Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9, 731-741 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 17 June 2025; Revised: 11 August 2025; Accepted: 14 August 2025; Published: 12 September 2025 * Correspondence: lork@lincoln.edu.my The influence of curriculum design on university students’ academic performance in Cambodia: Exploring the mediating role of self-regulation Chumneanh Lork1*, Dhakir Abbas Ali2 1,2School of Business and Management, Lincoln University College, Malaysia; lork@lincoln.edu.my (C.L.) drdhakir@lincoln.edu.my (D.A.A.) Abstract: This study investigates the influence of curriculum design on university students’ academic performance in Cambodian higher education, emphasizing the mediating role of self-regulation. A quantitative research design was adopted, collecting data from 320 lecturers across public and private universities. Using SmartPLS (PLS-SEM), the measurement model demonstrated strong reliability (Cronbach’s α > 0.90) and validity (AVE > 0.60). The structural model explained 15.6% of the variance in self-regulation and 11.8% in students’ performance, with Q² values above zero, confirming predictive relevance. Curriculum design did not significantly affect students’ performance directly (β = 0.099, t = 1.655, p = 0.099) but had a strong positive effect on self-regulation (β = 0.405, t = 8.580, p = 0.000). Self-regulation significantly enhanced students’ performance (β = 0.296, t = 5.420, p = 0.000) and mediated the relationship between curriculum design and students’ performance (β = 0.120, t = 4.370, p = 0.000). The results suggest that curriculum design indirectly improves academic outcomes by fostering self-regulatory skills. These findings highlight the need for learner-centered, goal-oriented curricula that actively cultivate self-regulation, empowering students to manage their learning processes and achieve better academic performance. Keywords: Academic performance, Cambodian universities, Curriculum design, Mediating effect, Self-regulation. 1. Introduction In Cambodia’s evolving higher education landscape, curriculum design plays a crucial role in shaping students’ academic success. As universities expand and enrollment grows post-pandemic, it is increasingly clear that the quality of education depends not only on institutional growth but also on how well curricula are structured to meet students’ learning needs. Effective curriculum design integrates clear learning objectives, relevant content, appropriate teaching method, and assessment strategies that align with real-world competencies. This cultivates a dynamic and nurturing learning atmosphere that enhances student motivation and facilitates skill growth. Research highlights that thoughtfully designed curriculum can enhance student engagement and academic achievement by providing coherent and meaningful learning experiences [1]. Furthermore, curriculum design influences students’ ability to self-regulate their learning—setting goals, monitoring progress, and adjusting strategies—which is essential for academic success in higher education [2, 3]. Despite efforts by Cambodian institutions to improve educational quality through better infrastructure and qualified faculty, there remains limited research on how curriculum design specifically impacts student performance in Cambodia. Understanding this relationship, especially the role of self-regulation as a mediator, is vital for informing policy and practice to strengthen higher education outcomes in the country. Curriculum design plays a crucial role in shaping the quality of higher education, particularly in the evolving learning environments influenced by technological advancements and global trends [4]. In the https://orcid.org/0009-0007-9158-1953 https://orcid.org/0009-0000-6842-0157 732 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate context of Cambodia’s higher education, recent shifts towards blended learning—combining traditional face-to-face instruction with online components—have highlighted the importance of well-structured curricula that support diverse learning modes and student needs. Effective curriculum design integrates clear learning objectives, relevant content, varied teaching methods, and appropriate assessments to foster deeper understanding and engagement [5]. It also accommodates flexibility, allowing students to access materials asynchronously while benefiting from interactive, collaborative activities both online and in-class. This adaptability is vital in a post-pandemic era where institutions strive to enhance student performance despite challenges such as limited resources and digital divides. Moreover, a thoughtfully designed curriculum supports the development of critical thinking and self-regulated learning, essential skills for student success [6]. In Cambodia, efforts to improve curriculum design coincide with broader educational reforms aimed at raising academic standards and addressing disparities in access and quality [7]. By aligning curriculum development with emerging pedagogical practices and technological integration, Cambodian higher education institutions can better prepare students for a competitive, globalized world. Overall, prioritizing curriculum design that is responsive to changing learning environments and student diversity is key to advancing educational outcomes and ensuring sustainable improvements in student performance. This study aims to investigate the effect of curriculum design on the academic achievement of higher education students in Cambodia. Additionally, the study aims to explore the mediating role of students’ self-regulation abilities in this relationship, investigating how students’ capacity to manage their own learning influences the effectiveness of curriculum design. 2. Literature Review Transactional distance theory highlights how both physical and psychological gaps between learners and instructors can affect learning effectiveness, suggesting that well-structured curricula help reduce this distance and improve engagement [8]. Similarly, self-determination theory emphasizes that fostering autonomy, competence, and relatedness within a curriculum enhances students’ intrinsic motivation and performance [9, 10]. Research supports that thoughtful curriculum design—aligning learning objectives, assessment, and teaching method—positively influences academic success. For instance [11, 12] demonstrated that constructive alignment promotes better student achievement. Additionally, integrating formative assessments and timely feedback further supports learning progress. Effective course designs, which blend traditional and digital approaches, enhance student engagement and satisfaction, crucial for performance in today’s education landscape. These findings underscore that curriculum design is fundamental in shaping students’ learning outcomes and overall academic success. Zimmerman’s Social Cognitive Model of Self-Regulation highlights the interaction between personal factors, environmental influences, and behavior in learning. It emphasizes that self-regulation involves goal setting, self-monitoring, evaluation, and reinforcement, all of which can be supported through effective curriculum design [13]. A well-structured curriculum provides clear objectives, scaffolded activities, and opportunities for reflection and feedback, fostering students’ ability to regulate their own learning. Bandura’s Social Cognitive Theory also underscores the importance of observational learning and self-efficacy, suggesting that curricula incorporating modeling and self-regulatory strategies can enhance student motivation and achievement [14]. Empirical evidence supports this, as interventions that embed self-regulated learning techniques within curricula have been shown to improve students’ self-regulation skills and academic performance. These findings reinforce that curriculum design plays a crucial role in promoting self-regulation, which is key to effective learning and academic success. Social Cognitive Theory (SCT) highlights the interaction between personal factors, environment, and behavior, emphasizing self-regulation through processes like self-monitoring, evaluation, and reinforcement. Similarly, Self-Determination Theory (SDT) identifies autonomy, competence, and relatedness as essential psychological needs that drive motivation and promote self-regulated behavior [15]. In higher education, self-regulation involves setting goals, monitoring progress, and adapting 733 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate strategies to achieve academic success. Empirical research supports this connection; for example [16] found that self-regulatory behaviors positively predicted college students’ academic performance. Furthermore, a meta-analysis of more than 100 studies [17] confirmed a robust association between self-regulation and academic performance across various educational settings. These results highlight the essential importance of self-regulation in promoting academic success among higher education students. Curriculum design plays a crucial role in shaping higher education students' performance by influencing their ability to self-regulate learning. Students learn through observation and modeling within their learning environment, where curriculum structure, teaching method, and technology use significantly affect their engagement and motivation [12, 18]. Self-regulation enables students to set goals, monitor progress, and adapt strategies, fostering autonomy and persistence despite challenges. Research shows that well-designed curricula that promote autonomy and support self-regulatory behaviors enhance academic achievement [16]. Specifically, when curricula provide clear goals, varied learning activities, and meaningful feedback, students are better equipped to manage their learning processes effectively. Thus, curriculum design not only impacts cognitive engagement but also cultivates students’ self-regulatory skills, which are essential for sustained academic success in higher education settings. 2.1. Hypotheses and Research Framework H1: Curriculum design has a significantly influence on students’ performance of higher education in Cambodia. H2: Curriculum design has a significantly influence on self-regulation in Cambodian higher education institutions. H3: Self-regulation has a significantly influence on students’ performance of higher education in Cambodia. H4: Self-regulation significantly mediates the relationship between curriculum design and students’ performance of higher education in Cambodia. Figure 1. Research Framework 3. Methodology 3.1. Sampling and Data Collection A research design serves as a structured and methodical plan that guides the entire research process, ensuring consistency between the research questions, theoretical foundation, hypotheses (if any), and the chosen methodology. It acts as a blueprint that directs how data will be collected, measured, analyzed, and interpreted, thereby upholding the study's validity, reliability, and ethical standards [19]. For this study, a descriptive research design using quantitative methods was deemed most appropriate. In alignment with [20] the preference for quantitative over qualitative methods supports a more objective and measurable approach. The target population consists of lecturers from selected public universities in Cambodia, chosen based on their relevance and accessibility. Based on the 734 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate sample size guidelines proposed by Krejcie and Morgan [21] the appropriate number of participants for a population of 2,000 was adhered to in this study. A structured questionnaire was designed using previously validated items aligned with the study’s main constructs. A pilot study was undertaken to evaluate the instrument’s reliability, with Cronbach’s alpha values ranging from 0.708 to 0.911, all exceeding the generally accepted minimum threshold of 0.70 [22]. After finalizing the instrument, printed questionnaires were distributed to academic staff in selected public and private universities. A total of 405 questionnaires were handed out, and 347 were successfully completed and returned, yielding an initial response rate of 85.7%. After excluding 27 incomplete responses, 320 valid surveys remained, resulting in a final usable response rate of 79% in Table 1, which is considered acceptable for quantitative research. Table 1. The demographic characteristics of the respondents. Factors Classification Repetition Proportion Gender Female 34 10.6 Male 286 89.4 Age Below 30yrs 18 5.6 31-40yrs 52 16.3 41-50yrs 162 50.6 51-60yrs 81 25.3 61yrs and above 7 2.2 Academic Qualification MSc. 271 84.7 PhD 49 15.3 Working Experience Below 5yrs 28 8.8 6 – 10yrs 47 14.7 11 – 15yrs 166 51.9 16 – 20yrs 68 21.3 Above 20yrs 11 3.4 N 320 3.2. Measurement A five-point Likert scale, spanning from 1 (strongly disagree) to 5 (strongly agree), was applied to measure the constructs explored in the study. The questionnaire was organized into six sections. Items addressing curriculum design were created to evaluate the technological context, based on adaptations of existing measurement tools. The section on self-regulation included items modified from established frameworks, while student performance was assessed using four key dimensions drawn from prior models and research. 3.3. Data Analysis SmartPLS software was utilized in the present study to evaluate the proposed research framework, as it is a widely adopted tool for quantitative data analysis. Specifically, SmartPLS was utilized to evaluate the structural model, allowing for the analysis of both the model’s predictive capability and the interrelationships among the constructs [23, 24]. In this study, SmartPLS 3.0 was employed to estimate both the measurement model (external model), which involved evaluating constructs’ consistency and strength, and the structural model (internal model), which assessed the hypothesized relationships between latent variables. 4. Result and Discussion 4.1. Measurement Model Evaluation Table 2, the reliability and validity of the constructs were confirmed using Cronbach’s alpha, composite reliability (CR), AVE, and discriminant validity, following [23, 24]. All constructs 735 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate demonstrated strong internal consistency (α and CR > 0.90) and convergent validity (AVE > 0.66). Items with loadings between 0.7 and 0.9 were kept in the model. Table 2. Construct Reliability and Validity. Construct Items Loadings Cronbach Alpha Composite Reliability Average Variance Extracted Curriculum Design CD1 0.826 0.943 0.951 0.661 CD10 0.779 CD2 0.854 CD3 0.776 CD4 0.832 CD5 0.857 CD6 0.776 CD7 0.835 CD8 0.808 CD9 0.778 Students’ Performance SP1 0.885 0.931 0.946 0.745 SP10 0.904 SP11 0.851 SP12 0.907 SP14 0.908 SP15 0.908 SP16 0.801 SP17 0.892 SP2 0.742 SP3 0.712 SP4 0.732 SP5 0.714 SP6 0.739 SP7 0.891 SP8 0.905 SP9 0.826 Self-Regulation SR1 0.916 0.971 0.974 0.699 SR2 0.902 SR3 0.821 SR4 0.790 SR5 0.820 SR6 0.920 Table 3 shows that discriminant validity was confirmed using the Fornell–Larcker criterion, demonstrating that each construct is empirically distinct. The square root of the AVE for each construct curriculum design (0.813), self-regulation (0.863), and students' performance (0.836) exceeded its correlations with other constructs, meeting the threshold proposed by Fornell and Larcker [25]. These results support the discriminant validity and integrity of the measurement model [23, 24]. 736 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate Table 3. Latent Variable Correlations (Fornel-Larcker Criterion). Constructs CD SR SP Curriculum Design (CD) 0.813 Self-Regulation (SR) 0.395 0.863 Students' Performance (SP) 0.214 0.332 0.836 Table 4, discriminant validity was further supported using the Heterotrait-Monotrait Ratio (HTMT), with all values below the 0.90 threshold [26]. Specifically, SR–CD (0.411), SP–CD (0.218), and SP–SR (0.348) indicate clear distinction among constructs, confirming strong discriminant validity in the measurement model. Table 4. Discriminant Validity (Heterotrait-Monotrait Ratio - HTMT). Constructs CD SR SP Curriculum Design (CD) Self-Regulation (SR) 0.411 Students' Performance (SP) 0.218 0.348 4.2. Structural Model Evaluation After validating the measurement model, the R² values were examined to determine how well the endogenous constructs are explained by the exogenous variables. Higher R² values reflect greater explanatory power. As noted by Chin [27] R² values above 0.67 are considered substantial, values between 0.33 and 0.67 indicate a moderate level, and values ranging from 0.19 to 0.33 suggest a weak and R² values below 0.19 are undesirable. Table 5 presents the structural model indicators. The model accounts for 15.6% of the variance in self-regulation and 11.8% of the variance in students' performance, as indicated by the R² values. These values suggest the model has a very weak explanatory power, which is accepted. The adjusted R² values (0.154 and 0.113, respectively) confirm the robustness of these results while adjusting for the number of predictors in the model. Table 5. Coefficient of Determination (R Square). Constructs R-square R-square adjusted Self-Regulation 0.156 0.154 Students' Performance 0.118 0.113 Additionally, the f² effect sizes were computed to assess the impact of each exogenous variable on the R² value of the endogenous constructs. By convention, f² values of 0.02, 0.15, and 0.35 represent small, medium, and large effects, respectively. The (f²) effect size analysis reveals that self-regulation have a small impact on students’ performance (f² = 0.082), while curriculum design has only a no effect (f² = 0.009) on students’ performance. Moreover, curriculum design exerts a moderate effect on self- regulation (f² = 0.185) in Table 6. Table 6. Effect Sizes (f2) Analaysis. Students’Peformance Effect Size Decisions Curriculum Design 0.009 None Self-Regulation 0.082 Small Sefl-Regulation Effect Size Decisions Curriculum Design 0.185 Moderate 737 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate Furthermore, Q² values were derived using the blindfolding procedure to evaluate the model’s predictive relevance; values greater than zero suggest that the model has sufficient predictive accuracy [23, 24]. The Q² values for the endogenous constructs indicate that the model has predictive relevance. Specifically, the Q² for students' performance is 0.081, reflecting a medium level of predictive relevance. The Q² for self-regulation is 0.114, suggesting a moderate to strong predictive power. Since both values exceed the threshold of Zero, it can be concluded that the model exhibits acceptable predictive relevance for these constructs in Table 7. Table 7. Construct Cross Validated Redundancy (Q2). Constructs SSE SSO 1-SSE/SSO Self-Regulation 1,920.000 1,701.066 0.114 Students' Performance 5,120.000 4,705.170 0.081 Note: SSO - Systematic Sources of Output; SSE - Systematic Sources of Error. Thus, SRMR values for both the saturated model and the estimated model are both 0.079 below the recommended threshold of 0.10 it can be concluded that the model used in this study has a good fit [23, 24]. Table 8 presents an overview of the structural model’s indicators. Table 8. Goodness of Fit of The Model. Item Saturated Model Estimated Model SRMR 0.079 0.079 d_ULS 3.257 3.257 d_G 10.698 10.698 Chi-Square 9094.518 9094.518 NFI 0.504 0.504 738 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate 4.3. Hypothesis Testing Figure 2. Path Model Significant. Figure 3. Path Model Results of Mediation. 739 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate This study explored the relationships among curriculum design, self-regulation, and students' academic performance in Cambodian higher education. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings confirmed all four hypotheses (H1–H4), demonstrating that curriculum design exerts both direct and indirect effects on students’ academic outcomes. The findings reveal a positive but statistically non-significant effect of curriculum design on students’ academic performance (β = 0.099, t = 1.655, p = 0.099), indicating that H1 is not supported. This suggests that curriculum design alone may not directly enhance students’ performance in Cambodian higher education. This result contrasts with studies by Van Zyl, et al. [28] and Tashi [29] who reported significant positive impacts of curriculum design on student engagement and educational outcomes. The inconsistency may stem from contextual differences or the mediating influence of other variables, such as self-regulation, which warrants further exploration. The findings confirm that curriculum design has a positively significant influence on self-regulation among higher education students in Cambodia (β = 0.405, t = 8.580, p = 0.000), thus supporting H2. It aligns with the work of Brosens [30] who emphasized that thoughtfully designed, project-based curricula can effectively promote self-regulating soft skills, particularly in collaborative and applied learning contexts. These findings suggest that curriculum elements—such as autonomy, clarity of learning outcomes, and active learning strategies—serve as powerful enablers of self-regulated learning behaviors in the Cambodian higher education setting. The results confirm that self-regulation has a statistically significant influence on students’ performance of higher education in Cambodia (β = 0.296, t = 5.420, p = 0.000), thus supporting H3. This finding aligns with Elesio [31] who emphasized that self-regulated learning strategies significantly enhance academic outcomes among college students. The result underscores the crucial role of self-regulatory behaviors—such as goal setting, time management, and self-monitoring—in fostering academic success. The findings indicate that self-regulation significantly mediates the relationship between curriculum design and students’ academic performance (β = 0.120, t = 4.370, p = 0.000), thereby supporting H4. This result highlights that well-designed curriculum enhances students’ performance indirectly by fostering students’ self-regulatory capabilities. It is consistent with Park and Kim [32] who emphasized the pivotal role of self-regulation in linking instructional design to improved engagement and academic outcomes in higher education. Table 9. Direct and Indirect Effect Hypotheses Testing. Hypothesis Coef. Se T value P values Decision Curriculum Design -> Students' Performance 0.099 0.059 1.655 0.099 Not Supported Curriculum Design -> Self-Regulation 0.405 0.046 8.580 0.000 Supported Self-Regulation -> Students' Performance 0.296 0.054 5.420 0.000 Supported Curriculum Design -> Self-Regulation -> Students' Performance 0.120 0.027 4.370 0.000 Supported Note: Coef. = Coefficient; Se = standard error. 5. Conclusion This study explored the relationship between curriculum design and student academic performance in Cambodian higher education, with self-regulation as a mediating factor. The measurement model demonstrated strong reliability and validity (CA > 0.70, CR > 0.90, AVE > 0.60), and discriminant validity was established via the Fornell–Larcker criterion and HTMT values below 0.90. The structural model showed moderate explanatory power for student learning outcomes (R² values), and Q² values above zero confirmed predictive relevance. Effect size analysis indicated that self-regulation had a small impact on student performance (f² = 0.082), curriculum design had no direct effect on performance (f² = 0.009), but a moderate effect on self-regulation (f² = 0.185). The Goodness-of-Fit (GoF) score further supported model adequacy. In hypothesis testing, H1 was not supported, as curriculum design did not 740 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 731-741, 2025 DOI: 10.55214/2576-8484.v9i9.9952 © 2025 by the authors; licensee Learning Gate significantly affect performance directly (β = 0.099, t = 1.655, p = 0.099). H2 was supported, showing a strong effect of curriculum design on self-regulation (β = 0.405, t = 8.580, p = 0.000), and H3 confirmed that self-regulation significantly enhanced performance (β = 0.296, t = 5.420, p = 0.000). H4 validated the mediating role of self-regulation between curriculum design and academic performance (β = 0.120, t = 4.370, p = 0.000). These findings suggest that while curriculum design does not directly influence academic outcomes, it enhances them indirectly by promoting self-regulation—highlighting the need for learner-centered curricula that foster autonomy and strategic learning. This study faces several limitations, including its cross-sectional design, which restricts causal conclusions, and the use of self-reported data, which may introduce bias. The focus on Cambodian higher education limits generalizability, and other potential mediators such as motivation, engagement, or emotional regulation were not explored. 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