winne

P. H.

(2015). Self-regulated learning. In Wright

J. D.

(Ed.), International encyclopedia of social & behavioral sciences (2nd ed., pp. 535–540). Elsevier.

48.

Winne

P. H.

Hadwin

A. F.

(1998). Studying as self-regulated learning. In Hacker

Dunlosky

Graesser

(Eds.), Metacognition in educational theory and practice (pp. 277–304). L. Erlbaum Associates.

49.

Winne

P. H.

Hadwin

A. F.

(2008). The weave of motivation and self-regulated learning. In Schunk

D. H.

Zimmerman

B. J.

(Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–314). Erlbaum.

50.

K. M.

Cunha-Harvey

A. R.

King

R. B.

de Koning

B. B.

Paas

Baars

Zhang

de Groot

(2021). A cross-cultural investigation on perseverance, self-regulated learning, motivation, and achievement. Compare: A Journal of Comparative and International Education, 53(3), 1–19. https://doi.org/10.1080/03057925.2021.1922270

51.

Zhu

Valcke

Schellens

(2008). A cross-cultural study of Chinese and Flemish university students: Do they differ in learning conceptions and approaches to learning? Learning and Individual Differences, 18(1), 120–127. https//doi.org/10.1016/j.lindif.2007.07.004.

52.

Zimmerman

B. J.

(1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329

53.

Zimmerman

B. J.

(2000). Attaining self-regulation: A social cognitive perspective. In Boekaerts

Pintrich

P. R.

Zeidner

(Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.

54.

Zimmerman

B. J.

(2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

55.

Zimmerman

B. J.

Schunk

D. H.

(2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). L. Erlbaum.

Measuring Self-Regulated Learning for Chinese International and Canadian Domestic Undergraduate Students in Canada

Abstract

Keywords

Introduction

Literature Review

Methods

Participants

Procedure

Measures

Self-Regulated Learning

Academic Performance

Statistical Analyses

Results

Measurement Invariance

Relationships Between SRL and Academic Performance

Discussion

Limitations and Future Directions

Footnotes

Authors’ Note

ORCID iDs

Funding

Declaration of Conflicting Interests

Data Availability Statement

References