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Abstract

China is internationalizing its higher education by setting up international branch campuses (IBS) nationwide. This transformation also indicates a shift in teaching, learning, and curriculum for Chinese students studying at these IBS. Chinese traditional education is based on the values of respect and hierarchy, which limit students’ independence and autonomy in making decisions. Western educational philosophies and practices that emphasize freedom and divergent thinking support students’ independent learning skills. While independent learning is encouraged, the extent to which cognitive autonomy, metacognition, and self-regulation contribute to this process in Chinese higher education remains underexplored. Therefore, the primary objective of this study is to determine whether psychological factors such as cognitive autonomy, metacognition, and self-regulation will predict independent learning, which in turn would predict students’ academic performance. We hypothesize that these psychological constructs would predict independent learning, mediating students’ academic performance. Using an online survey, we collected self-report data from 226 undergraduate students from two Sino-American universities in the Zhejiang province of China. The associations among the constructs were evaluated using structural equation modelling. The results suggested that the data fit the proposed model well. The results have implications for teaching, learning, and student development activities to enhance students’ independent learning.

Keywords

cognitive autonomy metacognition self-regulation independent learning teaching and learning higher education

Students’ independent learning, which often refers to autonomous, self-planned, or self-directed learning, is at the heart of the student-centred learning model in higher education (Benson, 2011). Independent learning has several positive outcomes, including high academic performance, improved motivation, persistence, and self-awareness (Chan, 2001; Reeve & Cheon, 2021). To highlight the key characteristics of independent learners, Knowles (1975) described them as individuals who take initiative, identify their learning needs, seek appropriate resources to support their learning, and apply suitable strategies in response to the evaluation of their progress. Building on this foundation, Brockett and Hiemstra (2018) defined self-direction as a process of adult learning, which is also shaped by the learner’s surroundings and personality.

Higher education research has largely focused on how external factors, such as instructional design and assessment strategies, shape independent learning (Murray, 2014; Wenden, 1991; Zhong, 2018). However, cognitive autonomy, self-regulation, and metacognition—key psychological mechanisms that enable learners to take ownership of their learning—have received comparatively less attention. The concept of personal responsibility, defined as “ownership for one’s thoughts and actions” in self-directed learning (Knowles, 1975, p. 27), is shaped by learners’ capacity to regulate their thinking, motivation, and behavior, as well as their ability to make decisions and manage their own learning (Saks & Leijen, 2014). Therefore, the current study investigates how psychological factors such as cognitive autonomy, self-regulation, and metacognition predict independent learning.

The investigation of these factors is also unique, considering the context of the study, which is Sino-American higher education institutions. The three constructs, cognitive autonomy, self-regulation, and metacognition, are rooted in self-management, evaluative and independent thinking, and decision-making skills. The development of these attributes is contingent upon close environments such as culture, teaching and learning philosophy, and pedagogical practices (Meyer et al., 2008). China’s traditional education system, rooted in principles of humility, hierarchy, and respect, emphasizes rote memorization and passive learning, limiting students’ opportunities for critical dialogue and independent decision-making (Kaur, 2020). With a strong focus on teacher-led instruction and exam performance, students often struggle to develop self-directed learning skills, critical thinking, and self-regulation. The lack of exposure to open-ended inquiry, problem-solving tasks, and autonomous learning experiences hinders their ability to set learning goals, monitor progress, and adapt strategies based on reflection. Consequently, they tend to rely on structured guidance rather than cultivating autonomy and self-directedness in their learning process (Meng et al., 2021). In contrast, Sino-American universities adopt a Western educational model emphasising personal agency, critical thinking, and independent decision-making while fostering self-directed learning. These institutions generally follow a curriculum centered on student engagement, the development of cognitive independence, and critical analysis (Zhang, 2016). Teaching methods focus on active participation, including group discussions, problem-solving, and experiential learning, encouraging deeper student engagement. Assessments combine formative methods, such as projects and presentations, with summative exams (Kaur et al., 2019b). This shift in pedagogy presents a unique context for examining how students navigate the transition from a structured, exam-driven system to an environment that demands greater self-regulation, cognitive autonomy, and metacognitive awareness. Thus, it is essential to investigate how students in Sino-American universities develop and apply these psychological constructs in their learning. Understanding this interplay can offer insights into how educational environments shape independent learning and provide implications for curriculum design, pedagogical strategies, and student support systems in transnational education settings. However, limited research has explored how psychological constructs such as cognitive autonomy, metacognition, and self-regulation jointly contribute to independent learning in transnational Chinese higher education settings. Based on empirical evidence, we hypothesized that cognitive autonomy, metacognition, and self-regulation would predict independent learning, which in turn would mediate students’ academic performance.

Literature review

Learning scientists have offered multiple explanations for why some students learn and perform better than others. For example, behaviorists focused on the role of environment, reward, and punishment in shaping human learning (Skinner, 1989). Constructivists (Paris & Byrnes, 1989) believe supportive environments are crucial for success, while theorists of attribution (Dweck, 1986) concentrate on individual outcomes such as effort or talent. Cognitive learning theorists (Flavell, 1987) have emphasized the self-management of cognition and behaviors in determining students’ success. In line with the ideas of these learning theorists, we present a discussion on cognitive autonomy, self-regulated learning, and metacognition and their relationship with independent learning.

Independent/autonomous learning

The literature proposes several synonyms for independent learning. For example, Kesten (1987) described it as “autonomous learning, independent study, self-directed learning, student-initiated learning, project orientation, discovery and inquiry, teaching for thinking, learning to learn, self-instruction, and lifelong learning” (p. 9). Regardless of the term used, independent or autonomous learning refers to learners’ ability to regulate and monitor their learning without relying on external or structured sources such as textbooks or teachers (Race, 2002). It also indicates the transfer of accountability for learning from teachers to students and away from external support systems (Kobiljanovna, 2021).

Meyer et al. (2008) identified several benefits of independent learning for tertiary education students: improved academic performance, increased motivation and confidence, and greater awareness of their limitations and ability to manage them. Their review also demonstrated that the development of independent learning depends on several external and internal factors. For example, external factors such as strong relatedness between teachers and students, meaningful and frequent interaction, and pedagogical style facilitate the development of autonomous learning. On the other hand, internal factors include cognitive skills such as attention, memory, and problem-solving; metacognitive skills, which involve understanding, monitoring, and regulating one’s own learning; and affective factors related to emotions and motivation (Meyer et al., 2008). These internal factors are closely aligned with the present study’s model, which hypothesizes that cognitive autonomy, metacognition, and self-regulation are key predictors of independent learning.

Cognitive autonomy is the ability to think independently, evaluate information critically, and make reasoned decisions without relying excessively on external validation. Its development allows students to analyze different perspectives, challenge assumptions, and construct their own understanding of concepts (Beckert, 2007). This ability fosters deeper engagement with learning materials as students become more confident in forming and defending their viewpoints. Metacognition, which involves awareness and regulation of one’s own thinking and learning strategies, enables students to monitor their understanding and adjust approaches when necessary (Pintrich, 2002). Self-regulation, meanwhile, supports goal setting, time management, and sustained motivation during the learning process (Zimmerman, 2000). While each construct plays a distinct role, together they equip students to take ownership of their learning, manage academic challenges independently, and develop the capacity for lifelong autonomous learning.

The literature also suggests that several teaching and environmental interventions can facilitate the development of independent learning skills and mindsets among learners. For example, providing scaffolding, opportunities to self-monitor, independent communication, and decision-making ability can also facilitate the development of self-directed learning (Meyer et al., 2008). In China, the value of self-directed learning is highly recognized, and there is evidence of its implementation throughout higher education and in the nursing sector (e.g., Yang & Jiang, 2014). However, while independent learning is encouraged, the extent to which cognitive autonomy, metacognition, and self-regulation contribute to this process in Chinese higher education remains underexplored.

Cognitive autonomy

Given their shared emphasis on student-directed learning, cognitive autonomy, self-regulation, and metacognition are often discussed under the broader umbrella of independent learning. However, each construct represents a distinct dimension of learner agency. To establish a clearer understanding of their individual roles, we begin by reviewing the literature on cognitive autonomy.

Cognitive autonomy refers to students’ ability to learn independently, assess situations, and make independent decisions. Students with a high level of cognitive autonomy can invite and listen to different opinions, deliberate, compare and evaluate, and make decisions independently. The construct was measured using five domains: the ability to engage in evaluative thinking, personal opinion, decision-making, self-assessment, and comparative validation (Beckert, 2007). These dimensions reflect the individual’s ability to use logical deductive thinking to evaluate choices and reach an ultimate goal. They explicitly express personal opinions, engage in dialogue, and thoughtfully consider, compare, and evaluate multiple options. Thus, a lack of cognitive autonomy can lead adolescents to engage in meaningless behavior (Beckert, 2007).

Deniz (2022) describes cognitive autonomy as an integral part of independent learning and a key factor in young people with learning difficulties becoming self-directed learners and autonomous individuals. Similarly, Furtak and Kunter (2012) highlight that cognitive autonomy enables students to self-evaluate by assessing their abilities, strengths, weaknesses, interests, and preferences, ultimately allowing them to make informed choices. This self-evaluative process fosters a sense of independence and ownership over learning. Moreover, research by Stefanou et al. (2004) indicates that in classrooms with strong cognitive autonomy support, students demonstrate heightened enthusiasm and engagement in independent learning when given opportunities to make cognitive choices related to solution strategies. This growing body of research suggests that cognitive autonomy is a key predictor of self-directed learning. As students develop the ability to think critically, make independent judgments, and regulate their learning processes, they become more capable of setting their own goals, managing their progress, and adapting their strategies which are hallmarks of self-directed learning. Thus, fostering cognitive autonomy enhances students’ confidence in their intellectual capabilities and equips them with the necessary skills to navigate complex learning environments independently.

Buendía Arias (2015), in a comparative study, concluded that Chinese students demonstrated less cognitive autonomy in English learning than their Western counterparts. This indicates that Chinese college students had a limited understanding of their goals when choosing to learn English and were more dependent on their teachers. This leads to weaker management and evaluation of their own learning goals (Buendía Arias, 2015). Another study of 103 university students from 29 universities in China found that although students cognitively believed they could learn autonomously, their beliefs about their role in learning failed to translate into expected autonomous learning behaviors (Lin & Reinders, 2019). These studies revealed a deficit in cognitive autonomy among Chinese college students, which is reflected in their considerable difficulty in making independent decisions and controlling their learning. Given this, Sino-American educational institutions that emphasize experiential and project-based learning may provide more opportunities for students to engage in independent thinking, decision-making, and self-regulation. Thus, it would be meaningful to investigate whether cognitive autonomy among students in such learning environments predicts independent learning.

Self-regulated learning

Self-regulation is crucial in predicting self-directed learning, equipping students to manage their learning process effectively. Self-regulated learning (SRL) involves goal setting, planning, monitoring progress, making necessary adjustments, and evaluating performance (Chen & Lin, 2018; Zimmerman, 2000). These processes mirror the characteristics of self-directed learning, where learners take the initiative, exercise control over their educational experiences, and persistently refine their approaches to achieve learning goals. Students with strong self-regulation actively manage their cognition, motivation, and behaviour, allowing them to maintain focus, adapt to challenges, and sustain engagement in learning tasks (Efklides, 2011; Zimmerman, 1990). Furthermore, self-regulated individuals demonstrate perseverance and resilience, key attributes that enable them to navigate independent learning without reliance on external supervision (Moilanen et al., 2015). Research suggests that self-regulated learners achieve greater academic success and well-being (Tey et al., 2018), so it is reasonable to argue that self-regulation strongly predicts self-directed learning. Students who can effectively regulate their learning are better positioned to set goals, select appropriate strategies, and independently monitor their progress, ultimately fostering a self-sustaining learning cycle.

A study in the Chinese context showed that students were motivated to manage self-resource and external resource management, such as controlling their study time and seeking help from teachers (Guo et al., 2019). In addition, according to Li et al. (2018), self-efficacy, task strategy, and self-evaluation are the main strategies of Chinese students’ self-regulated learning. However, while Chinese students demonstrate strategic resource management and self-regulation techniques, research suggests that external guidance and teacher-directed instruction may still influence their learning behaviours. This raises questions about how their self-regulation translates into true self-directed learning, where learners independently take charge of their educational experiences beyond structured academic settings. Given that Sino-American educational institutions emphasize experiential and project-based learning, students in such environments may have more opportunities to practice self-regulation in a way that fosters greater independence. Investigating how self-regulated learning predicts self-directed learning in these settings could provide valuable insights into how different pedagogical approaches shape students’ ability to take ownership of their learning.

Meta-cognition

Metacognition is crucial in predicting independent learning, enabling students to regulate their thinking, assess their understanding, and refine their learning strategies. Metacognition involves storing and retrieving information effectively while continuously evaluating one’s comprehension and abilities (Ku & Ho, 2010). Research by Veenman et al. (2004) highlights that metacognitive skills contribute more to learning outcomes than intelligence alone, suggesting that students who actively monitor and control their cognitive processes can overcome limitations in cognitive ability (Veenman et al., 2006). Furthermore, students with well-developed metacognitive skills are more likely to achieve academic success, master content efficiently, and meet their learning goals on time (Winston et al., 2010). Since independent learning requires students to identify what needs to be learned, determine the most effective approach, and adjust their strategies, metacognitive skills such as monitoring, control, and self-assessment are essential. By fostering these skills, students gain greater autonomy over their learning process, making metacognition a key predictor of independent learning.

The relationship between metacognition and independent learning is particularly relevant when considering the context of Sino-American education. A study of Chinese English majors at Guizhou University found that both high- and low-performing students reported using metacognitive strategies at a moderate level, with selective attention emerging as the most frequently employed technique. However, self-reflection and self-evaluation strategies were the least used, suggesting gaps in developing more advanced metacognitive skills (Zhang & Seepho, 2013). Similarly, Chinese college students at a private university reported never receiving formal metacognitive education and expressed a lack of understanding regarding the usefulness of metacognitive strategies (Pei, 2014). These findings indicate that while Chinese students may use some metacognitive strategies, there is still a significant need for education that fosters higher-order metacognitive skills, such as self-reflection and evaluation, which are critical for independent learning.

In contrast, Sino-American educational environments, which emphasize experiential and project-based learning, offer greater opportunities for students to develop these essential metacognitive skills. By engaging in learning that requires them to assess their progress, reflect on their strategies, and adapt their approaches, students are more likely to cultivate the metacognitive awareness needed for autonomous learning. Therefore, investigating how metacognitive skills predict independent learning in these settings could provide valuable insights into how different pedagogical approaches support the development of self-regulation and autonomy in students. However, while each construct has been studied independently, few empirical models have examined their combined predictive value on independent learning within Sino-foreign education settings. Based on empirical evidence, we hypothesized that cognitive autonomy, metacognition, and self-regulation would predict independent learning which in turn would mediate students’ academic performance.

Methodology

A cross-sectional survey design research (Creswell, 2014; Creswell & Creswell, 2017) was employed to collect students’ responses on cognitive autonomy, metacognition, self-regulation, independent learning, and academic performance. This design was appropriate given the study’s focus on identifying statistical relationships among psychological constructs in a specific educational context. The quantitative research design is precise and narrow in focus, allowing concentration in objective and minimal bias. The findings of sample respondents could be used to generalize the target population. (Creswell, 2014).

Participants and sampling

The target population for this investigation was undergraduate students obtaining an education at international branch campuses in China. This sampling focus aligns with the research objective of understanding how students in transnational institutions develop independent learning dispositions in culturally hybrid educational environments. With this goal, at least 500 undergraduate students from two Sino-American universities in China were invited to participate in the study. An online survey link was sent to the participants through university representatives to be disseminated in undergraduate classes. In total, 332 responses were received. After missing data and outlier analysis, 226 responses were deemed appropriate for further analysis.

Of the total participants, 36.3% were female and 63.7% were male. The mean age of the participants was 20.55 years (SD = 2.67), with an age range between 18 and 22 years. Regarding the level of education in the four-year undergraduate program, 73.9% were in their senior year, 15.9% in their junior year, 5.3% in their sophomore year, and 4.9% were freshmen. Regarding disciplines, 58.41% were reported to be in social sciences and humanities, 32.2% in sciences, and 9.4% in others.

Data collection

A survey using psychometrically sound and validated instruments was employed to collect data. Given the challenges posed by the pandemic, data collection occurred online through a survey link distributed via the https://www.wjx.cn platform. The link was shared with teachers teaching undergraduate students at two Sino-American universities, and the teachers assisted in forwarding the survey to eligible students. The instruments used in the survey were specifically selected for their reliability and validity and were translated into Chinese using Brislin’s (1970) back-translation method. This involved two independent translators who translated the instruments from English to Chinese and then compared their translations to resolve discrepancies and ensure the accuracy of the final version. Institutional permission was sought, and teachers and students were provided with a detailed briefing on the purpose of the study before data collection. Participants were informed that their participation was voluntary and that their responses would remain anonymous and confidential. The institution’s ethics review board granted ethical approval for the study. Participants were informed that their participation was voluntary and that no compensation was offered.

Instruments

Independent learning scale

Independent learning refers to students’ autonomous learning habits. This construct was measured using 24 Items inventory from (Swatevacharkul, 2009) on a 1–5 (1-strongly disagree, -strongly agree) scale. An example of the construct is “I need to control myself to do learning tasks that I think I should do”. Internal consistency reliability (Cronbach’s α) for this construct was 0.86.

Cognitive autonomy scale

Cognitive autonomy refers to students’ ability to stand alone and be self-sufficient in making decisions. Ten items from Cognitive Autonomy and Self Evaluation Inventory (Beckert, 2007) were measured on a 1-5 scale. (1-strongly disagree, 5-strongly agree). An example of an item is “I think about the consequences of my decisions”. Internal consistency reliability (Cronbach’s α) for this construct was 0.80.

Self-regulation scale

Self-regulation refers to students’ ability to plan and monitor their own learning. This construct was measured using a 19-item scale (Chen & Lin, 2018) to respond on a 1–5 scale. (1-strongly disagree, 5-strongly agree). An example of an item is, “Once I have a goal, I can usually plan how to reach it.” Internal consistency reliability (Cronbach’s α) for this construct was 0.78.

Metacognition scale

Metacognition refers to the ability to think about own learning. This construct was measured using 13 inventory items from O'Neil and Abedi (1996). The responses were measured on a 1–5 scale. (1-strongly disagree, 5-strongly agree). An example of an item is “I was aware of which thinking technique or strategy to use and when to use it”. Internal consistency reliability (Cronbach’s α) for this construct was 0.86.

Academic achievement

Academic achievement was measured using students’ current GPA. GPA was self-reported by students based on the 5.0 grading scale used at their respective institutions.

Data analysis

Statistical Package for the Social Sciences (SPSS) version 25 was utilized for the data screening process and descriptive data analysis. As a preliminary analysis, descriptive statistics (Table 1) and correlational analysis were conducted among all study variables (Table 2). Given the model’s complexity and multiple latent constructs, structural equation modeling (SEM) was selected to analyze direct and mediated relationships. Independent t test analysis and ANOVA were run to test whether any gender and education-level-based individual differences occurred among the study variables. Similarly, regression analysis was conducted to test the first hypothesis using SPSS 25.

Table 1.

Demographic information of the participants.

Variable	Category	Percentage / value
Gender	Female	36.3%
Gender	Male	63.7%
Age	Mean age (SD)	20.55 years (SD = 2.67)
Age	Age range	18–22 years
Year in undergraduate program	Senior year	73.9%
	Junior year	15.9%
	Sophomore year	5.3%
	Freshman year	4.9%
Discipline	Social sciences and humanities	58.4%
	Sciences	32.2%
	Other	9.4%

N = 226.

Table 2.

Descriptive statistics of study variables.

Variable	Mean	Median	SD	Skewness	SE (Skew)	Kurtosis	SE (Kurt)
Cognitive autonomy	3.83	3.80	0.50	−0.06	0.162	0.57	0.322
Self-regulation	3.29	3.21	0.40	1.86	0.162	5.70	0.322
Metacognition	3.04	3.00	0.38	0.41	0.162	0.78	0.322
Independent learning	3.55	3.50	0.41	0.83	0.162	1.35	0.322
Academic achievement	4.66	4.60	1.05	0.17	0.162	0.20	0.322

n = 226.

To test the hypothesized mediation model, a path model analysis with composite variables was employed to evaluate the relationships among the constructs of the proposed model and to test the hypotheses of the present study. SEM is among the most advanced statistical analysis methods, combining aspects of factor analysis and regression, which enables researchers to investigate the correlations among latent constructs simultaneously (Hair et al., 2013).

Results

Preliminary analysis: Descriptive statistics, correlations, and individual differences

Table 1 presents the mean, standard deviation, normality of the distribution (kurtosis and skewness), and correlations among the variables. According to Leech et al. (2005), acceptable values for skewness and kurtosis are within the range of +1.00 and −1.00. Skewness values ranging from −0.06 to 1.86 and kurtosis values ranging from −0.20 to 1.35 signify the normal distribution of the variables. Furthermore, the Pearson correlation showed significant positive relationships among the main variables, with values ranging from 0.26 to 0.54.

Our independent t test analysis of all study variables revealed no significant differences based on gender, with values for cognitive autonomy (t = 1.61, p > .05), self-regulation (t = 0.62, p > .05), metacognition (t = 0.68, p > .05), independent learning (t = 1.57, p > .05), and academic achievement (t = 1.98, p > .05). Furthermore, ANOVA showed no significant differences in the study variables based on the year of study, with values for cognitive autonomy (F = 0.44, p > .05), self-regulation (F = 0.90, p > .05), metacognition (F = 0.50, p > .05), independent learning (F = 0.44, p > .05), and academic achievement (F = 0.19, p > .05).

The relevant assumptions for the final analysis were tested. First, a sample size of 226 participants was deemed adequate for testing three independent variables in one model (Tabachnick & Fidell, 2001). The assumption of singularity was also met, as the independent variables (cognitive autonomy, metacognition, and self-regulation) were not combinations of other independent variables. An examination of correlations further revealed that no independent variables were highly correlated (Tables 3 and 4).

Table 3.

Correlation between study variables.

	Cognitive autonomy	Self-regulation	Metacognition	Independent learning	Academic achievement
1. Cognitive autonomy	—
2. Self-regulation	0.425*	—
3. Metacognition	0.544*	0.348*	—
4. Independent learning	0.586*	0.501*	0.522*	—
5. Academic achievement	0.399*	0.262*	0.268*	0.549*	—

n = 226, *p < .001.

Table 4.

Standardized beta coefficients of the hypothesized model.

90% CI
	β	Low	High
CA---IL	0.34*	0.21	0.44
SR---IL	0.27*	0.16	0.38
MC---IL	0.24*	0.14	0.35
IL---AA	0.55*	0.45	0.62
CA---IL---AA	0.19*	0.11	0.26
SR---IL---AA	0.15*	0.08	0.22
MC---IL---AA	0.13*	0.08	0.19

*p < .01.

CA: Cognitive autonomy, SR: Self-regulation, MC: Metacognition, IL: Independent learning, AA: Academic achievement.

Main analysis

To achieve the research objective, the following hypotheses were tested.

Independent learning will mediate the relationship between cognitive autonomy, metacognition, and self-regulation, and academic performance.

We ran structural equation modelling to test the mediation model using AMOS version 24. The goodness of fit was evaluated using several fit indices (Hu & Bentler, 1999), including the chi-square to degrees of freedom (χ²/df) ratio, the Comparative Fit Index (CFI), the Tucker Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). The cutoff values defined for model fit were SRMR less than 0.08, RMSEA less than or equal to 0.06 (with a 90% confidence interval), and CFI and TLI equal to or greater than 0.90 (Hu & Bentler, 1999). The cutoff value for the χ²/df ratio is between 2.0 and 5.0. RMSEA values less than 0.08 and CFI and TLI values greater than 0.90 indicate good model fit (Hair et al., 2010). The mediation model achieved a very good fit to the data: χ²/df ratio = 2.5 (χ² = 2416.60, df = 966), CFI = 0.99, TLI = 0.98, RMSEA = 0.04, and SRMR = 0.04.

Path coefficients indicated that cognitive autonomy (β = 0.34, p < .001), self-regulation (β = 0.27, p < .001), and metacognition (β = 0.24, p < .001) positively and significantly predicted independent learning. This, in turn, significantly predicted students’ academic performance (β = 0.55, p < .001).

The significance level of the indirect effects was tested using the bias-corrected bootstrap approach with 2000 samples at the 90% confidence interval level. The results indicated that the indirect paths from cognitive autonomy (β = 0.19, p < .001), self-regulation (β = 0.15, p < .001), and metacognition (β = 0.13, p < .001) to academic achievement were statistically significant. The total variance for independent learning and academic achievement was 46% and 30%, respectively. The data from the model fit statistics and beta weights demonstrated that the proposed model is valid (Figure 1).

Figure 1.

The hypothesized model.

Discussion

The primary objective of this study was to determine whether psychological factors such as cognitive autonomy, metacognition, and self-regulation predict independent learning, which, in turn, would predict the academic performance of Sino-American University students in China. The results confirmed the hypothesized mediation model: all three psychological constructs were positively associated with independent learning, and independent learning significantly predicted academic performance. This aligns with previous research emphasizing the role of learner agency and cognitive control in student-centered learning environments (Meyer et al., 2008; Zimmerman, 2013). The literature informs that independent learning requires students’ autonomy from structural guidance from teachers and textbooks. It also seeks students to set goals, pursue them, and monitor their learning by themselves (Meyer et al., 2008). Independent learning is rooted in assuming accountability and independent decision-making abilities of students, and predicting their academic performance.

Cognitive autonomy is characterized by students’ ability to think independently, evaluate, and make independent judgments (Lee & Beckert, 2012). For independent learning, students with well-developed cognitive autonomy can undertake comparative evaluations of their learning progress in relation to their classmates and peers and environmental factors. The findings revealed that Cognitive autonomy, in particular, emerged as a strong predictor of independent learning. This supports Beckert et al. (2012), who noted that students with higher cognitive autonomy can critically evaluate and assume responsibility for their learning. In the present study, students who expressed confidence in their independent judgment and evaluative reasoning were likelier to monitor and direct their own learning, consistent with du Toit-Brits and van Zyl (2017). Cognitive autonomy also entails students’ ability to voice their opinions and share their perspectives with others to undertake complex tasks such as learning independently. These traits demonstrate a clear link with students’ independent learning. They can think critically, assess information, and make independent decisions, enabling them to take charge of their learning process.

Similarly, self-regulation significantly predicted independent learning. This finding confirms prior work that emphasizes how goal-setting, task management, and self-monitoring contribute to self-directed learning capacities (Efklides, 2011; Zeidner et al., 2000). The evidence suggests that fostering self-regulatory habits can help students shift from external dependence to internally driven learning, a central challenge in Chinese higher education. The evidence suggests that students with enhanced self-regulated learning skills can monitor, control, and regulate their learning, which leads to positive achievements (Zeidner et al., 2000). There is certainly a great deal of evidence to suggest that students who engage in self-regulation and autonomous learning do better in school, learn more, and achieve at high levels (Lewis & Vialleton, 2011). These skills undoubtedly help students to become autonomous learners.

Metacognition also significantly associated with independent learning, aligning with the argument that strategic self-reflection and cognitive monitoring are essential for academic success (Ku & Ho, 2010; Veenman et al., 2006). Students who exhibited greater awareness of their own learning strategies and outcomes demonstrated stronger tendencies toward autonomous learning. Students with enhanced metacognition can adopt independent learning skills more effectively than those without these skills. Numerous studies have demonstrated the crucial role of metacognitive abilities in enhancing learning processes and outcomes. Research (Karatas & Arpaci, 2021; Young & Fry, 2012) indicates that learners with well-developed self-regulated learning strategies achieve superior outcomes because they adopt autonomous approaches to tackle learning challenges for enhanced academic outcomes. These individuals exhibit intrinsic motivation, rely on structured learning methods, and actively engage in goal setting, planning, organization, memorization, and self-monitoring.

Importantly, while each construct was independently significant, the strength of their combined influence highlights the need for integrated pedagogical strategies that foster all three domains. This has particular relevance in transnational education contexts where students transition from teacher-centered models to learner-driven environments. The findings also respond to a gap in current scholarship, where few studies have examined how these three constructs function together within the culturally hybrid settings of Sino-foreign universities. While prior literature offers isolated accounts of each factor, this study demonstrates their collective predictive value for academic outcomes.

It is clearly evident in the literature that the development of these three skills is highly contingent upon environmental factors such as parental, pedagogical, and social structures (Kaur et al., 2019a; Lee & Beckert, 2012; Ramli et al., 2018). Specifically, in educational contexts, if lecturers provide adequate autonomy support for students to engage in the decision-making process, voice their opinions, and demonstrate agency, students are likely to develop higher cognitive abilities and self-management abilities (Draper & Fisher, 2020; González & Paoloni, 2015). While the study does not establish causality, it does offer insight into how psychological readiness for autonomy may be enhanced in international branch campuses. Although existing literature suggests that Western-style pedagogy can foster independent learning (Zhang, 2016), the present study cannot confirm this link definitively. Still, the statistically significant associations observed may reflect students’ exposure to active learning, formative assessment, and autonomy-supportive teaching (Draper & Fisher, 2020; González & Paoloni, 2015).

Assessment methods also reflect this philosophy, focusing on formative assessments such as projects, presentations, and reflective essays, alongside summative evaluations like exams (Kaur et al., 2019b). These practices aim to measure content knowledge and analytical and problem-solving abilities. Instructional delivery styles incorporate diverse tools, such as technology-enhanced learning platforms, which foster metacognition, self-regulation, and independent learning. By contrast, traditional Chinese curricula have historically emphasized rote memorization and teacher-led instruction, with assessments heavily reliant on standardized testing (Noman et al., 2023).

These results may have wider relevance for internationalized higher education globally. As more universities adopt student-centred curricula, the psychological factors underpinning independent learning become essential targets for both instruction and support services. This is particularly important for learners accustomed to directive pedagogical cultures, where metacognitive awareness and self-direction must be intentionally cultivated.

Implications and limitations

Our findings have direct and clear implications for university teaching and learning, as well as student development, to incorporate learning content and extracurricular activities that will enhance students’ self-regulation, metacognitive abilities, and cognitive autonomy. The research findings from the three independent variables provide evidence that self-regulation, metacognition, and cognitive autonomy can be fostered through external environmental efforts such as university curriculum, pedagogical techniques, use of learning strategies and tools, and extracurricular activities and policies for student development and participation.

The benefits of the internationalization of higher education, especially in terms of teaching and learning at Sino-American universities, can be transferred to other institutions of higher learning in China. For example, learning based on project-based and inquiry-based models linked to solving problems in real-life settings would encourage students to be open, curious, and think critically. It would allow students to pose questions, work collaboratively with peers, engage in research, evaluate viewpoints to accomplish tasks, and self-regulate their learning process (Kokotsaki et al., 2016). Including independent projects for assessment as learning would curb the use of tests and quizzes widely used in Chinese education. Innovative assessment methods that require students to engage in problem-solving, creative thinking, and collaborative learning will promote cognitive autonomy and provide opportunities for metacognition (Kaur et al., 2017). Finally, providing students opportunities to collaborate closely with faculty on curricular or extracurricular activities (Matthews et al., 2019) will support student accountability, decision-making engagement, and learning process self-regulation. The findings from this study are expected to provide educators with insights into the relevance of psychological constructs grounded in cognitive functioning. Instructors will be encouraged to think critically and design suitable strategies and content to enhance students’ cognitive competence.

This study is not without its limitations. It is a cross-sectional study, so it is not appropriate to establish causal links between independent and dependent variables. Other methodologies, such as experimental designs and observational methods, will contribute better insights into this model. Also, future research should consider using more diverse samples across multiple institutions or employing stratified random sampling to enhance external validity. Finally, including data from other foreign campuses in China and using multilevel modelling techniques can provide deeper insights into the proposed relationships.

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Provincial Grant Zhejiang, China (KT2021134).

ORCID iDs

Amrita Kaur

Mohammad Noman

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Examining the psychological constructs for independent learning of undergraduates in sino-foreign universities in China

Abstract

Keywords

Literature review

Independent/autonomous learning

Cognitive autonomy

Self-regulated learning

Meta-cognition

Methodology

Participants and sampling

Data collection

Instruments

Independent learning scale

Cognitive autonomy scale

Self-regulation scale

Metacognition scale

Academic achievement

Data analysis

Results

Main analysis

Discussion

Implications and limitations

Footnotes

Declaration of conflicting interests

Funding

ORCID iDs

References