Is There Relationship Between Motivational Components with Cognitive Strategy and Self-Regulation?

Is There Relationship Between Motivational Components with Cognitive Strategy and Self-Regulation?

Rozi Rifin¹, Kamaru Adzha Kadiran^2*, Mohamad Zhafran Bin Hussin³, Zahari Abu Bakar⁴, Khairul Kamarudin Hasan⁵, Muhammad Azamuddin Aminuddin⁶

^1,2,3,4,5Fakulti Kejuruteraan Electric, university Technology MARA Cawangan Johor Kampus Pasir Gudang, 81750 Masai Johor.

⁶Intel Technology, 11900 Bayan Lepas, Pulau Pinang, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000137

Received: 25 August 2025; Accepted: 30 August 2025; Published: 02 October 2025

ABSTRACT

This study investigates the relationship between motivational components and the use of cognitive strategies and self-regulation among 117 participants, predominantly young, male students in Science & Technology disciplines. Utilizing a quantitative approach with a 5-point Likert-scale survey adapted from Pintrich and De Groot (1990), the research explores learners’ perceptions of self-efficacy, intrinsic value, test anxiety, cognitive strategy use, and self-regulation. Findings reveal strong positive correlations between motivational components and both cognitive strategy use (r = .707, p = .000) and self-regulation (r = .583, p = .000), indicating that higher motivation enhances strategic learning behaviours. Moderate test anxiety and variability in self-regulation suggest areas for improvement. The results align with Zimmerman’s (2008) Self-Regulated Learning Theory, reinforcing the role of motivation in driving cognitive and regulatory processes. Pedagogical implications include fostering self-efficacy and addressing anxiety, while future research should explore longitudinal effects and diverse populations.

Keywords: Self-Regulated Learning, Motivational Components, Cognitive Strategies, Self-Efficacy, Intrinsic Value, Test Anxiety, Educational Psychology.

INTRODUCTION

Background of Study

In modern education, particularly at the tertiary level, students are increasingly required to take ownership of their learning. This shift toward learner autonomy has amplified interest in Self-Regulated Learning (SRL), a concept that describes the ability of individuals to actively plan, monitor, and control their cognitive, motivational, and behavioural processes to achieve academic goals (Zimmerman, 2008). SRL has been widely acknowledged as a key factor contributing to student success, especially in challenging academic environments where independent learning is essential. Central to SRL are three interconnected components: motivational components, cognitive strategies, and self-regulation strategies. Motivational components refer to the internal drivers that initiate and sustain learning behaviour. These include self-efficacy (belief in one’s ability to succeed), intrinsic value (the personal importance and enjoyment of the task), and test anxiety (emotional discomfort related to academic assessments) (Bandura, 2022; Wigfield & Eccles, 2020; Elliot & McGregor, 2021). These affect not only how much effort students invest but also how they react to setbacks and stress. Cognitive strategies, on the other hand, are the techniques used to process and retain information—such as summarizing, elaborating, or organizing content (Pintrich & De Groot, 1990). These strategies help transform motivation into effective learning. Meanwhile, self-regulation strategies involve planning, goal setting, time management, self-monitoring, and reflecting on performance to guide learning behaviours over time (Zimmerman, 2008).

Previous studies have consistently found that students with higher levels of self-efficacy and intrinsic motivation are more likely to apply effective cognitive and regulatory strategies (Yew et al., 2023; Talib et al., 2023). However, test anxiety often interferes with students’ ability to self-regulate and may reduce their academic performance. Furthermore, the effectiveness of SRL may vary depending on the learners’ context and discipline. In particular, students in science and technology fields face high academic demands and cognitive loads that may affect how they apply SRL strategies. Despite the theoretical importance of motivation in SRL, limited research has been conducted on how these components interact in specific academic domains such as engineering and applied sciences in Malaysia. This study aims to explore the relationship between motivational components and students’ use of cognitive and self-regulation strategies. Insights from this research can support the development of targeted interventions to foster strategic, motivated, and self-directed learners in higher education.

Statement of Problem

A growing body of empirical research has affirmed the positive association between motivation and self-regulated learning. For instance, Yew et al. (2023) reported that motivational beliefs such as self-efficacy and intrinsic task value significantly predicted the use of cognitive and metacognitive strategies among Malaysian undergraduates. Similarly, Talib et al. (2023) found that test anxiety negatively impacted SRL strategy adoption, while self-efficacy and intrinsic motivation were positively linked to students’ academic persistence and strategic behaviour. Despite these findings, nuanced issues remain, particularly concerning the differential impact of specific motivational components and their interactions with SRL strategies across varied educational contexts.

One of the critical challenges identified in current literature is the variability in how students apply cognitive strategies and regulate their learning in response to internal motivational states. For example, while some students may exhibit high self-efficacy and utilize deep processing strategies, others may rely on surface-level approaches even when they report similar motivational levels (Manganelli et al., 2021). Furthermore, moderate to high levels of test anxiety, as observed in several studies including the present one, continue to undermine students’ capacity for effective self-regulation (Schweder, 2025). This underscores a practical and theoretical need to better understand the mediating and moderating roles of emotional states within the motivation-SRL dynamic.

These inconsistencies point toward a significant issue: the mere presence of motivation does not guarantee optimal strategic engagement or self-regulation. As noted by Dignath and Veenman (2021), many interventions aimed at enhancing SRL fail to produce lasting effects because they do not sufficiently account for the underlying motivational drivers. This reveals a conceptual gap in integrating motivational regulation within instructional models and learner development frameworks. It is not merely the presence of motivational beliefs that matters, but how these beliefs are activated, sustained, and translated into observable learning behaviours.

Another dimension of the problem lies in the relative under-exploration of SRL within specific academic disciplines. While the theoretical constructs of SRL and motivation have been tested broadly, there is a lack of focused studies on learners in Science and Technology domains, especially within Malaysian public universities. This population often faces rigorous academic demands and may exhibit distinct motivational profiles and cognitive-behavioural responses compared to students in other fields (Rahmat et al., 2021). The present study, by targeting this demographic, seeks to address this contextual gap.

A review of recent literature reveals calls for further research into the longitudinal and domain-specific effects of motivational components on SRL. For instance, Dignath and Veenman (2021) suggest the need for studies that investigate how direct and indirect interventions influence SRL behaviours over time. Similarly, Schweder (2025) emphasizes the importance of examining emotion regulation—particularly test anxiety—as a mediating factor in the motivation-strategy link. These recommendations form the basis of the current research gap: while strong theoretical models exist, there is insufficient empirical work that applies these frameworks to specific learner populations using validated, multidimensional tools.

In response, this study aims to investigate the relationship between motivational components—namely self-efficacy, intrinsic value, and test anxiety—and learners’ use of cognitive strategies and self-regulation. By doing so, it directly responds to the scholarly call for empirical validation of SRL frameworks within specific educational contexts and addresses the need to understand the motivational underpinnings of strategic academic behaviour. These issues ultimately shape the study’s research objectives and research questions, which explore how learners perceive their motivation, cognitive strategy use, and self-regulation, and whether significant relationships exist among these variables.

Objective of the Study and Research Questions

The primary objective of this study is to investigate the relationship between motivational components and students’ use of cognitive and self-regulation strategies within the context of higher education. Specifically, the study aims to explore how learners perceive their levels of self-efficacy, intrinsic value, and test anxiety in relation to their engagement with cognitive strategies and self-regulated learning behaviours. It also seeks to examine how learners evaluate their use of cognitive strategies—such as organizing, elaborating, and reviewing—and their application of self-regulation strategies such as goal setting, time management, and self-monitoring.

To guide the investigation, this study addresses the following research questions: (1) How do learners perceive their motivational components in learning? (2) How do learners perceive their cognitive strategy use in learning? (3) How do learners perceive their self-regulation use in learning? and (4) Is there a relationship between motivation and categories of self-regulated learning strategies? By answering these questions, the study aims to identify the extent to which motivation influences learners’ strategic behaviour. The findings are expected to inform teaching practices, guide curriculum enhancements, and support the development of effective interventions that foster self-directed learning, particularly among students in science and technology programs.

LITERATURE REVIEW

Theoretical Framework of the Study

Self-Regulated Learning Theory

Zimmerman’s (2008) Self-Regulated Learning (SRL) Theory posits that learners actively manage their own learning processes through a cyclical, self-directed approach to achieve academic goals. The theory emphasizes a three-phase process—forethought, performance, and self-reflection—where learners set goals, apply strategies, and evaluate their progress, integrating cognitive, metacognitive, motivational, and behavioural elements to adapt and improve learning outcomes.

Zimmerman’s Self-Regulated Learning (SRL) Theory outlines a cyclical process with three main phases: the Forethought Phase, where learners set goals, plan strategies, and activate motivational beliefs like self-efficacy and task value; the Performance Phase, involving the application of cognitive strategies (e.g., rehearsal, elaboration), metacognitive monitoring, and behavioural actions such as time management; and the Self-Reflection Phase, which includes self-evaluation, attributing outcomes to specific factors, and adjusting strategies for future learning, fostering continuous improvement.

Motivation to Learn

The listed theories highlight diverse motivational drivers for learning, encompassing intrinsic (e.g., autonomy, interest) and extrinsic (e.g., grades, competence) factors. Ryan and Deci (2020) emphasize psychological needs for sustained engagement, while Wigfield and Eccles (2020) focus on the balance of expectancy and value in shaping effort. Elliot and McGregor (2021) differentiate goal orientations, noting mastery goals enhance deep learning, and Bandura (2022) underscores self-efficacy as a foundation for persistence. Together, these constructs suggest that motivation to learn is multifaceted, influenced by personal beliefs, goals, and environmental support, aligning with self-regulated learning frameworks (e.g., Zimmerman, 2008).

Past Studies

Past Studies on Self-Regulated Learning Strategies and Motivation

Yew et al. (2023) conducted a study to explore the relationship between motivational beliefs (self-efficacy, task value, and intrinsic belief) and self-regulated learning (SRL) strategies, specifically cognitive and metacognitive strategies, among 102 Malaysian undergraduates in the Faculty of Business and Management. Using a quantitative survey based on Pintrich and De Groot’s framework, the researchers administered a 44-item questionnaire (22 items on motivational beliefs, 22 on SRL strategies) with a 5-point Likert scale. The findings revealed strong positive correlations between motivational beliefs and the use of cognitive and metacognitive strategies, with motivational beliefs significantly predicting students’ strategic engagement. The study suggests that fostering self-efficacy and task value can enhance autonomous and strategic learning behaviours, offering practical implications for educators aiming to promote effective SRL practices.

Talib et al. (2023) investigated the relationships between three motivational variable which are self-efficacy, intrinsic motivation, and test anxiety with the use of self-regulated learning (SRL) strategies among 102 undergraduates from the Faculty of Business and Management at a Malaysian public university. Employing a quantitative correlational design based on Pintrich and De Groot’s (1990) instruments, the study used a 44-item online survey with sections on demographics, motivational scales, and SRL strategy items, rated on a 5-point Likert scale. Results showed a strong positive correlation between motivational beliefs and SRL strategies (r = .669, p < .001), with self-efficacy and intrinsic motivation positively linked to strategic behaviours. Conversely, test anxiety was found to potentially hinder SRL strategy use. The study recommends reducing test anxiety and enhancing self-efficacy and intrinsic motivation to improve students’ self-regulated learning practices.

Yew et al. (2023) highlight how motivational beliefs predict strategic engagement, aligning with the topic’s focus on motivation’s role in cognitive strategy use and self-regulation. Talib et al. (2023) further reinforce this by demonstrating that self-efficacy and intrinsic motivation enhance SRL strategies, while test anxiety hinders them, emphasizing the interplay of motivational factors in self-regulated learning. Together, these studies confirm that motivational components drive cognitive strategy application and self-regulatory processes, supporting the theoretical framework of SRL (Zimmerman, 2008) and its practical implications for educational interventions.

Conceptual Framework of the Study

Figure 1 shows the conceptual framework of the study. This study explores the relationship between motivational beliefs and self-regulated learning strategies. In order to succeed, learners need to have motivation and use their self-regulated learning strategies. Learners need confidence to stay motivated (Rahmat, et.al., 2021). According to Pintrich & De Groot (1990), learners depend on motivational beliefs such as self-efficacy, intrinsic value and test anxiety. According to Zimmerman (2008), self-regulated learners succeed in their learning because they set goals, they monitor progress, and then they modify their strategies and goals to achieve the planned goals.

In addition to that Pintrich & De Groot (1990) also add that successful learners use self-regulated learning strategies such as cognitive strategy use and self-regulation. This study thus investigates the relationship between motivational components and cognitive strategy use. It also looks at the relationship between motivational components and self-regulation.

Figure 1- Conceptual Framework of the Study Relationship between Motivational Components with  Cognitive Strategy and Self-Regulation

METHODOLOGY

This quantitative study is done to explore motivation and self-regulated strategies. A convenient sample of 117 participants responded to the survey. The instrument used is a 5 Likert-scale survey Table 1 below shows the categories used for the Likert scale; 1 is for Never, 2 is for Rarely, 3 is for Sometimes 4 is for Very Often and 5 is for Always.

Table 1- Likert Scale Use

Table 2- Distribution of Items in the Survey

Table 2 shows the distribution of items in the survey. The instrument is replicated from Pintrich & DeGroot (1990) to reveal the variables in table 3 below. Table 2 also shows the reliability of the survey. The analysis shows a Cronbach alpha of 0.901 for motivational components, 0.915 for Cognitive Strategy Use and 0.787 for Self-Regulation. The overall Cronbach alpha for all 43 items is 0.942; thus, revealing a good reliability of the instrument chosen (Jackson, 2015). Further analysis using SPSS is done to present findings to answer the research questions for this study.

The high Cronbach Alpha values (0.787–0.942) indicate excellent internal consistency across all scales, suggesting the survey reliably measures motivational components and self-regulated learning strategies. The slight dip in Self-Regulation (0.787) compared to Cognitive Strategy Use (0.915) may reflect greater variability in self-regulatory behaviors, while the strong overall reliability (0.942) supports the instrument’s robustness for assessing the interplay of motivation and self-regulation, aligning with the study’s focus on their relationship.

FINDINGS

Demographic Analysis

Table 3- Percentage for Demographic Profile

Table 3 presents the demographic profile of participants, detailing percentages across various categories. The gender distribution shows 77% male and 23% female participants. Discipline is predominantly Science & Technology (89%), with 11% in Social Sciences. Age is mostly below 30 years old (95%), with only 5% over 30. Current occupation indicates 27% are working and studying, while 73% are only studying. Educational background reveals 68% with a high school level and 32% holding a Bachelor, Master, or PhD degree.

Descriptive Statistics  

Findings for Motivational Components

This section presents data to answer research question 1- How do learners perceive their motivational components in learning? In the context of this study, this is measured by self-efficacy, (ii)intrinsic value and (iii)test anxiety.

Figure 2- Mean for Self-Efficacy

Figure 2 presents mean scores and standard deviations (SD) for nine items (MBSEQ1 to MBSEQ9) assessing self-efficacy beliefs among students. Mean scores range from 3.4 to 3.9 on a presumed 5-point scale, indicating moderate to high confidence, with the highest means for “I expect to do very well in this class” (3.9) and “I am certain I can understand the ideas taught in this course” (3.8), and the lowest for “Compared with other students in this class I think I know a great deal about the subject” (3.4). Standard deviations range from 0.7 to 0.9, suggesting relatively consistent responses across participants.

The data shows moderate to high self-efficacy (means 3.4–3.9), with students expressing the strongest confidence in expecting good grades and understanding course material (means 3.8–3.9). The slight variability (SD 0.7–0.9) suggests consistent self-belief, but lower means (e.g., 3.4 for subject knowledge) indicate potential insecurity in some areas, suggesting a need for targeted confidence-building interventions.

Figure 3- Mean for Intrinsic Value

Figure 3 displays mean scores and standard deviations (SD) for nine items assessing intrinsic value among students. Mean scores range from 3.8 to 4.0 on a presumed 5-point scale, reflecting high perceived value and interest, with the highest means for “It is important for me to learn what is being taught in this class” (4.0) and “Even when I do poorly on a test I try to learn from my mistakes” (4.0), and a consistent 4.0 for items MBIVQ7, MBIVQ8, and MBIVQ9. Standard deviations range from 0.7 to 0.8, indicating relatively uniform responses across participants.

The results indicate high intrinsic value (means 3.8–4.0), with students valuing learning importance and usefulness (means 4.0) and showing interest in challenging work. Consistent SDs (0.7–0.8) reflect uniform engagement, but the slight dip at 3.8 suggests some variability in enjoying challenging tasks, pointing to opportunities to enhance intrinsic motivation through diverse learning experiences.

Figure 4- Mean for Test Anxiety

Figure 4 shows the mean for test anxiety. Two items share the highest mean of 3.2. Firstly, is item 3 (mean -3.2, SD=1.1) that states that the students were worried about the tests. Next is item 4 (mean=3.2, SD=1.1) which reports that the students worry about how poorly they perform for the test. Next, item 1 (mean=3.1, SD=1.1) states that the students were so nervous during the test that they could not remember facts they had learnt. Finally, item 2 (mean=3, SD=1.1) reports that the students reported having unset feeling when they were taking the test.

The data suggests a moderate level of test anxiety among participants, with means around 3.0–3.2 indicating frequent concern about performance and memory during tests. The consistent SD of 1.1 reflects similar variability across items, implying that while anxiety is prevalent, individual experiences differ moderately. This highlights the need to address test anxiety to enhance self-regulation and cognitive strategy use, aligning with the study’s focus on motivational components and learning outcomes.

This section presents data to answer research question 2- How do learners perceive their cognitive strategy use in learning?

Cognitive Strategy Use (13 items)

Figure 5- Mean for Cognitive Strategy Use

Figure 5 presents mean scores and standard deviations (SD) for 12 items assessing cognitive strategy use among students. Mean scores range from 3.7 to 4.1 on a presumed 5-point scale, indicating moderate to high strategy use, with the highest mean for “When studying, I copy my notes over to help me remember material” (4.1) and the lowest for “I outline the chapters in my book to help me study” (3.7). Standard deviations range from 0.8 to 0.9, suggesting consistent responses across participants.

The data reveals moderate to high strategy use (means 3.7–4.1), with copying notes (4.1) and connecting concepts (4.0) being the most common, while outlining chapters (3.7) is less frequent. SDs (0.8–0.9) show reasonable consistency, but the lower mean for outlining suggests students may underutilize advanced strategies, indicating a potential area for instructional focus to deepen learning approaches.

This section presents data to answer research question 3- How do learners perceive their self-regulation use in learning? Self-Regulation (9 items)

Figure 6- Mean for Self-Regulation

Figure 6 displays mean scores and standard deviations (SD) for nine items assessing self-regulation among students, with means ranging from 3.3 to 3.9 on a presumed 5-point scale. The highest mean (3.9) is for “I work hard to get a good grade even when I don’t like a class,” indicating strong effort despite disinterest, while the lowest (3.3) is for “When work is hard I either give up or study only the easy parts” and “I find that when the teacher is talking, I think of other things and don’t really listen to what is being said,” suggesting challenges with persistence and focus. SDs range from 0.7 to 1.0, reflecting moderate consistency in responses. The data suggests a generally positive self-regulation tendency, with students showing resilience such as working hard for grades but struggling with focus and persistence under difficulty, which may indicate areas for targeted educational support.

Exploratory Statistics

Findings for Relationship between motivation and categories of self-regulated learning strategies This section presents data to answer research question 4- Is there a relationship between motivation and categories of self-regulated learning strategies?

To determine if there is a significant association in the mean scores between motivation and categories of self-regulated learning strategies, data is analysed using SPSS for correlations. Results are presented separately in table 5 and 6 below.

Table 5- Correlation between Motivational Components and Cognitive Strategy

**Correlation is significant at the 0.01 level (2-tailed)

Table 5 shows there is an association between motivational components and cognitive strategy use. Correlation analysis shows that there is a high significant association between motivational components and cognitive strategy use (r=.707**) and (p=.000). According to Jackson (2015), coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is also a strong positive relationship between motivational components and cognitive strategy use.

Table 6-Correlation between Motivational Components and Self-Regulation

**Correlation is significant at the 0.01 level (2-tailed)

Table 6 shows there is an association between motivational components and self-regulation. Correlation analysis shows that there is a high significant association between motivational components and self-regulation (r=.583**) and (p=.000). According to Jackson (2015), coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is also a strong positive relationship between motivational components and self-regulation.

CONCLUSION

5.1 Summary of Findings and Discussions

This study explored learners’ motivation and their use of self-regulated learning (SRL) strategies among 117 participants, utilizing a 5-point Likert-scale survey adapted from Pintrich and De Groot (1990). The research questions (RQs) and findings are summarized below, with connections to past studies.

Based on RQ1, participants exhibited moderate to high perceptions of motivational components, with strong self-efficacy (e.g., confidence in doing well in class) and intrinsic value (e.g., valuing learning importance), though test anxiety was moderately prevalent (e.g., worry about tests). This aligns with Ryan and Deci (2020), who highlight autonomy and competence as key motivators, and Wigfield and Eccles (2020), who note expectancy and value as drivers of motivation, suggesting these components shape learners’ engagement.

Learners demonstrated moderate to high use of cognitive strategies, favoring methods like copying notes and connecting concepts, though advanced strategies like outlining were less common. This is consistent with Manganelli et al. (2021), who found motivational regulation predicts cognitive strategy use, indicating that learners’ strategy preferences may reflect motivational influences. This answered on RQ2.

Self-regulation was generally positive, with strong effort toward grades despite disinterest, but challenges persisted with focus and persistence under difficulty. This supports Schweder (2025), who noted motivational regulation enhances metacognitive monitoring, suggesting that variability in self-regulation may stem from motivational factors which related to RQ3.

Correlation analyses revealed strong positive relationships between motivational components and both cognitive strategy use (r = .707, p = .000) and self-regulation (r = .583, p = .000), indicating that higher motivation enhances SRL strategy application. This finding echoes Dignath and Veenman (2023), whose meta-analysis showed combined motivational and cognitive interventions boost SRL, and Talib et al. (2023), who reported a significant correlation (r = .669) between motivation and SRL strategies, reinforcing the interplay of these constructs.

The results align with Zimmerman’s (2008) SRL theory, which emphasizes the cyclical integration of motivation, cognitive strategies, and self-regulation. Past studies like Yew et al. (2023) and Manganelli et al. (2021) similarly found motivational beliefs predict SRL strategy use, while Dignath and Veenman (2023) and Schweder (2025) underscore the mediating role of motivation in enhancing SRL outcomes. The moderate test anxiety observed mirrors Talib et al. (2023), highlighting its potential to hinder SRL, suggesting a need for anxiety-reduction strategies. Collectively, these findings validate the study’s focus on motivation’s relationship with cognitive and self-regulatory strategies, consistent with recent SRL research.

Based on the research findings, the overall conclusion is that there is a strong positive relationship between motivational components and both cognitive strategy use and self-regulation among the 117 participants, supporting the study’s objective to explore these interconnections. Learners exhibited moderate to high levels of self-efficacy and intrinsic value, enhancing their engagement with cognitive strategies such as copying notes, connecting concepts, and self-regulatory efforts such as working hard for grades, though challenges with persistence, focus, and test anxiety suggest areas for improvement. The significant correlations (r = .707 for cognitive strategy use, r = .583 for self-regulation, both p = .000) confirm that higher motivation drives more effective SRL strategies, aligning with Zimmerman’s (2008) SRL theory and recent studies by Dignath & Veenman, 2023 and Manganelli et al., 2021. This underscores the importance of fostering motivational beliefs, particularly self-efficacy and intrinsic value, while addressing test anxiety to optimize self-regulated learning outcomes in educational settings.

5.2 Implications and Suggestions for Future Research.

5.2.1    Theoretical and Conceptual Implications.

The findings align with Zimmerman’s (2008) Self-Regulated Learning (SRL) Theory, which frames learning as a cyclical process involving forethought, performance, and self-reflection phases, integrating motivational beliefs, cognitive strategies, and self-regulation. The study’s evidence of strong correlations between motivational components (e.g., self-efficacy, intrinsic value) and cognitive strategy use (r = .707) and self-regulation (r = .583) supports the theory’s emphasis on motivation as a driver in the forethought phase, activating strategic planning and persistence. The moderate test anxiety and variability in self-regulation (e.g., lower means for focus and persistence) reflect challenges in the performance and self-reflection phases, suggesting a need to refine the theory by incorporating emotional regulation as a mediating factor, as hinted by Schweder (2025). Conceptually, the results reinforce the interplay of expectancy-value (Wigfield & Eccles, 2020) and self-efficacy (Bandura, 2022) in shaping SRL, indicating that motivational constructs are central to adaptive learning behaviours, consistent with the study’s focus on their relationship

5.2.2    Pedagogical Implications.

The findings suggest that teaching should prioritize enhancing motivational beliefs to boost SRL strategies. Educators can integrate activities that strengthen self-efficacy such as mastery experiences, positive feedback, and intrinsic value as example relevant, challenging tasks, to encourage cognitive strategy use like elaboration and organization. Given the moderate test anxiety, teaching should include anxiety-reduction techniques, such as mindfulness or practice tests, to improve focus and memory during assessments. The lower use of advanced strategies such as outlining, and challenges with persistence indicate a need for explicit instruction in metacognitive skills and scaffolding to support self-regulation, especially for students balancing work and study. Tailored support for the predominantly young, male, Science & Technology-focused cohort could involve tech-based learning tools to sustain engagement and address discipline-specific needs.

5.2.3    Suggestions for Future Research.

Future researchers should explore longitudinal designs to assess how motivational components and SRL strategies evolve over time, addressing the current study’s cross-sectional limitation. Investigating diverse populations (e.g., beyond the 95% under-30, 89% Science & Technology sample) could enhance generalizability, particularly across genders and disciplines. The role of emotion regulation, especially test anxiety, warrants further study as a mediator in the motivation-SRL relationship, potentially using experimental interventions. Additionally, examining the impact of pedagogical interventions (e.g., metacognitive training) on underrepresented strategies like outlining could provide insights into improving self-regulation across educational contexts.

ACKNOWLEDGEMENT

The author would like to acknowledge Professor Dr. Noor Hanim Rahmat for her comprehensive support throughout the research process, which included providing invaluable guidance in conceptualizing research ideas, developing the hypothesis, designing the experimental methodology, constructing the research model, coordinating data collection, and assisting with data analysis.

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