Student-centered curriculum management predicts self-regulated learning among Generation Z students in Islamic higher education in Sumatra, Indonesia

Student-centered curriculum management predicts self-regulated learning among Generation Z students in Islamic higher education in Sumatra, Indonesia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Student-centered curriculum management predicts self-regulated learning among Generation Z students in Islamic higher education in Sumatra, Indonesia Eka Putra, Wasehudin Wasehudin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8840560/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study examines factors influencing students’ self-regulation using Partial Least Squares–Structural Equation Modeling (PLS-SEM) within the Self-Regulated Learning (SRL) framework, which conceptualizes self-regulation as a cyclical process comprising forethought, performance, and self-reflection. The proposed model integrates Instructional Support and Faculty Facilitation (ISFF), Curriculum Relevance and Flexibility (RFC), and Student Participation and Voice (SVP), with Gender tested as a moderating variable. Data were collected through an online survey of 508 students from Islamic universities across Sumatra, Indonesia. PLS-SEM was employed to assess both measurement and structural models. The findings indicate strong explanatory power, with R² values exceeding 0.70 for all endogenous constructs. ISFF emerged as the most robust and consistent predictor across all three SRL phases, underscoring the critical role of instructional support and lecturer facilitation in students’ learning planning, execution, and reflection. RFC showed a positive effect mainly on the forethought phase, with limited influence on performance and self-reflection. SVP negatively affected forethought but positively and significantly influenced performance and self-reflection, suggesting that student participation operates differently across SRL phases. Gender had no direct effect on self-regulation; however, it moderated the relationships between ISFF and SVP, as well as between ISFF and self-regulation. Theoretically, this study reinforces the contextual and phase-specific nature of self-regulation. Practically, the findings highlight the importance of adaptive instructional design, prioritizing instructional support while aligning student participation with specific stages of self-regulated learning. Self-regulated learning instructional support student participation curriculum relevance gender moderation PLS-SEM Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Changes in the landscape of higher education inevitably require student-centered curriculum management to ensure that graduates are competent in conducting independent and adaptive learning (Kaur & Pahuja, 2019 ). This change in the landscape of higher education is not without reason: it occurs when the focus of education, which was teacher-centered, shifts toward a student-centered learning process. Of course, this change is supported by curriculum management, which places students at the center of the learning process—from planning and implementation to curriculum evaluation. This kind of management fosters harmony among students’ needs, characteristics, and learning experiences, ensuring alignment with learning objectives, teaching strategies, and assessment systems. From this kind of focus, it seems very obvious that the position of students is not functioning and interpreted only as passive recipients of knowledge, but is positioned as active learners who have space to choose, participate, and reflect on their learning process (Mundia, 2022 ; Qureshi & Ullah, 2014 ). Weimer ( 2002 ) further explained that student-centered curriculum management has several advantages—distinctions that make this concept stronger than others, including shifting learning from lecturers to students by emphasizing learning responsibility, active engagement, and high-level skill development. At the same time, Biggs ( 2011 ) emphasizes that learning objectives, learning activities, and assessments should ideally be designed in harmony to achieve meaningful learning outcomes. In this context, student-centered curriculum management is not only about classroom teaching methods but also encompasses curriculum policies, syllabus design, evaluation approaches, and monitoring mechanisms that fully support active and independent learning. (Shi et al., 2025 ). In this context, student-centered curriculum management plays a key role. This is because curriculum management that is aligned among learning objectives, learning activities, and assessment systems can provide autonomy, encourage active engagement, and deliver consistent feedback, which are essential for the development of self-regulated learning. (Zimmerman, 2016 ). At the same time, this affirms, from a social-cognitive perspective, that self-regulated learning does not develop separately as a concept. However, one example of a curriculum design product that allows students to set goals, apply learning strategies, and reflect. As a result, student-centered curriculum management can be seen as a strategic institutional mechanism in fostering students’ self-regulation skills. Ideally, student-centered curriculum management as a theory seems necessary to test in a relevant social context, and in this case, among Gen-Z students. Why Gen-Z students should be used as the basis for empirical testing of this theory seems to rest on an underlying argument: the uniqueness of Gen-Z students is not only a reality but also a theory. Eskasasnanda et al. ( 2025 ) In this case, the author elaborates on the Gen-Z character, explaining that this generation was born between the mid-1990s and the early 2010s. In relation to universities, Gen-Z students have strong abilities to access and process information quickly, multitask, and use digital technology as a cognitive extension in learning activities. In the Indonesian context, Gen-Z students are more likely to have a shorter, more immediate understanding span; they are distracted by digital entertainment and have difficulty maintaining long-term focus and information retention (Eskasasnanda et al., 2025 ). This character certainly affects the learning process of Gen-Z students, and it is often judged by its functional outcomes—by completing an assignment—rather than by its depth. The above conditions certainly confirm an undeniable reality: Gen-Z students have a fairly high dependence on gadgets and social media. This kind of dependence certainly has implications for their study quality and motivation, disrupts time management, and even leads to a tendency to postpone academic assignments. In addition, although Gen Z students are generally used to interacting online, many show limitations in face-to-face communication, in expressing opinions, and in active involvement in class discussions. They are often overcome with anxiety about social judgment and are reluctant to express learning difficulties to lecturers. Of course, this can hinder the academic and personal development of Gen-Z students. This identification is based entirely on the study of Eskasasnanda et al. ( 2025 ). Moreover, it affirms that Gen-Z students need not only cognitive support but also psychological and emotional support, as well as a safe, conducive, and participatory learning environment. In this context, a curriculum design that is better adapted to the needs of Gen-Z students is needed: one that focuses on meaningful technology integration and provides quick, constructive feedback. Of course, it also requires strengthening autonomy and self-reflection. Therefore, a comprehensive understanding of Gen Z’s learning characteristics is needed to provide an important foundation for designing student-centered curriculum management that supports the optimal development of self-regulated learning. There is extensive literature showing that a student-centered learning approach can support self-regulated learning (SLR), as evidenced by studies by Alharbi et al. ( 2012 ), Anastasia and Abidin ( 2024 ), and Zimmerman ( 2016 ). This means that studies on this topic are growing rapidly and are both in demand and considered important to continue. However, specific studies examining the role of curriculum management policy and practices at the university level among Gen-Z students in Islamic universities in Sumatra remain very limited; even now, they have not been addressed comprehensively. It is also worth highlighting that existing studies published at the local level—Indonesian national journals, still tend to focus on teaching or course interventions, and do not touch on the more macro-aspects of curriculum management planning, implementation, monitoring, and evaluation (García-Cano Torrico et al., 2024 ; Horlin et al., 2024 ; Watanabe & Saito, 2024 ). In response to these limitations, this study and article are expected to present a new model that estimates complex variables involving curriculum management, Gen-Z characteristics, and their influence on SRL. However, this study was conducted in the context of Islamic universities in Sumatra—a domain that remains underexplored in the literature. Thus, the study is expected to contribute by developing the theory of self-regulated learning by treating student-centered curriculum management as an exogenous variable, which is suspected to affect SLR among Gen-Z students at State Islamic Religious Universities on the island of Sumatra, Indonesia. 2. Literature Review 2.1. Student-Centered Curriculum Management and Self-Regulated Learning: An Elaborated-Concept The development of 21st-century education requires a paradigm shift—from a teacher-oriented approach to learning to one that puts students at the center of the learning process. This shift concerns not only learning strategies in the classroom but also a more structural aspect: how the curriculum is designed, managed, and implemented systematically. In this context, curriculum management based on student-centered learning (SCL) is an important foundation for encouraging learners to become independent, reflective, and able to manage their own learning process, a process known as self-regulated learning (SRL). Student-centered learning emphasizes students’ active role in building knowledge through meaningful, collaborative, and contextual learning experiences. This approach views students not as passive recipients of information but as subjects capable of planning, monitoring, and evaluating their own learning process. Several studies have shown that implementing student-centered learning positively contributes to intrinsic motivation, deeper conceptual understanding, and the development of critical thinking and problem-solving skills (Hu et al., 2025 ; Pei & Wu, 2019). Nevertheless, the effectiveness of this approach depends largely on how the curriculum is managed thoroughly and consistently. This is understandable because curriculum management includes a series of interrelated processes, ranging from planning and organizing to implementing and evaluating the curriculum. From the perspective of educational management, the curriculum is understood not only as an academic document but also as a strategic tool for achieving learning goals and developing students’ competencies. Neliwati et al. (2023) emphasized that effective curriculum management can improve the quality of learning and academic achievement of students through systematic planning, adaptive implementation, and continuous evaluation. When the student-centered principle is applied at every stage of curriculum management, the space for student learning independence will be wider. One relevant approach in this context is the competence-based curriculum, which focuses on developing real competencies through active and reflective learning, as studied by Hu et al. ( 2025 ) show that competency-based curriculum reform, with an interactive learning model and blended learning, can increase student participation, satisfaction with learning, and independent learning ability. This model implicitly encourages self-regulated learning through integrated pre-class, in-class, and post-class stages. Thus, SRL is not only a learning goal but also an internal mechanism that is strengthened by curriculum design and management. Self-regulated learning refers to students’ ability to set learning goals, choose appropriate strategies, monitor progress, and reflect on learning outcomes independently. In a student-centered learning environment, SRL acts as a bridge between curriculum policy and individual learning experiences. Without a flexible, student-oriented curriculum, SRL development tends to be sporadic and relies solely on personal initiative. On the other hand, curriculum management that is consciously designed to support learning autonomy will sustainably strengthen students’ self-regulation. Furthermore, the integration of technology and blended learning also strengthens the relationship between student-centered curriculum management and SRL. The digital learning environment provides opportunities for students to access learning resources independently, set their own learning pace, and interact reflectively with materials and lecturers. However, as noted in various studies, technology will only be effective if it is supported by a clear curriculum design, measurable learning objectives, and an evaluation system that encourages self-reflection (Ma et al., 2022 ; Yang & Yang, 2023 ). Based on this description, it can be concluded that student-centered curriculum management and self-regulated learning are interrelated and mutually reinforcing. Student-oriented curriculum management provides structure, resources, and learning experiences that support the development of self-regulation. On the contrary, students’ self-regulated learning ability is an indicator of the success of implementing a student-centered curriculum. Therefore, conceptual elaboration of the relationship between these two concepts is important as a theoretical basis for developing curriculum policies and learning practices in higher education. 3. Self-Determination Theory: An Overview Self-Determination Theory (SDT) is one of the most comprehensive motivational frameworks in modern psychology, providing an understanding of why and how humans act in various social contexts. In contrast to classical motivational theories, which tend to judge motivation by its quantity—how strong a person’s motivation is—SDT emphasizes the quality of motivation. It is these qualities that determine whether a person acts autonomously or is under external control. With this perspective, SDT develops not just as a theory of motivation but as a metatheory encompassing six mini-theories that explain human development, social functioning, and psychological health. As a metatheory, SDT departs from the organismal assumption that humans have a natural drive toward growth, self-integration, and active involvement. However, this push does not work automatically; It can be hampered when the social environment is oppressive or does not support basic psychological needs. Therefore, SDT examines in depth how social contexts—in education, work, health, and interpersonal relationships—can facilitate or even inhibit healthy motivation. The essence of SDT is three basic psychological needs: autonomy, competence, and relatedness (Dunn & Zimmer, 2020 ). Autonomy refers to the need to feel that actions come from oneself and are not forced. Competence refers to the sense of effectiveness in mastering one’s environment and achieving goals. Relatedness describes the need to feel emotional closeness and meaningful connections with others. SDT introduces an assumption that these three needs are universal—not culturally specific—with a consistent function in maintaining motivation and psychological well-being (Legault, 2017 ). Within the framework of SDT, motivation is divided into two broad categories: autonomous motivation and controlled motivation. Autonomic motivation includes intrinsic motivation and deeply internalized forms of extrinsic motivation, such as identified and integrated regulation. A person acts out of a personal value or interest. In contrast, controlled motivation arises when behavior is triggered by external or internal pressures, such as punishment, rewards, guilt, or demands to maintain self-esteem. SDT shows that autonomic motivation leads to sustained performance, better mental health, and higher life satisfaction, whereas controlled motivation is associated with stress, burnout, and lower long-term performance. In this regard, Organismic Integration Theory (OIT) explains that extrinsic motivation is not singular, but rather on the internalized continuum—a person can act entirely due to external factors (external regulation), partly internal (introjected regulation), or entirely due to integrated regulation (integrated regulation). In this context, the ILO strongly opposes the traditional view that extrinsic motivation is inferior; in reality, the quality of extrinsic regulation depends heavily on support for autonomy and competence. In addition, Cognitive Evaluation Theory (CET) can be used; in this case, it focuses on the factors that affect intrinsic motivation. CET shows that rewards, feedback, or evaluations can increase or decrease intrinsic motivation depending on whether the stimulus is perceived as a control or as information. This phenomenon is contrary to the classic behavioristic assumption that rewards always increase motivation. In relation to SDT, it is understandable that controlling rewards can undermine a sense of autonomy and reduce one’s intrinsic interest in certain activities (So, 1971). In addition, Causality Orientation Theory (COT) explains the differences in an individual’s disposition to respond to the environment: autonomous orientation (acting on personal values), controlled orientation (acting on pressure), and impersonal orientation (feeling powerless). This orientation is formed from long-term experiences in social contexts that support or suppress psychological needs. Overall, SDT provides a critical and humanistic picture of human motivation: that optimal development occurs not through external control, but through an environment that values psychological freedom, provides appropriate challenges, and establishes supportive relationships. SDT is not just a theory of motivation but also a guide to building a social context that drives human growth, resilience, and well-being. In relation to Self-Regulated Learning (SRL), Self-Determination Theory provides a motivational foundation that explains why and how students can independently, sustainably, and reflectively manage their learning process. SRL requires individuals to set goals, monitor progress, manage learning strategies, and evaluate learning outcomes consciously—and this whole process is highly dependent on the quality of motivation. When autonomy needs are met, learners are more likely to set meaningful learning goals and choose strategies that align with personal values; Fulfillment of competence strengthens self-confidence to monitor and adjust learning strategies effectively; While relatedness creates a sense of psychological security that supports perseverance and engagement in learning. In this framework, autonomous motivation as explained by SDT—especially through the internalization of learning values (identified and integrated regulation)—is an important prerequisite for the development of adaptive SRL. In contrast, controlled motivation tends to result in shallow, reactive, and dependent learning. Thus, SDT not only complements SRL as a supportive motivational theory but also deepens understanding of how self-regulation in learning is rooted in the socio-pedagogical context that supports students’ basic psychological needs. 4. Framework: Initial Model and Hypothesis Formulation This research framework is based on the understanding that student-oriented curriculum management (student-centered curriculum management) plays an important role in improving Generation Z students’ self-regulation skills, especially at Islamic religious universities in the Sumatra region. The student-centered approach to the curriculum emphasizes flexibility, relevance, active student participation, and instructional support that facilitate an independent and reflective learning process (Weimer, 2002 ). Student-oriented curriculum management in this study is operationalized through three main dimensions, namely: (1) curriculum relevance and flexibility, (2) student participation and voice, and (3) instructional support and lecturer facilitation. Relevance and flexibility reflect the extent to which the curriculum can adapt to students’ needs and the demands of scientific development. Student participation and voice describe student involvement in the decision-making process and learning activities (Bovill, 2020 ). Meanwhile, instructional support includes the role of lecturers in guiding, motivating, and providing a learning environment that encourages independence (Zimmerman, 2016 ). These three dimensions are assumed to contribute to the development of students’ self-regulated learning abilities. Self-regulated learning is understood as the ability of students to plan, monitor, control, and evaluate their learning process independently (Zimmerman et al., 2025 ). In the context of digital generation learning, this ability is crucial because Generation Z students tend to want independent, flexible, and technology-based learning (Seemiller & Grace, 2017 ). Based on this conceptual framework, this study formulates a main hypothesis that assesses the relationship between student-oriented curriculum management and self-regulated learning as an overall construct (Fig. 1 ). In addition, several sub-hypotheses were formulated that tested the influence of each dimension—curriculum relevance and flexibility, student participation and voice, and instructional support and lecturer facilitation—on self-regulated learning . Using this hypothetical model, the research aims to provide an empirical basis for the extent to which a student-centered curriculum approach can improve students’ ability to manage self-directed learning (SLR). The research findings are expected to contribute to the development of curriculum, learning strategies, and education policies that are more responsive to the characteristics and learning needs of Generation Z. The full study hypothesis is presented in Table 1 . Table 1 Research Hypotheses Code Variable Relationship Hypothesis Statement H1 Curriculum Relevance and Flexibility → Forethought Phase Curriculum relevance and flexibility have a positive and significant effect on students’ forethought phase. H2 Curriculum Relevance and Flexibility → Performance Phase Curriculum relevance and flexibility have a positive and significant effect on students’ performance. H3 Curriculum Relevance and Flexibility → Self-Reflection Phase Curriculum relevance and flexibility have a positive and significant effect on students’ self-reflection phase. H4 Student Participation and Voice → Forethought Phase Student participation and voice have a positive and significant effect on students’ forethought phase. H5 Student Participation and Voice → Performance Phase Student participation and voice have a positive and significant effect on students’ performance. H6 Student Participation and Voice → Self-Reflection Phase Student participation and voice have a positive and significant effect on students’ self-reflection phase. H7 Instructional Support and Faculty Facilitation → Forethought Phase Instructional support and faculty facilitation have a positive and significant effect on students’ forethought phase. H8 Instructional Support and Faculty Facilitation → Performance Phase Instructional support and faculty facilitation have a positive and significant effect on students’ performance. H9 Instructional Support and Faculty Facilitation → Self-Reflection Phase Instructional support and faculty facilitation have a positive and significant effect on students’ self-reflection phase. H10 Forethought Phase → Q1 (Learning Outcome) The forethought phase has a positive and significant effect on learning outcomes (Q1). H11 Performance Phase → Q1 (Learning Outcome) The performance phase has a positive and significant effect on learning outcomes (Q1). H12 Self-Reflection Phase → Q1 (Learning Outcome) The self-reflection phase has a positive and significant effect on learning outcomes (Q1). 5. Methods 5.1. Research Design This study uses a quantitative approach with a Partial Least Squares–Structural Equation Modeling (PLS-SEM) design. The use of PLS-SEM was chosen because the research model involves complex relationships among latent variables, relatively large sample sizes, and potentially unevenly distributed data. According to Hair et al. (2021), PLS-SEM is a suitable method for both predictive and exploratory research. It is ideal when the objective is to maximize the explanatory variance (R²) of endogenous constructs. This research model comprises three main constructs: Student-Centered Curriculum Management (SCCM), Self-Regulated Learning (SRL), and Learning Engagement . The research aims to test the causal relationships among the three constructs, based on the formulated hypothesis. 5.2. Sample and Data Collection The research population includes students who are studying at Islamic Religious Universities in the Sumatra region. Sampling technique using volunteer sampling , with a Google Form-based online distribution system for active students in the current academic year. The total number of responses collected is 508, which is considered adequate for PLS-SEM analysis, as suggested by Hair et al. ( 2019 ), which states that the large sample size increases the stability of the model estimation. Data were collected online via a Google Form questionnaire. The questionnaire was distributed through academic networks, social media, and student groups at Islamic universities in Sumatra, Indonesia. The use of online questionnaires was chosen to expand the reach of respondents and simplify the data collection process (Creswell, 2013 ). In addition, the data collection period runs from October 1, 2025, to January 1, 2026. 5.3. Research Instruments The research instrument consists of three main parts that measure the constructs of SCCM, SRL, and Learning Engagement. Each construct is measured using several items based on the Likert scale of 1–5 (1 = strongly disagree, 5 = strongly agree), in accordance with the common practice of measuring latent variables in social research (Meng et al., 2014 ). The instrument was developed based on literature studies and adapted to the context of Islamic Universities. The content’s validity is assessed through a validation test before the questionnaire is distributed online. All instrument items are listed in the Google Form, which serves as a data collection tool. 5.4. Data Analysis Procedure Data analysis was carried out in two main stages, following the PLS-SEM approach: First, Measurement Model Analysis (Outer Model) to assess the construct’s validity and reliability. Second , Structural Model Analysis (Inner Model) to test the relationship between latent variables, as well as hypothesis testing. The analysis is carried out using the latest version of SmartPLS, as recommended by Hair et al. ( 2019 ) for PLS-SEM analysis. 5.5. Measurement Model Assessments (Outer Model) Evaluation of the measurement model in the PLS-SEM approach is conducted to ensure that the indicators used represent the latent constructs validly and reliably. This process includes three main components, namely convergent validity, discriminant validity, and construct reliability. First, convergent validity is assessed using outer loadings and the Average Variance Extracted (AVE). An indicator is considered to have an adequate contribution if its outer loading value exceeds 0.708. Meanwhile, an AVE of at least 0.50 indicates that the construct accounts for at least 50% of the variance in its indicators (Hair et al., 2018 ). Thus, a construct is considered to be valid in a convergent manner if both criteria are met. Second, the validity of the discriminator is evaluated to ensure that each construct truly distinguishes itself from the others. The assessment was carried out using two approaches. First, the Fornell–Larcker criterion, which is to compare the square root of AVE with the correlation between constructs. The validity of the discriminant is fulfilled when the root of AVE is higher than the correlation of the other constructs. Second, the Heterotrait–Monotrait Ratio (HTMT) is a modern, more sensitive approach for detecting discriminant validity issues. An HTMT value below 0.85 indicates that the constructs are completely different (Henseler & Sarstedt, 2013 ). Third, a reliability test is carried out to assess the internal consistency of the indicator in measuring the same construct. The two measures used are Cronbach’s Alpha and Composite Reliability (CR). The values of both must be between 0.70 and 0.95 to be reliable. The range illustrates a balance between adequate internal consistency and avoiding indicator redundancy. Overall, the measurement model is deemed eligible if all validity and reliability parameters fall within the tolerance limits recommended in the literature. If all these requirements are met, the construct is ready to be used in the evaluation stage of the structural or inner model. 5.6. Structural Model Assessment (Inner Model) Once the measurement model is declared valid and reliable, the next step in the PLS-SEM approach is to evaluate the structural model (inner model). This evaluation assesses the strength of relationships among latent constructs and the model’s ability to explain the variance of the endogenous constructs. The evaluation process includes several main stages: collinearity check, determination coefficient (R²), effect size (f²), predictive relevance (Q²), and interpretation of path coefficients. The first stage is Collinearity Assessment, which is conducted to ensure that relationships among predictor variables are not multicollinear. The collinearity test was performed using the Variance Inflation Factor (VIF), with a threshold of 5, indicating that the model is free of collinearity that could interfere with parameter estimation (Hair et al., 2018 ). The second stage is the examination of the Coefficient of Determination (R²), which indicates how much variance in the endogenous construct is explained by its predictors. The R² value reflects the strength of the model in explaining the phenomenon being studied. According to Hair et al. (2021), the R² value can be categorized into three levels: substantial (≥ 0.75), moderate (≥ 0.50), and weak (≥ 0.25). A high R² value indicates that the model has strong explanatory abilities. Next, Effect Size (f²) is used to assess the magnitude of each predictor variable’s influence on the endogenous constructs. The value of f² allows the researcher to understand the unique contribution of each predictor in the model. A higher value of f² indicates that the variable has a greater impact on the variance of the endogenous constructs. The fourth stage is Predictive Relevance (Q²), calculated using the blindfolding technique. A Q² value greater than 0 indicates that the model has adequate predictive capabilities against endogenous constructs. (Hair et al., 2019 ). Thus, the Q² value is an important indicator of whether the model is not only theoretically sound but also empirically relevant. The final stage is interpreting Path Coefficients, which describe the strength and direction of the relationships between latent variables in the model. The path coefficient serves as the basis for testing the research hypothesis. A larger coefficient value indicates a stronger relationship, while a positive or negative sign indicates the direction of influence between constructs. Testing the significance of this coefficient is typically done using bootstrapping in PLS-SEM. Overall, the evaluation of the structural model provides a comprehensive overview of the model’s predictive quality, the strength of relationships between latent variables, and the contribution of each construct in explaining the phenomenon under study. If all parameters are within the recommended limits, the model is considered suitable for further interpretation during hypothesis testing. 5.7. Hypothesis Testing Hypothesis testing in this study was conducted using a bootstrapping procedure with 5,000 subsamples, as recommended by Hair et al. (2021), to ensure accurate parameter estimation despite the relatively small sample size. This study involved 568 students from various Islamic Universities in Sumatra, which is an adequate sample size for PLS-SEM analysis, as this method does not require a normal distribution and can accommodate medium sample sizes. The bootstrapping technique is used to obtain t-statistics and p-values, which are the basis for determining whether a research hypothesis is accepted or rejected. A latent construct relationship is declared significant when the t-statistic value reaches or exceeds 1.96 at the significance level of α = 0.05, and the p-value is at or below 0.05. This criterion indicates that the relationship between latent variables is statistically significant and not random. Through this procedure, hypothesis testing shows whether the relationship between constructs in the research model is significant. If the relationship is significant, it means that empirical data from 568 respondents support the influence between constructs; Conversely, the insignificant relationship suggests that the effects between variables do not have sufficient statistical support. Thus, testing hypotheses through bootstrapping allows researchers to assess the validity of causal relationships in structural models and to provide an empirical basis for the theoretical and practical interpretation of the research. 6. Results 6.1. Data Description and Respondent Characteristics Figure 2 presents the distribution of respondents by college of origin, showing variation in the number of respondents across institutions. Descriptively, this distribution shows the concentration of respondents across several universities, with IAIN Curup having the highest number of respondents. Visually, the dominance of IAIN Curup respondents is evident in the taller bar relative to other universities, indicating a significant contribution to the overall research sample. This pattern reflects the active involvement of the institution’s academic community in the research conducted, which can be related to institutional proximity, academic networks, or the suitability of research topics to the local academic context and needs. Statistically, this distribution reflects a pattern of respondent concentration, the main characteristic of the study sample’s composition. At IAIN Curup, Fatmawati Sukarno Islamic University (UINFAS), and UIN Mahmud Yunus Batusangkar are in the second group, with relatively high numbers of respondents, but are quantitatively quite far from the top institutions. This group forms a secondary cluster that acts as a distribution balancer, but it is not enough to reduce the dominance of the main institution. Furthermore, there is a medium group filled by UIN Raden Fatah, IAIN Kerinci, and UIN Sjech M. Djamil Djambek. The frequency of respondents in this group declined gradually. Statistically, this condition follows an exponentially decreasing distribution: the further to the right on the category axis, the fewer the respondents. This group serves as a transition between institutions that make large contributions and those that make very small contributions. The last part of the graph shows the long-tail distribution, marked by the number of universities with very low response rates, such as UIN Raden Intan Lampung, UIN Al-Azhar Lubuklinggau, and UIN Sultan Syarif Kasim. Statistically, this group makes a minor contribution to the total number of respondents, but it is still important to maintain institutional diversity in the research. Descriptively, this graph illustrates the empirical reality of respondents’ participation and shows the academic centers most actively involved in the research. Overall, the distribution of respondents in this graph shows a pattern of high concentration in one major institution, a gradual decline in medium-sized institutions, and a long tail of institutions with low participation, which are common characteristics in academic and region-specific network-based research. Figure 3 , a pie chart, illustrates the distribution of respondents by gender in this study. Overall, the majority of respondents were female (75.6%), while male respondents accounted for 24.4%. This composition shows that participation in the study is higher among women. The distribution pattern reflects respondents’ involvement, which aligns with the characteristics of participation in academic survey-based research, especially in higher education or a specific field of study. The dominance of female respondents indicates a significant contribution from a female perspective to the research data, which is integral to the overall findings. Methodologically, this distribution can represent the empirical conditions of the field and the level of respondent involvement, based on demographic characteristics relevant to the research context. Thus, this graph provides an overview of the respondents’ gender composition, which is the basis for interpreting the research results as a whole. 6.2. Evaluation of Measurement Models (Outer Model) The evaluation of the outer model in this study assessed the quality of indicators in representing both exogenous and endogenous latent constructs. The assessment of the outer model focuses on the convergent validity test, as reflected in the outer loading values of each indicator. According to Hair et al. ( 2019 ), the indicator is declared to have good convergent validity if the outer loading value ≥ 0.70, because the value indicates that more than 50% of the indicator’s variance can be explained by the latent construct measured. Table 2 presents the first exogenous variable, Curriculum Relevance and Flexibility (RFC), measured using five indicators (C.1–C.5). The test results showed that all indicators had high outer loadings, ranging from 0.863 to 0.887. This value exceeds the minimum recommended by Hair et al. ( 2019 ), indicating that these indicators strongly and consistently reflect the curriculum’s relevance and flexibility. The C.4 indicator has the highest value (0.887), indicating its dominant contribution to shaping the RFC construct. Table 2 Outer Model RFC Please ISFF FP PP SRP VIF Exogenous variable 1: Curriculum Relevance and Flexibility (RFC) C.1 0.863 2.686 C.2 0.868 2.375 C.3 0.879 2.867 C.4 0.887 3.222 C.5 0.885 3.191 Exogenous variable 2: Student Participation and Voice (SVP) D.1 0.890 3.187 D.2 0.837 2.383 D.3 0.787 2.009 D.4 0.909 3.687 D.5 0.926 4.406 Exogenous variable 3: Instructional Support and Faculty Facilitation (ISFF) E.1 0.894 3.383 E.2 0.913 4.095 E.3 0.919 4.208 E.4 0.913 4.089 E.5 0.890 3.411 Endogenous variable 1: Forethought Phase (FP) G.1 0.911 3.752 G.2 0.897 3.449 G.3 0.905 3.834 G.4 0.913 4.049 G.5 0.898 3.403 Endogenous variable 2: Performance Phase (PP) H.1 0.887 3.031 H.2 0.872 2.822 H.3 0.864 2.644 H.4 0.871 2.757 H.5 0.859 2.600 Endogenous variable 2: Self-Reflection Phase (SRP) I.1 0.848 2.452 I.2 0.904 3.569 I.3 0.891 3.380 I.4 0.933 4.926 I.5 0.845 2.519 Source : Data analysis The second exogenous variable, Student Participation and Voice (SVP), also showed excellent measurement quality. The five indicators used (D.1–D.5) had outer loadings ranging from 0.787 to 0.926. Indicator D.5 recorded the highest score (0.926), which indicates that the student participation and vote aspects of the indicator are the strongest representation of the SVP construct. Although indicator D.3 has the lowest value (0.787), Hair et al. assert that indicators above 0.70 are still worth defending, especially if they support the construct’s content validity. Furthermore, the third exogenous variable, Instructional Support and Faculty Facilitation (ISFF), showed a very strong performance of the outer model. All indicators (E.1–E.5) have high outer loading values, ranging from 0.890 to 0.919. The E.3 indicator with the highest score (0.919) shows that instructional support and lecturer facilitation are key elements in the formation of the ISFF construct. The high outer loading value in all indicators confirms the consistency and reliability of the measurement of this variable, as required in the evaluation of the PLS-SEM measurement model according to Hair et al., ( 2014 ). For the endogenous variables, the Forethought Phase (FP) was measured using five indicators (G.1–G.5), with outer loadings ranging from 0.897 to 0.913. All indicators show a relatively balanced and strong contribution, indicating that the initial planning phase of learning is comprehensively shaped by the indicators used. The Performance Phase (PP) variable has an outer loading of 0.859–0.887. Although the value is slightly lower than that of the other constructs, all indicators still meet the criteria for convergent validity set by Hair et al., so this construct is empirically valid. Finally, the Self-Reflection Phase (SRP) is measured using indicators I.1–I.5, with an outer loading value of 0.845–0.933. Indicator I.4 shows the highest value (0.933), confirming the central role of self-reflection in the formation of the SRP construct. Overall, the results of the outer model evaluation indicate that all constructs in this study meet the convergent validity criteria recommended by Hair et al., so the measurement model is deemed feasible to proceed to the inner model analysis stage. 6.3. Structural Model Evaluation Table 3 presents the coefficient of determination (R²) and adjusted R² for three endogenous constructs: FP, PP, and SRP. In general, the value of R² indicates the proportion of variation in the dependent variable that can be explained by the independent variable in the model, thus reflecting the strength of the structural model’s explanatory power. The analysis showed that the FP construct had an R² of 0.751 and an adjusted R² of 0.748, indicating that about 75.1% of FP’s variance is explained by the model’s predictors. This value is relatively high, indicating that the model has a strong ability to explain FP, with small differences between R² and adjusted R², indicating good model stability. Furthermore, the PP construct shows an R² of 0.704 and an adjusted R² of 0.700, indicating that the exogenous construct accounts for 70.4% of the PP variance. Although the value is slightly lower than FP, it is still considered strong, so the model can be considered effective at explaining the behavior or phenomenon represented by the PP. The small difference between the adjusted R² and R² also indicates that the model is not overfitting. Table 3 Coefficient of Determination (R²) R-square R-square adjusted FP 0.751 0.748 PP 0.704 0.7 SRP 0.749 0.746 Source : Data Analysis Meanwhile, the SRP construct has an R² of 0.749 and an adjusted R² of 0.746, indicating that the independent variables in the model explain 74.9% of the variance in SRP. This value is almost equivalent to FP and reinforces the conclusion that structural models as a whole have strong explainability. Thus, based on the general criteria for PLS-SEM analysis, the entire endogenous construct in this model demonstrates good explanatory quality and supports the model’s feasibility for further hypothesis testing. Table 4 Path Coefficient of Structural Model Predictor FP PP SRP Gender -0.059 -0.061 -0.001 ISFF 0.592 0.348 0.624 RFC 0.281 0.074 0.027 Please -0.073 0.362 0.194 Gender × ISFF -0.163 0.139 -0.134 Gender × SVP 0.429 — 0.212 Gender × RFC -0.099 — — Source : Data Analysis (PLS-SEM) The path coefficients in Table 4 above provide a comprehensive overview of the direct effects of Gender moderation on three endogenous constructs: Forethought Phase (FP), Performance Phase (PP), and Self-Reflection Phase (SRP). Overall, the findings suggest that substantive variables in the model make a stronger contribution than demographic variables. At the same time, Gender plays a more significant role as a moderation variable than as a direct predictor. Directly, Gender showed a very small coefficient and tended to be negative towards FP (β = −0.059), PP (β = −0.061), and SRP (β = −0.001). These values indicate that gender differences do not directly affect the planning, implementation, or self-reflection phases, so Gender is not the main determinant of variation in the three constructs. In contrast, Instructional Support and Faculty Facilitation (ISFF) emerged as the most dominant and consistent predictor. ISFF had a positive and strong effect on FP (β = 0.592), PP (β = 0.348), and SRP (β = 0.624). These findings confirm that instructional support and lecturer facilitation play a crucial role in shaping all phases of student self-regulation, starting from planning, performance, and reflection. The Curriculum Relevance and Flexibility (RFC) variable showed a positive but relatively weak influence on FP (β = 0.281), PP (β = 0.074), and SRP (β = 0.027). This indicates that the curriculum’s relevance and flexibility contribute more to the planning phase than to the performance and reflection phases. Meanwhile, Student Participation and Voice (SVP) showed a different pattern, namely a negative influence on FP (β = −0.073), but a fairly strong positive influence on PP (β = 0.362) and SRP (β = 0.194). These findings show that student participation plays a greater role in the implementation and self-evaluation phases than in the initial planning phase. In terms of moderation, the interaction of Gender × ISFF showed a negative effect on FP (β = −0.163) and SRP (β = −0.134), but a positive effect on PP (β = 0.139), indicating that the effect of ISFF on self-regulation varied by gender. Gender Interaction × SVP showed a strong positive influence on FP (β = 0.429) and SRP (β = 0.212), indicating that the impact of student participation was amplified by gender. In contrast, the Gender × RFC interaction showed only a negative effect on FP (β = −0.099), indicating a limited moderating effect. Overall, these results confirm the complexity of the relationships among variables and the importance of considering gender moderation in structural models. 6.4. Hypothesis Testing The results of the hypothesis test (Fig. 4 ) on the PLS-SEM structural model provide a comprehensive picture of the direct relationships and moderating effects of the exogenous variables across the three main phases of student self-regulation: Forethought Phase (FP), Performance Phase (PP), and Self-Reflection Phase (SRP). Evaluation of the structural model showed that all endogenous constructs had high coefficient of determination values, namely R² = 0.751 for FP, R² = 0.703 for PP, and R² = 0.749 for SRP. These values indicate that the model has strong explanatory power, with more than 70% of the variation in each endogenous construct explained by the predictor variables, making it feasible to use the model for empirical hypothesis testing. The first through third hypothesis tests examine the direct influence of Instructional Support and Faculty Facilitation (ISFF) on FP, PP, and SRP. The analysis showed that ISFF had a strong positive effect on FP (β = 0.592), PP (β = 0.348), and SRP (β = 0.624). These findings support the hypothesis put forward and affirm that instructional support and lecturer facilitation play a central role in shaping all phases of student self-regulation. The strongest influence of ISFF was found in the self-reflection phase, which showed that the quality of academic support strongly determines students’ ability to evaluate and improve their learning strategies. The next hypothesis tests the influence of Curriculum Relevance and Flexibility (RFC) on the three phases of self-regulation. The results showed that RFC had a positive effect on FP (β = 0.281), but its effect on PP (β = 0.074) and SRP (β = 0.027) was relatively weak. Thus, the hypothesis regarding the influence of RFCs on FP is supported, whereas their influence on PP and SRP provides only limited support. These findings indicate that a relevant and flexible curriculum primarily supports students during the planning and learning goal-setting stages, but has less direct impact on the implementation and reflection stages. Furthermore, hypothesis testing regarding Student Participation and Voice (SVP) showed mixed results. SVP negatively affects FP (β = −0.073), so the hypothesis of SVP’s positive effect on FP is not supported. Conversely, SVP had a positive and strong effect on PP (β = 0.362) and SRP (β = 0.194), so the hypothesis of SVP’s influence on both phases is supported. These findings show that active participation and student involvement play a greater role in the implementation phase of learning strategies and in reflecting on learning outcomes than in the initial planning phase. The direct influence of Gender on FP, PP, and SRP was also tested in the model. The results showed that Gender had a very small coefficient and tended to be negative towards FP (β = −0.059), PP (β = −0.061), and SRP (β = −0.001). Therefore, the hypothesis that gender directly influences student self-regulation is not supported. This indicates that gender differences do not directly determine students’ self-regulation abilities in this study. However, the role of Gender becomes significant when it is modeled as a moderating variable. Gender Interactions × ISFF showed a negative effect on FP (β = −0.163) and SRP (β = −0.134), but a positive effect on PP (β = 0.139). These findings suggest that the influence of ISFF on student self-regulation differs by gender, with instructional support being more effective during the performance phase for specific groups. Furthermore, the interaction of Gender × SVP showed a strong positive effect on FP (β = 0.429) and SRP (β = 0.212), supporting the moderation hypothesis. This indicates that student participation and voice have a greater impact on self-regulation when gender is considered. In contrast, the Gender interaction × RFC showed only a negative effect on FP (β = −0.099) and no effect on PP and SRP, which supports this hypothesis only partially. Overall, the hypothesis test results confirm that ISFF is the dominant predictor in the model, followed by SVP and RFC, with different patterns of influence across the phases of self-regulation. In addition, these findings emphasize the importance of including gender-moderation variables to understand the complexity of relationships in student self-regulation models. 7. Discussion The findings of this study make a strong empirical contribution to understanding student self-regulation through an integrated theoretical approach. Using Self-Regulated Learning (SRL) theory as the main framework, and supported by Social Cognitive Theory, Self-Determination Theory, and Gender Schema Theory, the PLS-SEM results show that the determinants of self-regulation operate in a phase-specific, contextual, and moderated manner rather than as direct predictors. The high value of the determination coefficient in the entire endogenous construct (R² > 0.70) indicates that the model has a very strong and theoretically relevant explanatory power (Hair et al., 2019 ). From the perspective of Self-Regulated Learning theory, self-regulation is understood as a cyclical process consisting of phases of forethought, performance, and self-reflection (Zimmerman, 2016 ). The empirical findings of this study directly confirm this conceptual structure. Instructional Support and Faculty Facilitation (ISFF) proved to be the most consistent and dominant predictor across all three phases of self-regulation, with strong effects on FP (β = 0.592), PP (β = 0.348), and SRP (β = 0.624). These results show that students’ self-regulation does not develop independently but is strongly influenced by the quality of instructional support and lecturer facilitation, especially in higher education. These findings are in line with Zimmerman’s ( 2016 ), which emphasizes that environmental factors play an important role in shaping student learning planning, implementation, and reflection. The dominance of ISFF can be better understood through the lens of Social Cognitive Theory, which emphasizes reciprocal determinism among personal, behavioral, and environmental factors (Bandura, 1978 ). In this context, lecturers serve as environmental agents that influence students’ self-confidence, learning strategies, and self-evaluation. The strongest influence of ISFF in the self-reflection phase indicates that academic feedback, reflective facilitation, and lecturer guidance strongly determine students’ ability to evaluate the effectiveness of their learning strategies. These findings support Bandura’s ( 1978 ) argument that appropriate environmental reinforcement can increase individuals’ reflective and adaptive capacity during learning. In contrast to ISFF, Curriculum Relevance and Flexibility (RFC) shows a limited and phase-specific pattern of influence. RFC had a positive effect on the forethought phase (β = 0.281), but its effects on the performance (β = 0.074) and self-reflection (β = 0.027) phases were relatively weak. Theoretically, this suggests that the curriculum functions primarily as an initial structural framework that helps students understand learning objectives, set goals, and build learning readiness. These findings are consistent with SRL’s assumption that structural factors have a greater role at the planning stage than at the implementation and reflection stages. (Zimmerman et al., 2025 ). In other words, a relevant and flexible curriculum is important for facilitating early learning orientation, but it is not enough to encourage sustainable self-regulation without active instructional support. The most theoretically interesting findings appeared in the Student Participation and Voice (SVP) variable. SVP showed a negative effect on the forethought phase (β = −0.073), but a strong positive effect on the performance (β = 0.362) and self-reflection (β = 0.194) phases. This pattern can be explained well by Self-Determination Theory (SDT). According to Deci and Ryan ( 2000 )The need for autonomy and competence is best met when individuals are actively involved in learning. In the forethought phase, students still need structure, direction, and clear goals, so participating too early can actually reduce focus on planning. On the other hand, in the implementation and reflection phases, active involvement and student voices increase intrinsic motivation, a sense of ownership of the learning process, and the quality of self-evaluation. These findings show that the effectiveness of student participation depends heavily on self-regulation readiness at each phase of learning. The role of Gender in structural models shows a pattern consistent with contemporary theory. Gender does not have a significant direct influence on FP, PP, or SRP, but it moderates the relationship between ISFF and self-regulation. These findings are in line with the Gender Schema Theory (Well, 1981) Dan’s gender similarities hypothesis (Hyde, 2005 ), which states that differences in cognitive abilities and self-regulation between males and females are relatively small. However, differences arise in the way individuals respond to pedagogic and social contexts. The Gender Interaction × ISFF and Gender × SVP indicate that the effectiveness of lecturer support and student participation is influenced by different patterns of socialization and learning experiences between genders, rather than by inherent differences in self-regulatory capacity. Overall, the results of this study confirm that student self-regulation is a complex and dynamic process, influenced by the interaction between instructional support, curriculum structure, student participation, and gender factors. The integration of the four theories provides a sharper understanding of why ISFF is the dominant predictor, why the RFC’s influence is limited to the initial phase, why the SVP operates differently across phases, and why Gender is better positioned as a moderator rather than a direct predictor. Theoretically, this study enriches the SRL literature by providing empirical evidence on the phase-specific nature of self-regulatory determinants. In practice, these findings imply the need for a learning design in higher education that is adaptive to the stages of student self-regulation and sensitive to gender context. 8. Conclusion This study aims to analyze the determinants of student self-regulation using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach, integrated within the framework of Self-Regulated Learning (SRL). By mapping self-regulation into three main phases—forethought, performance, and self-reflection—and incorporating instructional, curricular, participatory, and demographic variables, this study provides a comprehensive understanding of how student self-regulation develops in higher education. The results of the study showed that Instructional Support and Faculty Facilitation (ISFF) was the most dominant and consistent predictor in all phases of student self-regulation. These findings confirm that instructional support and lecturer facilitation play a central role in helping students plan learning, implement learning strategies effectively, and conduct self-reflection and evaluation. With high and stable path coefficients across all three phases, ISFF proved to be the most decisive environmental factor in the development of student self-regulation, reinforcing the main assumption in SRL and Social Cognitive Theories that self-regulation is contextual and significantly influenced by the quality of pedagogic interactions. In contrast, Curriculum Relevance and Flexibility (RFC) shows a limited and phase-specific influence. RFC contributes positively mainly to the forethought phase, but its influence on the performance and self-reflection phases is relatively weak. These findings indicate that a relevant and flexible curriculum plays an important role in shaping the initial orientation and clarity of learning objectives, but does not directly ensure the sustainability of self-regulation at the implementation and reflection stages without adequate instructional support. Student Participation and Voice (SVP) shows a different pattern of influence across the phases of self-regulation. The negative influence of SVP on the forethought phase suggests that student participation is not necessarily effective at the initial planning stage, which still requires structure and direction. On the other hand, the positive influence of SVP on the performance and self-reflection phases confirms that active involvement and student voices become especially meaningful when students are already engaged in learning and self-evaluation. These findings provide empirical evidence that the effectiveness of student participation is conditional and highly dependent on the stages of self-regulation. Regarding demographic factors, gender does not show a significant direct influence on the three phases of student self-regulation. However, gender plays an important moderating role in the strength of the relationship between ISFF, SVP, and self-regulation. This shows that gender differences do not lie in self-regulation itself. However, it is more about how students respond to instructional support and participation opportunities in the learning context. These findings reinforce the contemporary theoretical view that gender is better understood as a contextual factor than as a major determinant of learning. Theoretical implication . Theoretically, this study makes an important contribution to the Self-Regulated Learning literature by showing that the determinants of student self-regulation are phase-specific, asymmetric, and influenced by contextual interactions, including gender factors. The integration of SRL with Social Cognitive Theory, Self-Determination Theory, and Gender Schema Theory enables a sharper, more holistic understanding of students’ self-regulation mechanisms in higher education. Practically, the findings of this study imply that the development of student self-regulation cannot rely solely on curriculum design or increased participation. Higher education institutions need to make instructional support for lecturers the main strategy, while adjusting the levels of structure, facilitation, and space for student participation in accordance with the developing phase of self-regulation. With an adaptive and gender-sensitive approach, efforts to improve learning quality are expected to be more effective at sustainably encouraging student independence in learning. Declarations Conflict of Interest The authors declare that there is no conflict of interest regarding the publication of this article. Funding The authors received no financial support for the research, authorship, or publication of this article. Ethics approval statement The research protocol was reviewed and approved by the Research and Community Service Ethics Committee of the Institute for Research and Community Service, State Islamic Institute IAIN Kerinci, Indonesia, under Approval Number B 203 In31 R KP001 02 2026. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and complied with applicable national research ethics guidelines for studies involving human participants. All participants provided informed consent prior to participation, and their anonymity, confidentiality, and voluntary participation were fully ensured throughout the research process. Consent to Participate Statement All participants involved in this study were aged 16 years or older. Informed consent to participate in the research was obtained from all participants prior to data collection. Participation was voluntary and participants were informed about the purpose of the study, the procedures involved, their right to withdraw at any time without consequence, and the assurance of anonymity and confidentiality. As no participants were under 16 years of age, parental or legal guardian consent was not required. Consent to Publish All participants provided informed consent for the anonymous use and publication of their research data findings and related academic materials. No identifiable personal information is included in this manuscript. The contribution statement The author conceptualised the study, designed the research framework, developed the research instruments, conducted data collection and analysis, and drafted the manuscript. Author B contributed to refining the research design, provided critical feedback on the theoretical and methodological components, reviewed and edited the manuscript, and validated the interpretation of the findings. All authors reviewed and approved the final manuscript and take responsibility for the integrity of the work. Data Availability The data and materials that support the findings of this study are available from the corresponding author upon reasonable request. Due to ethical considerations and the need to protect participant confidentiality, the data are not publicly available but can be shared in an anonymised form for research purposes. References Alharbi A, Henskens F, Hannaford M. Student-Centered Learning Objects to Support the Self-Regulated Learning of Computer Science. Creative Educ. 2012;3(October):773–83. Anastasia L, Abidin Z. Self-Regulated Learning in Students Who Pass the SNBT Using E-Learning as a Learning Media. Int J Sci Soc. 2024;6(2):602–14. Bandura A. Social Learning Theory of Aggression. J Od Communication. 1978;39(11):13–29. Bem SL. (1981). Gender schema theory: A cognitive account of sex typing. In Psychological Review (Vol. 88, Issue 4, pp. 354–364). American Psychological Association. https://doi.org/10.1037/0033-295X.88.4.354 Biggs J, Tang C. Teaching for quality learning at university. McGraw-Hill Education; 2011. Bovill C. Co-creation in learning and teaching: the case for a whole-class approach in higher education. Content courtesy of Springer Nature, terms of use apply. Rights reserved. High Educ. 2020;79(1):1023–37. Creswell JW. Research Design: Qualitative, Quantitative, and Mixed Method Approaches. SAGE; 2013. Deci EL. Effects of externally mediated rewards on intrinsic motivation. J Personal Soc Psychol. 1971;18(1):105–15. https://doi.org/10.1037/h0030644 . Dunn JC, Zimmer C. Self-determination theory. Routledge Handb Adapted Phys Educ. 2020;55(1):296–312. https://doi.org/10.4324/9780429052675-23 . Eskasasnanda IDP, Kurniawan NC, Rahmad- N. (2025). Gen Z in Higher Education: Harnessing Digital Strengths, Overcoming Learning Barriers, and Redefining the Educator’s Role. The International Conference on Social Studies and Educational Issues Research , 510–521. https://doi.org/10.18502/kss.v10i31.20405 García-Cano Torrico M, Jiménez Millán A, Hinojosa Pareja EF. Talking for the sake of talking? Imagining inclusive universities in Spain through their leaders. Int J Incl Educ. 2024;1–15. https://doi.org/10.1080/13603116.2024.2335377 . Hair JF, Black WC, Babin BJ, Anderson RE. (2014). Multivariate Data Analysis . Pearson New International Edition, Pearson Education. Hair JF, Risher JJ, Sarstedt M, Ringle CM. The Results of PLS-SEM Article Information. Eur Bus Rev. 2018;31(1):2–24. Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;31(1):2–24. https://doi.org/10.1108/EBR-11-2018-0203 . Henseler J, Sarstedt M. Goodness-of-fit indices for partial least squares path modeling. Comput Stat. 2013;28(2):565–80. https://doi.org/10.1007/s00180-012-0317-1 . Horlin C, Hronska B, Nordmann E. I can be a normal student: the role of lecture capture in supporting the participation of disabled and neurodivergent students in higher education. High Educ. 2024. https://doi.org/10.1007/s10734-024-01201-5 . Hu Z, Liu H, Peng Y, Luo M, Peng J, Peng X, Wang Y, Zeng L, Zeng Z. Competence-based curriculum reform of Principles of Genetic Engineering in biomedical education for promoting the construction of first- class majors and disciplines: a qualitative study Competence-based curriculum reform of Principles of Genetic Engineering in biomedical education for promoting the construction. Cogent Educ. 2025;12(1). https://doi.org/10.1080/2331186X.2025.2466306 . Hyde JS. (2005). The gender similarities hypothesis. In American Psychologist (Vol. 60, Issue 6, pp. 581–592). American Psychological Association. https://doi.org/10.1037/0003-066X.60.6.581 Kaur S, Pahuja J. Best Classroom Management Practices. 2019 Ijrti |. 2019;4(4):82. www.ijrti.org. Legault L. (2017). Self-Determination Theory. In Encyclopedia ofPersonality and Individual Differences (pp. 1–9). Springer. https://doi.org/10.1007/978-3-319-28099-8 Ma Y, Yang XM, Hong L, Tang RJ. (2022). The Influence of Stress Perception on Academic Procrastination in Postgraduate Students: The Role of Self- Efficacy for Self-Regulated Learning and Self-Control. International Journal of Digital Multimedia Broadcasting , 2022 . https://doi.org/https://doi.org/10.1155/2022/6722805 Meng X, Bin, Tao J, Shi NZ. An item response model for Likert-type data that incorporates response time in personality measurements. J Stat Comput Simul. 2014;84(1):1–21. https://doi.org/10.1080/00949655.2012.692368 . Mundia L. Psychology Teacher Education in Brunei: Evolution, Development, Challenges, and Growth. In Handbook of Research on Teacher Education: Innovations and Practices in Asia . Springer Nat. 2022. https://doi.org/10.1007/978-981-16-9785-2_19 . Neliwati H, U., P., R., S., A., Arif M. Curriculum Management in Improving The Quality of Student Learning and Academic Achievement. Munaddhomah: J Islamic Educ Manage. 2023;4(1):115–21. Qureshi S, Ullah R. Learning Experiences of Higher Education Students: Approaches to Learning as Measures of Quality of Learning Outcomes. Bull Educ Res. 2014;36(1):79–100. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68–78. https://doi.org/https://doi.org/10.1037/0003-066X.55.1.68 . Seemiller C, Grace M. (2017). Generation Z: Educating and Engaging the Next Generation of Students. In About Campus: Enriching the Student Learning Experience (pp. 21–26). Sage Publication Ltd. https://doi.org/https://doi.org/10.1002/abc.2129 Shi Y, Wu M, Wei Y, Chen J, Dong Q, Zhu K. (2025). A person-centered perspective in assessing college students ′ self-regulated learning in an online learning environment: potential profiles, antecedents, and outcomes. Int J Educational Technol High Educ, 22 (66). Watanabe C, Saito K. (2024). Designing a Self-Regulated and Constructive Database Course for Deaf and Hard-of-Hearing Students. In M. D., B. S.S., & L. M, editors, ACM International Conference Proceeding Series (pp. 27–32). Association for Computing Machinery. https://doi.org/10.1145/3663649.3664370 Weimer M. Learner Centered Teaching: Five Key Changes to Practice. Jossey-Bass; 2002. Yang L, Yang M. (2023). Effects of self-assessment and peer-assessment interventions on academic performance: A meta-analysis. Educational Research Review , 37 (December 2021). https://doi.org/10.1016/j.edurev.2022.100484 Zimmerman B. (2016). Becoming a Self-Regulated Learner: An Overview Becoming a Self-Regulated Learner : An Overview. Theory into Practice , JUNE 2002 . https://doi.org/10.1207/s15430421tip4102 Zimmerman B, Weimer M, Leeuwen A, Van, Janssen J, Conijn R, Kester L, Strategies RI, Learning AS, Alharbi A, Henskens F, Hannaford M, Marín VI, Zarouk MY, Khaldi M, Anastasia L, Abidin Z, Carmen M, Torres G, Bergamin PB, Rifqoh F. Student-Centered Learning Objects to Support the Self-Regulated Learning of Computer Science. Smart Learn Environ. 2025;5(2):649–64. https://doi.org/10.1186/s40561-021-00184-5 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 14 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8840560","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598515297,"identity":"6143ec8a-efa3-42ff-9b48-a2c8fd916521","order_by":0,"name":"Eka Putra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYFACNiA2AFLMzIcfSP6xAfIYGw8Qp4WdLc3AsiENpKWBCC0gwM9jIFHZcBjMxquFX7ot8XFFwR15PmYGA4ObO87brW0/DLSlxiYalxbJOccOG54xeGbYxsyQ8HDmmdvJ284kArUcS8ttwKHF4EZ6m2SDweEENmaGA8YSbLeTzQ4AtTA2HMapxf5GevtPiBbGBuk/bOeSzc4/xK/FQCLtGCNECzODhGTbATuzGwRskbhzLBnkMKBf2NgMJM4kJ5jdANqSgMcv/LPbDD82/DksL99//vMDiQo7e7Pz6Q8ffKixwamFQQKNnwhWmYBLOTYt9vgUj4JRMApGwcgEAGeTYk9/o1lXAAAAAElFTkSuQmCC","orcid":"","institution":"Institut Agama Islam Negeri Kerinci","correspondingAuthor":true,"prefix":"","firstName":"Eka","middleName":"","lastName":"Putra","suffix":""},{"id":598515298,"identity":"1830bc58-1835-4ea2-aabf-fb19d6d92070","order_by":1,"name":"Wasehudin Wasehudin","email":"","orcid":"","institution":"Universitas Islam Negeri Sultan Maulana Hasanuddin Banten","correspondingAuthor":false,"prefix":"","firstName":"Wasehudin","middleName":"","lastName":"Wasehudin","suffix":""}],"badges":[],"createdAt":"2026-02-10 11:47:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8840560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8840560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104412648,"identity":"37c3fe11-c4a5-46ef-a360-8042673ca1ea","added_by":"auto","created_at":"2026-03-11 13:00:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133770,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesis Formulation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eAuthor’s\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8840560/v1/c6f564b512c7881612d71cdb.png"},{"id":104410595,"identity":"dfdbad03-d295-48e1-8d27-b91d72a21df7","added_by":"auto","created_at":"2026-03-11 12:52:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114269,"visible":true,"origin":"","legend":"\u003cp\u003eData Description\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Author’s\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8840560/v1/5988fd0923839e5c9a83a950.png"},{"id":104412641,"identity":"5da2596c-2e82-413d-8ea7-f0c6fa01866a","added_by":"auto","created_at":"2026-03-11 13:00:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17732,"visible":true,"origin":"","legend":"\u003cp\u003eGender distribution of Respondents\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Author’s\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8840560/v1/0c5ab914b70acb4486601411.png"},{"id":104412666,"identity":"a1f003ad-d961-493c-b35b-d2265f6f2802","added_by":"auto","created_at":"2026-03-11 13:00:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142517,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesis-based Model Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Data Analysis (PLS-SEM)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8840560/v1/394008892c1d7b0350fc7f50.png"},{"id":104415756,"identity":"4ab08009-0b80-4f69-b6b7-35162e543dd5","added_by":"auto","created_at":"2026-03-11 13:11:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1344903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8840560/v1/36581160-015b-449a-8940-5484e209f2b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Student-centered curriculum management predicts self-regulated learning among Generation Z students in Islamic higher education in Sumatra, Indonesia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChanges in the landscape of higher education inevitably require student-centered curriculum management to ensure that graduates are competent in conducting independent and adaptive learning (Kaur \u0026amp; Pahuja, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This change in the landscape of higher education is not without reason: it occurs when the focus of education, which was teacher-centered, shifts toward a student-centered learning process. Of course, this change is supported by curriculum management, which places students at the center of the learning process\u0026mdash;from planning and implementation to curriculum evaluation. This kind of management fosters harmony among students\u0026rsquo; needs, characteristics, and learning experiences, ensuring alignment with learning objectives, teaching strategies, and assessment systems. From this kind of focus, it seems very obvious that the position of students is not functioning and interpreted only as passive recipients of knowledge, but is positioned as active learners who have space to choose, participate, and reflect on their learning process (Mundia, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qureshi \u0026amp; Ullah, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeimer (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) further explained that student-centered curriculum management has several advantages\u0026mdash;distinctions that make this concept stronger than others, including shifting learning from lecturers to students by emphasizing learning responsibility, active engagement, and high-level skill development. At the same time, Biggs (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) emphasizes that learning objectives, learning activities, and assessments should ideally be designed in harmony to achieve meaningful learning outcomes. In this context, \u003cem\u003estudent-centered curriculum management\u003c/em\u003e is not only about classroom teaching methods but also encompasses curriculum policies, syllabus design, evaluation approaches, and monitoring mechanisms that fully support active and independent learning. (Shi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this context, \u003cem\u003estudent-centered curriculum management\u003c/em\u003e plays a key role. This is because curriculum management that is aligned among learning objectives, learning activities, and assessment systems can provide autonomy, encourage active engagement, and deliver consistent feedback, which are essential for the development \u003cem\u003eof self-regulated learning.\u003c/em\u003e (Zimmerman, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At the same time, this affirms, from a social-cognitive perspective, that \u003cem\u003eself-regulated learning\u003c/em\u003e does not develop separately as a concept. However, one example of a curriculum design product that allows students to set goals, apply learning strategies, and reflect. As a result, \u003cem\u003estudent-centered curriculum management\u003c/em\u003e can be seen as a strategic institutional mechanism in fostering students\u0026rsquo; self-regulation skills.\u003c/p\u003e \u003cp\u003eIdeally, student-centered curriculum management as a theory seems necessary to test in a relevant social context, and in this case, among Gen-Z students. Why Gen-Z students should be used as the basis for empirical testing of this theory seems to rest on an underlying argument: the uniqueness of Gen-Z students is not only a reality but also a theory. Eskasasnanda et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) In this case, the author elaborates on the Gen-Z character, explaining that this generation was born between the mid-1990s and the early 2010s. In relation to universities, Gen-Z students have strong abilities to access and process information quickly, multitask, and use digital technology as a cognitive extension in learning activities. In the Indonesian context, Gen-Z students are more likely to have a shorter, more immediate understanding span; they are distracted by digital entertainment and have difficulty maintaining long-term focus and information retention (Eskasasnanda et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This character certainly affects the learning process of Gen-Z students, and it is often judged by its functional outcomes\u0026mdash;by completing an assignment\u0026mdash;rather than by its depth.\u003c/p\u003e \u003cp\u003eThe above conditions certainly confirm an undeniable reality: Gen-Z students have a fairly high dependence on gadgets and social media. This kind of dependence certainly has implications for their study quality and motivation, disrupts time management, and even leads to a tendency to postpone academic assignments. In addition, although Gen Z students are generally used to interacting online, many show limitations in face-to-face communication, in expressing opinions, and in active involvement in class discussions. They are often overcome with anxiety about social judgment and are reluctant to express learning difficulties to lecturers. Of course, this can hinder the academic and personal development of Gen-Z students. This identification is based entirely on the study of Eskasasnanda et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, it affirms that Gen-Z students need not only cognitive support but also psychological and emotional support, as well as a safe, conducive, and participatory learning environment. In this context, a curriculum design that is better adapted to the needs of Gen-Z students is needed: one that focuses on meaningful technology integration and provides quick, constructive feedback. Of course, it also requires strengthening autonomy and self-reflection. Therefore, a comprehensive understanding of Gen Z\u0026rsquo;s learning characteristics is needed to provide an important foundation for designing student-centered curriculum management that supports the optimal development of self-regulated learning.\u003c/p\u003e \u003cp\u003eThere is extensive literature showing that a student-centered learning approach can support \u003cem\u003eself-regulated learning\u003c/em\u003e (SLR), as evidenced by studies by Alharbi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Anastasia and Abidin (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Zimmerman (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This means that studies on this topic are growing rapidly and are both in demand and considered important to continue. However, specific studies examining the role of curriculum management policy and practices at the university level among Gen-Z students in Islamic universities in Sumatra remain very limited; even now, they have not been addressed comprehensively. It is also worth highlighting that existing studies published at the local level\u0026mdash;Indonesian national journals, still tend to focus on teaching or course interventions, and do not touch on the more macro-aspects of curriculum management planning, implementation, monitoring, and evaluation (Garc\u0026iacute;a-Cano Torrico et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Horlin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Watanabe \u0026amp; Saito, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In response to these limitations, this study and article are expected to present a new model that estimates complex variables involving curriculum management, Gen-Z characteristics, and their influence on SRL. However, this study was conducted in the context of Islamic universities in Sumatra\u0026mdash;a domain that remains underexplored in the literature. Thus, the study is expected to contribute by developing the theory of self-regulated learning by treating student-centered curriculum management as an exogenous variable, which is suspected to affect SLR among Gen-Z students at State Islamic Religious Universities on the island of Sumatra, Indonesia.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Student-Centered Curriculum Management and Self-Regulated Learning:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eAn Elaborated-Concept\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe development of 21st-century education requires a paradigm shift\u0026mdash;from a teacher-oriented approach to learning to one that puts students at the center of the learning process. This shift concerns not only learning strategies in the classroom but also a more structural aspect: how the curriculum is designed, managed, and implemented systematically. In this context, curriculum management based on student-centered learning (SCL) is an important foundation for encouraging learners to become independent, reflective, and able to manage their own learning process, a process known as self-regulated learning (SRL). Student-centered learning emphasizes students\u0026rsquo; active role in building knowledge through meaningful, collaborative, and contextual learning experiences. This approach views students not as passive recipients of information but as subjects capable of planning, monitoring, and evaluating their own learning process. Several studies have shown that implementing student-centered learning positively contributes to intrinsic motivation, deeper conceptual understanding, and the development of critical thinking and problem-solving skills (Hu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pei \u0026amp; Wu, 2019). Nevertheless, the effectiveness of this approach depends largely on how the curriculum is managed thoroughly and consistently. This is understandable because curriculum management includes a series of interrelated processes, ranging from planning and organizing to implementing and evaluating the curriculum. From the perspective of educational management, the curriculum is understood not only as an academic document but also as a strategic tool for achieving learning goals and developing students\u0026rsquo; competencies. Neliwati et al. (2023) emphasized that effective curriculum management can improve the quality of learning and academic achievement of students through systematic planning, adaptive implementation, and continuous evaluation. When the student-centered principle is applied at every stage of curriculum management, the space for student learning independence will be wider.\u003c/p\u003e \u003cp\u003eOne relevant approach in this context is the competence-based curriculum, which focuses on developing real competencies through active and reflective learning, as studied by Hu et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that competency-based curriculum reform, with an interactive learning model and blended learning, can increase student participation, satisfaction with learning, and independent learning ability. This model implicitly encourages self-regulated learning through integrated pre-class, in-class, and post-class stages. Thus, SRL is not only a learning goal but also an internal mechanism that is strengthened by curriculum design and management. Self-regulated learning refers to students\u0026rsquo; ability to set learning goals, choose appropriate strategies, monitor progress, and reflect on learning outcomes independently. In a student-centered learning environment, SRL acts as a bridge between curriculum policy and individual learning experiences. Without a flexible, student-oriented curriculum, SRL development tends to be sporadic and relies solely on personal initiative. On the other hand, curriculum management that is consciously designed to support learning autonomy will sustainably strengthen students\u0026rsquo; self-regulation.\u003c/p\u003e \u003cp\u003eFurthermore, the integration of technology and blended learning also strengthens the relationship between student-centered curriculum management and SRL. The digital learning environment provides opportunities for students to access learning resources independently, set their own learning pace, and interact reflectively with materials and lecturers. However, as noted in various studies, technology will only be effective if it is supported by a clear curriculum design, measurable learning objectives, and an evaluation system that encourages self-reflection (Ma et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang \u0026amp; Yang, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on this description, it can be concluded that student-centered curriculum management and self-regulated learning are interrelated and mutually reinforcing. Student-oriented curriculum management provides structure, resources, and learning experiences that support the development of self-regulation. On the contrary, students\u0026rsquo; self-regulated learning ability is an indicator of the success of implementing a student-centered curriculum. Therefore, conceptual elaboration of the relationship between these two concepts is important as a theoretical basis for developing curriculum policies and learning practices in higher education.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Self-Determination Theory: An Overview","content":"\u003cp\u003eSelf-Determination Theory (SDT) is one of the most comprehensive motivational frameworks in modern psychology, providing an understanding of \u003cem\u003ewhy\u003c/em\u003e and \u003cem\u003ehow\u003c/em\u003e humans act in various social contexts. In contrast to classical motivational theories, which tend to judge motivation by its quantity\u0026mdash;how strong a person\u0026rsquo;s motivation is\u0026mdash;SDT emphasizes the quality of motivation. It is these qualities that determine whether a person acts autonomously or is under external control. With this perspective, SDT develops not just as a theory of motivation but as a metatheory encompassing six mini-theories that explain human development, social functioning, and psychological health. As a metatheory, SDT departs from the organismal assumption that humans have a natural drive toward growth, self-integration, and active involvement. However, this push does not work automatically; It can be hampered when the social environment is oppressive or does not support basic psychological needs. Therefore, SDT examines in depth how social contexts\u0026mdash;in education, work, health, and interpersonal relationships\u0026mdash;can facilitate or even inhibit healthy motivation.\u003c/p\u003e \u003cp\u003eThe essence of SDT is three basic psychological needs: autonomy, competence, and relatedness (Dunn \u0026amp; Zimmer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Autonomy refers to the need to feel that actions come from oneself and are not forced. Competence refers to the sense of effectiveness in mastering one\u0026rsquo;s environment and achieving goals. Relatedness describes the need to feel emotional closeness and meaningful connections with others. SDT introduces an assumption that these three needs are universal\u0026mdash;not culturally specific\u0026mdash;with a consistent function in maintaining motivation and psychological well-being (Legault, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Within the framework of SDT, motivation is divided into two broad categories: autonomous motivation and controlled motivation. Autonomic motivation includes intrinsic motivation and deeply internalized forms of extrinsic motivation, such as identified and integrated regulation. A person acts out of a personal value or interest. In contrast, controlled motivation arises when behavior is triggered by external or internal pressures, such as punishment, rewards, guilt, or demands to maintain self-esteem. SDT shows that autonomic motivation leads to sustained performance, better mental health, and higher life satisfaction, whereas controlled motivation is associated with stress, burnout, and lower long-term performance. In this regard, Organismic Integration Theory (OIT) explains that extrinsic motivation is not singular, but rather on the internalized continuum\u0026mdash;a person can act entirely due to external factors (external regulation), partly internal (introjected regulation), or entirely due to integrated regulation (integrated regulation). In this context, the ILO strongly opposes the traditional view that extrinsic motivation is inferior; in reality, the quality of extrinsic regulation depends heavily on support for autonomy and competence.\u003c/p\u003e \u003cp\u003eIn addition, Cognitive Evaluation Theory (CET) can be used; in this case, it focuses on the factors that affect intrinsic motivation. CET shows that rewards, feedback, or evaluations can increase or decrease intrinsic motivation depending on whether the stimulus is perceived as a control or as information. This phenomenon is contrary to the classic behavioristic assumption that rewards always increase motivation. In relation to SDT, it is understandable that controlling rewards can undermine a sense of autonomy and reduce one\u0026rsquo;s intrinsic interest in certain activities (So, 1971). In addition, Causality Orientation Theory (COT) explains the differences in an individual\u0026rsquo;s disposition to respond to the environment: autonomous orientation (acting on personal values), controlled orientation (acting on pressure), and impersonal orientation (feeling powerless). This orientation is formed from long-term experiences in social contexts that support or suppress psychological needs. Overall, SDT provides a critical and humanistic picture of human motivation: that optimal development occurs not through external control, but through an environment that values psychological freedom, provides appropriate challenges, and establishes supportive relationships. SDT is not just a theory of motivation but also a guide to building a social context that drives human growth, resilience, and well-being.\u003c/p\u003e \u003cp\u003eIn relation to Self-Regulated Learning (SRL), Self-Determination Theory provides a motivational foundation that explains why and how students can independently, sustainably, and reflectively manage their learning process. SRL requires individuals to set goals, monitor progress, manage learning strategies, and evaluate learning outcomes consciously\u0026mdash;and this whole process is highly dependent on the quality of motivation. When autonomy needs are met, learners are more likely to set meaningful learning goals and choose strategies that align with personal values; Fulfillment of competence strengthens self-confidence to monitor and adjust learning strategies effectively; While relatedness creates a sense of psychological security that supports perseverance and engagement in learning. In this framework, autonomous motivation as explained by SDT\u0026mdash;especially through the internalization of learning values (identified and integrated regulation)\u0026mdash;is an important prerequisite for the development of adaptive SRL. In contrast, controlled motivation tends to result in shallow, reactive, and dependent learning. Thus, SDT not only complements SRL as a supportive motivational theory but also deepens understanding of how self-regulation in learning is rooted in the socio-pedagogical context that supports students\u0026rsquo; basic psychological needs.\u003c/p\u003e"},{"header":"4. Framework: Initial Model and Hypothesis Formulation","content":"\u003cp\u003eThis research framework is based on the understanding that student-oriented curriculum management (student-centered curriculum management) plays an important role in improving Generation Z students\u0026rsquo; self-regulation skills, especially at Islamic religious universities in the Sumatra region. The student-centered approach to the curriculum emphasizes flexibility, relevance, active student participation, and instructional support that facilitate an independent and reflective learning process (Weimer, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). Student-oriented curriculum management in this study is operationalized through three main dimensions, namely: (1) curriculum relevance and flexibility, (2) student participation and voice, and (3) instructional support and lecturer facilitation. Relevance and flexibility reflect the extent to which the curriculum can adapt to students\u0026rsquo; needs and the demands of scientific development. Student participation and voice describe student involvement in the decision-making process and learning activities (Bovill, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meanwhile, instructional support includes the role of lecturers in guiding, motivating, and providing a learning environment that encourages independence (Zimmerman, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). These three dimensions are assumed to contribute to the development of students\u0026rsquo; self-regulated learning abilities. \u003cem\u003eSelf-regulated learning\u003c/em\u003e is understood as the ability of students to plan, monitor, control, and evaluate their learning process independently (Zimmerman et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the context of digital generation learning, this ability is crucial because Generation Z students tend to want independent, flexible, and technology-based learning (Seemiller \u0026amp; Grace, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on this conceptual framework, this study formulates a main hypothesis that assesses the relationship between student-oriented curriculum management and \u003cem\u003eself-regulated learning\u003c/em\u003e as an overall construct (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, several sub-hypotheses were formulated that tested the influence of each dimension\u0026mdash;curriculum relevance and flexibility, student participation and voice, and instructional support and lecturer facilitation\u0026mdash;on \u003cem\u003eself-regulated learning\u003c/em\u003e. Using this hypothetical model, the research aims to provide an empirical basis for the extent to which a student-centered curriculum approach can improve students\u0026rsquo; ability to manage self-directed learning (SLR). The research findings are expected to contribute to the development of curriculum, learning strategies, and education policies that are more responsive to the characteristics and learning needs of Generation Z. The full study hypothesis is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResearch Hypotheses\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCode\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable Relationship\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHypothesis Statement\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurriculum Relevance and Flexibility \u0026rarr; Forethought Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurriculum relevance and flexibility have a positive and significant effect on students\u0026rsquo; forethought phase.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurriculum Relevance and Flexibility \u0026rarr; Performance Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurriculum relevance and flexibility have a positive and significant effect on students\u0026rsquo; performance.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurriculum Relevance and Flexibility \u0026rarr; Self-Reflection Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurriculum relevance and flexibility have a positive and significant effect on students\u0026rsquo; self-reflection phase.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudent Participation and Voice \u0026rarr; Forethought Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudent participation and voice have a positive and significant effect on students\u0026rsquo; forethought phase.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudent Participation and Voice \u0026rarr; Performance Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudent participation and voice have a positive and significant effect on students\u0026rsquo; performance.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudent Participation and Voice \u0026rarr; Self-Reflection Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudent participation and voice have a positive and significant effect on students\u0026rsquo; self-reflection phase.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstructional Support and Faculty Facilitation \u0026rarr; Forethought Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstructional support and faculty facilitation have a positive and significant effect on students\u0026rsquo; forethought phase.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstructional Support and Faculty Facilitation \u0026rarr; Performance Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstructional support and faculty facilitation have a positive and significant effect on students\u0026rsquo; performance.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstructional Support and Faculty Facilitation \u0026rarr; Self-Reflection Phase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstructional support and faculty facilitation have a positive and significant effect on students\u0026rsquo; self-reflection phase.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForethought Phase \u0026rarr; Q1 (Learning Outcome)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe forethought phase has a positive and significant effect on learning outcomes (Q1).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePerformance Phase \u0026rarr; Q1 (Learning Outcome)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe performance phase has a positive and significant effect on learning outcomes (Q1).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSelf-Reflection Phase \u0026rarr; Q1 (Learning Outcome)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe self-reflection phase has a positive and significant effect on learning outcomes (Q1).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"5. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Research Design\u003c/h2\u003e \u003cp\u003eThis study uses a quantitative approach with a Partial Least Squares\u0026ndash;Structural Equation Modeling (PLS-SEM) design. The use of PLS-SEM was chosen because the research model involves complex relationships among latent variables, relatively large sample sizes, and potentially unevenly distributed data. According to Hair et al. (2021), PLS-SEM is a suitable method for both predictive and exploratory research. It is ideal when the objective is to maximize the explanatory variance (R\u0026sup2;) of endogenous constructs. This research model comprises three main constructs: \u003cem\u003eStudent-Centered Curriculum Management\u003c/em\u003e (SCCM), \u003cem\u003eSelf-Regulated Learning\u003c/em\u003e (SRL), and \u003cem\u003eLearning Engagement\u003c/em\u003e. The research aims to test the causal relationships among the three constructs, based on the formulated hypothesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Sample and Data Collection\u003c/h2\u003e \u003cp\u003eThe research population includes students who are studying at Islamic Religious Universities in the Sumatra region. Sampling technique using \u003cem\u003evolunteer sampling\u003c/em\u003e, with a Google Form-based online distribution system for active students in the current academic year. The total number of responses collected is 508, which is considered adequate for PLS-SEM analysis, as suggested by Hair et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which states that the large sample size increases the stability of the model estimation. Data were collected online via a Google Form questionnaire. The questionnaire was distributed through academic networks, social media, and student groups at Islamic universities in Sumatra, Indonesia. The use of online questionnaires was chosen to expand the reach of respondents and simplify the data collection process (Creswell, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, the data collection period runs from October 1, 2025, to January 1, 2026.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Research Instruments\u003c/h2\u003e \u003cp\u003eThe research instrument consists of three main parts that measure the constructs of SCCM, SRL, and Learning Engagement. Each construct is measured using several items based on the Likert scale of 1\u0026ndash;5 (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree), in accordance with the common practice of measuring latent variables in social research (Meng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The instrument was developed based on literature studies and adapted to the context of Islamic Universities. The content\u0026rsquo;s validity is assessed through a validation test before the questionnaire is distributed online. All instrument items are listed in the Google Form, which serves as a data collection tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Data Analysis Procedure\u003c/h2\u003e \u003cp\u003eData analysis was carried out in two main stages, following the PLS-SEM approach: First, Measurement Model Analysis (Outer Model) to assess the construct\u0026rsquo;s validity and reliability. \u003cem\u003eSecond\u003c/em\u003e, Structural Model Analysis (Inner Model) to test the relationship between latent variables, as well as hypothesis testing. The analysis is carried out using the latest version of SmartPLS, as recommended by Hair et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for PLS-SEM analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.5. Measurement Model Assessments (Outer Model)\u003c/h2\u003e \u003cp\u003eEvaluation of the measurement model in the PLS-SEM approach is conducted to ensure that the indicators used represent the latent constructs validly and reliably. This process includes three main components, namely convergent validity, discriminant validity, and construct reliability. First, convergent validity is assessed using outer loadings and the Average Variance Extracted (AVE). An indicator is considered to have an adequate contribution if its outer loading value exceeds 0.708. Meanwhile, an AVE of at least 0.50 indicates that the construct accounts for at least 50% of the variance in its indicators (Hair et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, a construct is considered to be valid in a convergent manner if both criteria are met. Second, the validity of the discriminator is evaluated to ensure that each construct truly distinguishes itself from the others. The assessment was carried out using two approaches. First, the Fornell\u0026ndash;Larcker criterion, which is to compare the square root of AVE with the correlation between constructs. The validity of the discriminant is fulfilled when the root of AVE is higher than the correlation of the other constructs. Second, the Heterotrait\u0026ndash;Monotrait Ratio (HTMT) is a modern, more sensitive approach for detecting discriminant validity issues. An HTMT value below 0.85 indicates that the constructs are completely different (Henseler \u0026amp; Sarstedt, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Third, a reliability test is carried out to assess the internal consistency of the indicator in measuring the same construct. The two measures used are Cronbach\u0026rsquo;s Alpha and Composite Reliability (CR). The values of both must be between 0.70 and 0.95 to be reliable. The range illustrates a balance between adequate internal consistency and avoiding indicator redundancy. Overall, the measurement model is deemed eligible if all validity and reliability parameters fall within the tolerance limits recommended in the literature. If all these requirements are met, the construct is ready to be used in the evaluation stage of the structural or inner model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.6. Structural Model Assessment (Inner Model)\u003c/h2\u003e \u003cp\u003eOnce the measurement model is declared valid and reliable, the next step in the PLS-SEM approach is to evaluate the structural model (inner model). This evaluation assesses the strength of relationships among latent constructs and the model\u0026rsquo;s ability to explain the variance of the endogenous constructs. The evaluation process includes several main stages: collinearity check, determination coefficient (R\u0026sup2;), effect size (f\u0026sup2;), predictive relevance (Q\u0026sup2;), and interpretation of path coefficients. The first stage is Collinearity Assessment, which is conducted to ensure that relationships among predictor variables are not multicollinear. The collinearity test was performed using the Variance Inflation Factor (VIF), with a threshold of 5, indicating that the model is free of collinearity that could interfere with parameter estimation (Hair et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The second stage is the examination of the Coefficient of Determination (R\u0026sup2;), which indicates how much variance in the endogenous construct is explained by its predictors. The R\u0026sup2; value reflects the strength of the model in explaining the phenomenon being studied. According to Hair et al. (2021), the R\u0026sup2; value can be categorized into three levels: substantial (\u0026ge;\u0026thinsp;0.75), moderate (\u0026ge;\u0026thinsp;0.50), and weak (\u0026ge;\u0026thinsp;0.25). A high R\u0026sup2; value indicates that the model has strong explanatory abilities. Next, Effect Size (f\u0026sup2;) is used to assess the magnitude of each predictor variable\u0026rsquo;s influence on the endogenous constructs. The value of f\u0026sup2; allows the researcher to understand the unique contribution of each predictor in the model. A higher value of f\u0026sup2; indicates that the variable has a greater impact on the variance of the endogenous constructs. The fourth stage is Predictive Relevance (Q\u0026sup2;), calculated using the blindfolding technique. A Q\u0026sup2; value greater than 0 indicates that the model has adequate predictive capabilities against endogenous constructs. (Hair et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, the Q\u0026sup2; value is an important indicator of whether the model is not only theoretically sound but also empirically relevant. The final stage is interpreting Path Coefficients, which describe the strength and direction of the relationships between latent variables in the model. The path coefficient serves as the basis for testing the research hypothesis. A larger coefficient value indicates a stronger relationship, while a positive or negative sign indicates the direction of influence between constructs. Testing the significance of this coefficient is typically done using bootstrapping in PLS-SEM. Overall, the evaluation of the structural model provides a comprehensive overview of the model\u0026rsquo;s predictive quality, the strength of relationships between latent variables, and the contribution of each construct in explaining the phenomenon under study. If all parameters are within the recommended limits, the model is considered suitable for further interpretation during hypothesis testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.7. Hypothesis Testing\u003c/h2\u003e \u003cp\u003eHypothesis testing in this study was conducted using a bootstrapping procedure with 5,000 subsamples, as recommended by Hair et al. (2021), to ensure accurate parameter estimation despite the relatively small sample size. This study involved 568 students from various Islamic Universities in Sumatra, which is an adequate sample size for PLS-SEM analysis, as this method does not require a normal distribution and can accommodate medium sample sizes. The bootstrapping technique is used to obtain t-statistics and p-values, which are the basis for determining whether a research hypothesis is accepted or rejected. A latent construct relationship is declared significant when the t-statistic value reaches or exceeds 1.96 at the significance level of α\u0026thinsp;=\u0026thinsp;0.05, and the p-value is at or below 0.05. This criterion indicates that the relationship between latent variables is statistically significant and not random. Through this procedure, hypothesis testing shows whether the relationship between constructs in the research model is significant. If the relationship is significant, it means that empirical data from 568 respondents support the influence between constructs; Conversely, the insignificant relationship suggests that the effects between variables do not have sufficient statistical support. Thus, testing hypotheses through bootstrapping allows researchers to assess the validity of causal relationships in structural models and to provide an empirical basis for the theoretical and practical interpretation of the research.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e6.1. Data Description and Respondent Characteristics\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the distribution of respondents by college of origin, showing variation in the number of respondents across institutions. Descriptively, this distribution shows the concentration of respondents across several universities, with IAIN Curup having the highest number of respondents. Visually, the dominance of IAIN Curup respondents is evident in the taller bar relative to other universities, indicating a significant contribution to the overall research sample. This pattern reflects the active involvement of the institution\u0026rsquo;s academic community in the research conducted, which can be related to institutional proximity, academic networks, or the suitability of research topics to the local academic context and needs. Statistically, this distribution reflects a pattern of respondent concentration, the main characteristic of the study sample\u0026rsquo;s composition.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt IAIN Curup, Fatmawati Sukarno Islamic University (UINFAS), and UIN Mahmud Yunus Batusangkar are in the second group, with relatively high numbers of respondents, but are quantitatively quite far from the top institutions. This group forms a secondary cluster that acts as a distribution balancer, but it is not enough to reduce the dominance of the main institution. Furthermore, there is a medium group filled by UIN Raden Fatah, IAIN Kerinci, and UIN Sjech M. Djamil Djambek. The frequency of respondents in this group declined gradually. Statistically, this condition follows an exponentially decreasing distribution: the further to the right on the category axis, the fewer the respondents. This group serves as a transition between institutions that make large contributions and those that make very small contributions. The last part of the graph shows the long-tail distribution, marked by the number of universities with very low response rates, such as UIN Raden Intan Lampung, UIN Al-Azhar Lubuklinggau, and UIN Sultan Syarif Kasim. Statistically, this group makes a minor contribution to the total number of respondents, but it is still important to maintain institutional diversity in the research. Descriptively, this graph illustrates the empirical reality of respondents\u0026rsquo; participation and shows the academic centers most actively involved in the research. Overall, the distribution of respondents in this graph shows a pattern of high concentration in one major institution, a gradual decline in medium-sized institutions, and a long tail of institutions with low participation, which are common characteristics in academic and region-specific network-based research.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, a pie chart, illustrates the distribution of respondents by gender in this study. Overall, the majority of respondents were female (75.6%), while male respondents accounted for 24.4%. This composition shows that participation in the study is higher among women. The distribution pattern reflects respondents\u0026rsquo; involvement, which aligns with the characteristics of participation in academic survey-based research, especially in higher education or a specific field of study. The dominance of female respondents indicates a significant contribution from a female perspective to the research data, which is integral to the overall findings. Methodologically, this distribution can represent the empirical conditions of the field and the level of respondent involvement, based on demographic characteristics relevant to the research context. Thus, this graph provides an overview of the respondents\u0026rsquo; gender composition, which is the basis for interpreting the research results as a whole.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e6.2. Evaluation of Measurement Models (Outer Model)\u003c/h2\u003e\n\u003cp\u003eThe evaluation of the outer model in this study assessed the quality of indicators in representing both exogenous and endogenous latent constructs. The assessment of the outer model focuses on the convergent validity test, as reflected in the outer loading values of each indicator. According to Hair et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), the indicator is declared to have good convergent validity if the outer loading value\u0026thinsp;\u0026ge;\u0026thinsp;0.70, because the value indicates that more than 50% of the indicator\u0026rsquo;s variance can be explained by the latent construct measured. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the first exogenous variable, Curriculum Relevance and Flexibility (RFC), measured using five indicators (C.1\u0026ndash;C.5). The test results showed that all indicators had high outer loadings, ranging from 0.863 to 0.887. This value exceeds the minimum recommended by Hair et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), indicating that these indicators strongly and consistently reflect the curriculum\u0026rsquo;s relevance and flexibility. The C.4 indicator has the highest value (0.887), indicating its dominant contribution to shaping the RFC construct.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOuter Model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRFC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePlease\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eISFF\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSRP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVIF\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eExogenous variable 1: Curriculum Relevance and Flexibility (RFC)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.863\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.686\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.868\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.375\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.879\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.867\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.222\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.191\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eExogenous variable 2: Student Participation and Voice (SVP)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.187\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.383\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.787\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.687\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.406\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eExogenous variable 3: Instructional Support and Faculty Facilitation (ISFF)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.383\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.095\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.919\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.208\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.411\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eEndogenous variable 1: Forethought Phase (FP)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.911\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.752\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.834\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.049\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.898\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.403\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eEndogenous variable 2: Performance Phase (PP)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.822\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.864\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.644\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.757\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eH.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.600\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eEndogenous variable 2: Self-Reflection Phase (SRP)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.452\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.904\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.569\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.380\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.926\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.519\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Data analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe second exogenous variable, Student Participation and Voice (SVP), also showed excellent measurement quality. The five indicators used (D.1\u0026ndash;D.5) had outer loadings ranging from 0.787 to 0.926. Indicator D.5 recorded the highest score (0.926), which indicates that the student participation and vote aspects of the indicator are the strongest representation of the SVP construct. Although indicator D.3 has the lowest value (0.787), Hair et al. assert that indicators above 0.70 are still worth defending, especially if they support the construct\u0026rsquo;s content validity. Furthermore, the third exogenous variable, Instructional Support and Faculty Facilitation (ISFF), showed a very strong performance of the outer model. All indicators (E.1\u0026ndash;E.5) have high outer loading values, ranging from 0.890 to 0.919. The E.3 indicator with the highest score (0.919) shows that instructional support and lecturer facilitation are key elements in the formation of the ISFF construct. The high outer loading value in all indicators confirms the consistency and reliability of the measurement of this variable, as required in the evaluation of the PLS-SEM measurement model according to Hair et al., (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor the endogenous variables, the Forethought Phase (FP) was measured using five indicators (G.1\u0026ndash;G.5), with outer loadings ranging from 0.897 to 0.913. All indicators show a relatively balanced and strong contribution, indicating that the initial planning phase of learning is comprehensively shaped by the indicators used. The Performance Phase (PP) variable has an outer loading of 0.859\u0026ndash;0.887. Although the value is slightly lower than that of the other constructs, all indicators still meet the criteria for convergent validity set by Hair et al., so this construct is empirically valid. Finally, the Self-Reflection Phase (SRP) is measured using indicators I.1\u0026ndash;I.5, with an outer loading value of 0.845\u0026ndash;0.933. Indicator I.4 shows the highest value (0.933), confirming the central role of self-reflection in the formation of the SRP construct. Overall, the results of the outer model evaluation indicate that all constructs in this study meet the convergent validity criteria recommended by Hair et al., so the measurement model is deemed feasible to proceed to the inner model analysis stage.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e6.3. Structural Model Evaluation\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the coefficient of determination (R\u0026sup2;) and adjusted R\u0026sup2; for three endogenous constructs: FP, PP, and SRP. In general, the value of R\u0026sup2; indicates the proportion of variation in the dependent variable that can be explained by the independent variable in the model, thus reflecting the strength of the structural model\u0026rsquo;s explanatory power. The analysis showed that the FP construct had an R\u0026sup2; of 0.751 and an adjusted R\u0026sup2; of 0.748, indicating that about 75.1% of FP\u0026rsquo;s variance is explained by the model\u0026rsquo;s predictors. This value is relatively high, indicating that the model has a strong ability to explain FP, with small differences between R\u0026sup2; and adjusted R\u0026sup2;, indicating good model stability. Furthermore, the PP construct shows an R\u0026sup2; of 0.704 and an adjusted R\u0026sup2; of 0.700, indicating that the exogenous construct accounts for 70.4% of the PP variance. Although the value is slightly lower than FP, it is still considered strong, so the model can be considered effective at explaining the behavior or phenomenon represented by the PP. The small difference between the adjusted R\u0026sup2; and R\u0026sup2; also indicates that the model is not overfitting.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCoefficient of Determination (R\u0026sup2;)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR-square\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR-square adjusted\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.748\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.749\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.746\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Data Analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMeanwhile, the SRP construct has an R\u0026sup2; of 0.749 and an adjusted R\u0026sup2; of 0.746, indicating that the independent variables in the model explain 74.9% of the variance in SRP. This value is almost equivalent to FP and reinforces the conclusion that structural models as a whole have strong explainability. Thus, based on the general criteria for PLS-SEM analysis, the entire endogenous construct in this model demonstrates good explanatory quality and supports the model\u0026rsquo;s feasibility for further hypothesis testing.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePath Coefficient of Structural Model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePredictor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSRP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.624\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRFC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.281\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.194\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender \u0026times; ISFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender \u0026times; SVP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.429\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.212\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender \u0026times; RFC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Data Analysis (PLS-SEM)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe path coefficients in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e above provide a comprehensive overview of the direct effects of Gender moderation on three endogenous constructs: Forethought Phase (FP), Performance Phase (PP), and Self-Reflection Phase (SRP). Overall, the findings suggest that substantive variables in the model make a stronger contribution than demographic variables. At the same time, Gender plays a more significant role as a moderation variable than as a direct predictor. Directly, Gender showed a very small coefficient and tended to be negative towards FP (\u0026beta; = \u0026minus;0.059), PP (\u0026beta; = \u0026minus;0.061), and SRP (\u0026beta; = \u0026minus;0.001). These values indicate that gender differences do not directly affect the planning, implementation, or self-reflection phases, so Gender is not the main determinant of variation in the three constructs. In contrast, Instructional Support and Faculty Facilitation (ISFF) emerged as the most dominant and consistent predictor. ISFF had a positive and strong effect on FP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.592), PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.348), and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.624). These findings confirm that instructional support and lecturer facilitation play a crucial role in shaping all phases of student self-regulation, starting from planning, performance, and reflection. The Curriculum Relevance and Flexibility (RFC) variable showed a positive but relatively weak influence on FP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.281), PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.074), and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.027). This indicates that the curriculum\u0026rsquo;s relevance and flexibility contribute more to the planning phase than to the performance and reflection phases. Meanwhile, Student Participation and Voice (SVP) showed a different pattern, namely a negative influence on FP (\u0026beta; = \u0026minus;0.073), but a fairly strong positive influence on PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.362) and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.194). These findings show that student participation plays a greater role in the implementation and self-evaluation phases than in the initial planning phase. In terms of moderation, the interaction of Gender \u0026times; ISFF showed a negative effect on FP (\u0026beta; = \u0026minus;0.163) and SRP (\u0026beta; = \u0026minus;0.134), but a positive effect on PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.139), indicating that the effect of ISFF on self-regulation varied by gender. Gender Interaction \u0026times; SVP showed a strong positive influence on FP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.429) and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.212), indicating that the impact of student participation was amplified by gender. In contrast, the Gender \u0026times; RFC interaction showed only a negative effect on FP (\u0026beta; = \u0026minus;0.099), indicating a limited moderating effect. Overall, these results confirm the complexity of the relationships among variables and the importance of considering gender moderation in structural models.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e6.4. Hypothesis Testing\u003c/h2\u003e\n\u003cp\u003eThe results of the hypothesis test (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) on the PLS-SEM structural model provide a comprehensive picture of the direct relationships and moderating effects of the exogenous variables across the three main phases of student self-regulation: Forethought Phase (FP), Performance Phase (PP), and Self-Reflection Phase (SRP). Evaluation of the structural model showed that all endogenous constructs had high coefficient of determination values, namely R\u0026sup2; = 0.751 for FP, R\u0026sup2; = 0.703 for PP, and R\u0026sup2; = 0.749 for SRP. These values indicate that the model has strong explanatory power, with more than 70% of the variation in each endogenous construct explained by the predictor variables, making it feasible to use the model for empirical hypothesis testing.\u003c/p\u003e\n\u003cp\u003eThe first through third hypothesis tests examine the direct influence of Instructional Support and Faculty Facilitation (ISFF) on FP, PP, and SRP. The analysis showed that ISFF had a strong positive effect on FP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.592), PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.348), and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.624). These findings support the hypothesis put forward and affirm that instructional support and lecturer facilitation play a central role in shaping all phases of student self-regulation. The strongest influence of ISFF was found in the self-reflection phase, which showed that the quality of academic support strongly determines students\u0026rsquo; ability to evaluate and improve their learning strategies.\u003c/p\u003e\n\u003cp\u003eThe next hypothesis tests the influence of Curriculum Relevance and Flexibility (RFC) on the three phases of self-regulation. The results showed that RFC had a positive effect on FP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.281), but its effect on PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.074) and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.027) was relatively weak. Thus, the hypothesis regarding the influence of RFCs on FP is supported, whereas their influence on PP and SRP provides only limited support. These findings indicate that a relevant and flexible curriculum primarily supports students during the planning and learning goal-setting stages, but has less direct impact on the implementation and reflection stages. Furthermore, hypothesis testing regarding Student Participation and Voice (SVP) showed mixed results. SVP negatively affects FP (\u0026beta; = \u0026minus;0.073), so the hypothesis of SVP\u0026rsquo;s positive effect on FP is not supported. Conversely, SVP had a positive and strong effect on PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.362) and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.194), so the hypothesis of SVP\u0026rsquo;s influence on both phases is supported. These findings show that active participation and student involvement play a greater role in the implementation phase of learning strategies and in reflecting on learning outcomes than in the initial planning phase. The direct influence of Gender on FP, PP, and SRP was also tested in the model. The results showed that Gender had a very small coefficient and tended to be negative towards FP (\u0026beta; = \u0026minus;0.059), PP (\u0026beta; = \u0026minus;0.061), and SRP (\u0026beta; = \u0026minus;0.001). Therefore, the hypothesis that gender directly influences student self-regulation is not supported. This indicates that gender differences do not directly determine students\u0026rsquo; self-regulation abilities in this study. However, the role of Gender becomes significant when it is modeled as a moderating variable. Gender Interactions \u0026times; ISFF showed a negative effect on FP (\u0026beta; = \u0026minus;0.163) and SRP (\u0026beta; = \u0026minus;0.134), but a positive effect on PP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.139). These findings suggest that the influence of ISFF on student self-regulation differs by gender, with instructional support being more effective during the performance phase for specific groups. Furthermore, the interaction of Gender \u0026times; SVP showed a strong positive effect on FP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.429) and SRP (\u0026beta;\u0026thinsp;=\u0026thinsp;0.212), supporting the moderation hypothesis. This indicates that student participation and voice have a greater impact on self-regulation when gender is considered. In contrast, the Gender interaction \u0026times; RFC showed only a negative effect on FP (\u0026beta; = \u0026minus;0.099) and no effect on PP and SRP, which supports this hypothesis only partially. Overall, the hypothesis test results confirm that ISFF is the dominant predictor in the model, followed by SVP and RFC, with different patterns of influence across the phases of self-regulation. In addition, these findings emphasize the importance of including gender-moderation variables to understand the complexity of relationships in student self-regulation models.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"7. Discussion","content":"\u003cp\u003eThe findings of this study make a strong empirical contribution to understanding student self-regulation through an integrated theoretical approach. Using Self-Regulated Learning (SRL) theory as the main framework, and supported by Social Cognitive Theory, Self-Determination Theory, and Gender Schema Theory, the PLS-SEM results show that the determinants of self-regulation operate in a phase-specific, contextual, and moderated manner rather than as direct predictors. The high value of the determination coefficient in the entire endogenous construct (R\u0026sup2; \u0026gt; 0.70) indicates that the model has a very strong and theoretically relevant explanatory power (Hair et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From the perspective of Self-Regulated Learning theory, self-regulation is understood as a cyclical process consisting of phases of forethought, performance, and self-reflection (Zimmerman, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The empirical findings of this study directly confirm this conceptual structure. Instructional Support and Faculty Facilitation (ISFF) proved to be the most consistent and dominant predictor across all three phases of self-regulation, with strong effects on FP (β\u0026thinsp;=\u0026thinsp;0.592), PP (β\u0026thinsp;=\u0026thinsp;0.348), and SRP (β\u0026thinsp;=\u0026thinsp;0.624). These results show that students\u0026rsquo; self-regulation does not develop independently but is strongly influenced by the quality of instructional support and lecturer facilitation, especially in higher education. These findings are in line with Zimmerman\u0026rsquo;s (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which emphasizes that environmental factors play an important role in shaping student learning planning, implementation, and reflection.\u003c/p\u003e \u003cp\u003eThe dominance of ISFF can be better understood through the lens of Social Cognitive Theory, which emphasizes reciprocal determinism among personal, behavioral, and environmental factors (Bandura, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). In this context, lecturers serve as environmental agents that influence students\u0026rsquo; self-confidence, learning strategies, and self-evaluation. The strongest influence of ISFF in the self-reflection phase indicates that academic feedback, reflective facilitation, and lecturer guidance strongly determine students\u0026rsquo; ability to evaluate the effectiveness of their learning strategies. These findings support Bandura\u0026rsquo;s (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) argument that appropriate environmental reinforcement can increase individuals\u0026rsquo; reflective and adaptive capacity during learning. In contrast to ISFF, Curriculum Relevance and Flexibility (RFC) shows a limited and phase-specific pattern of influence. RFC had a positive effect on the forethought phase (β\u0026thinsp;=\u0026thinsp;0.281), but its effects on the performance (β\u0026thinsp;=\u0026thinsp;0.074) and self-reflection (β\u0026thinsp;=\u0026thinsp;0.027) phases were relatively weak. Theoretically, this suggests that the curriculum functions primarily as an initial structural framework that helps students understand learning objectives, set goals, and build learning readiness. These findings are consistent with SRL\u0026rsquo;s assumption that structural factors have a greater role at the planning stage than at the implementation and reflection stages. (Zimmerman et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In other words, a relevant and flexible curriculum is important for facilitating early learning orientation, but it is not enough to encourage sustainable self-regulation without active instructional support.\u003c/p\u003e \u003cp\u003e The most theoretically interesting findings appeared in the Student Participation and Voice (SVP) variable. SVP showed a negative effect on the forethought phase (β = \u0026minus;0.073), but a strong positive effect on the performance (β\u0026thinsp;=\u0026thinsp;0.362) and self-reflection (β\u0026thinsp;=\u0026thinsp;0.194) phases. This pattern can be explained well by Self-Determination Theory (SDT). According to Deci and Ryan (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e)The need for autonomy and competence is best met when individuals are actively involved in learning. In the forethought phase, students still need structure, direction, and clear goals, so participating too early can actually reduce focus on planning. On the other hand, in the implementation and reflection phases, active involvement and student voices increase intrinsic motivation, a sense of ownership of the learning process, and the quality of self-evaluation. These findings show that the effectiveness of student participation depends heavily on self-regulation readiness at each phase of learning. The role of Gender in structural models shows a pattern consistent with contemporary theory. Gender does not have a significant direct influence on FP, PP, or SRP, but it moderates the relationship between ISFF and self-regulation. These findings are in line with the Gender Schema Theory (Well, 1981) Dan\u0026rsquo;s gender similarities hypothesis (Hyde, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which states that differences in cognitive abilities and self-regulation between males and females are relatively small. However, differences arise in the way individuals respond to pedagogic and social contexts. The Gender Interaction \u0026times; ISFF and Gender \u0026times; SVP indicate that the effectiveness of lecturer support and student participation is influenced by different patterns of socialization and learning experiences between genders, rather than by inherent differences in self-regulatory capacity. Overall, the results of this study confirm that student self-regulation is a complex and dynamic process, influenced by the interaction between instructional support, curriculum structure, student participation, and gender factors. The integration of the four theories provides a sharper understanding of why ISFF is the dominant predictor, why the RFC\u0026rsquo;s influence is limited to the initial phase, why the SVP operates differently across phases, and why Gender is better positioned as a moderator rather than a direct predictor. Theoretically, this study enriches the SRL literature by providing empirical evidence on the phase-specific nature of self-regulatory determinants. In practice, these findings imply the need for a learning design in higher education that is adaptive to the stages of student self-regulation and sensitive to gender context.\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThis study aims to analyze the determinants of student self-regulation using the Partial Least Squares\u0026ndash;Structural Equation Modeling (PLS-SEM) approach, integrated within the framework of Self-Regulated Learning (SRL). By mapping self-regulation into three main phases\u0026mdash;forethought, performance, and self-reflection\u0026mdash;and incorporating instructional, curricular, participatory, and demographic variables, this study provides a comprehensive understanding of how student self-regulation develops in higher education. The results of the study showed that Instructional Support and Faculty Facilitation (ISFF) was the most dominant and consistent predictor in all phases of student self-regulation. These findings confirm that instructional support and lecturer facilitation play a central role in helping students plan learning, implement learning strategies effectively, and conduct self-reflection and evaluation. With high and stable path coefficients across all three phases, ISFF proved to be the most decisive environmental factor in the development of student self-regulation, reinforcing the main assumption in SRL and Social Cognitive Theories that self-regulation is contextual and significantly influenced by the quality of pedagogic interactions. In contrast, Curriculum Relevance and Flexibility (RFC) shows a limited and phase-specific influence. RFC contributes positively mainly to the forethought phase, but its influence on the performance and self-reflection phases is relatively weak. These findings indicate that a relevant and flexible curriculum plays an important role in shaping the initial orientation and clarity of learning objectives, but does not directly ensure the sustainability of self-regulation at the implementation and reflection stages without adequate instructional support. Student Participation and Voice (SVP) shows a different pattern of influence across the phases of self-regulation. The negative influence of SVP on the forethought phase suggests that student participation is not necessarily effective at the initial planning stage, which still requires structure and direction. On the other hand, the positive influence of SVP on the performance and self-reflection phases confirms that active involvement and student voices become especially meaningful when students are already engaged in learning and self-evaluation. These findings provide empirical evidence that the effectiveness of student participation is conditional and highly dependent on the stages of self-regulation. Regarding demographic factors, gender does not show a significant direct influence on the three phases of student self-regulation. However, gender plays an important moderating role in the strength of the relationship between ISFF, SVP, and self-regulation. This shows that gender differences do not lie in self-regulation itself. However, it is more about how students respond to instructional support and participation opportunities in the learning context. These findings reinforce the contemporary theoretical view that gender is better understood as a contextual factor than as a major determinant of learning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheoretical implication\u003c/b\u003e. Theoretically, this study makes an important contribution to the Self-Regulated Learning literature by showing that the determinants of student self-regulation are phase-specific, asymmetric, and influenced by contextual interactions, including gender factors. The integration of SRL with Social Cognitive Theory, Self-Determination Theory, and Gender Schema Theory enables a sharper, more holistic understanding of students\u0026rsquo; self-regulation mechanisms in higher education. Practically, the findings of this study imply that the development of student self-regulation cannot rely solely on curriculum design or increased participation. Higher education institutions need to make instructional support for lecturers the main strategy, while adjusting the levels of structure, facilitation, and space for student participation in accordance with the developing phase of self-regulation. With an adaptive and gender-sensitive approach, efforts to improve learning quality are expected to be more effective at sustainably encouraging student independence in learning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol was reviewed and approved by the Research and Community Service Ethics Committee of the Institute for Research and Community Service, State Islamic Institute IAIN Kerinci, Indonesia, under Approval Number B 203 In31 R KP001 02 2026. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and complied with applicable national research ethics guidelines for studies involving human participants. All participants provided informed consent prior to participation, and their anonymity, confidentiality, and voluntary participation were fully ensured throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants involved in this study were aged 16 years or older. Informed consent to participate in the research was obtained from all participants prior to data collection. Participation was voluntary and participants were informed about the purpose of the study, the procedures involved, their right to withdraw at any time without consequence, and the assurance of anonymity and confidentiality. As no participants were under 16 years of age, parental or legal guardian consent was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent for the anonymous use and publication of their research data findings and related academic materials. No identifiable personal information is included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author conceptualised the study, designed the research framework, developed the research instruments, conducted data collection and analysis, and drafted the manuscript. Author B contributed to refining the research design, provided critical feedback on the theoretical and methodological components, reviewed and edited the manuscript, and validated the interpretation of the findings. All authors reviewed and approved the final manuscript and take responsibility for the integrity of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials that support the findings of this study are available from the corresponding author upon reasonable request. Due to ethical considerations and the need to protect participant confidentiality, the data are not publicly available but can be shared in an anonymised form for research purposes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlharbi A, Henskens F, Hannaford M. Student-Centered Learning Objects to Support the Self-Regulated Learning of Computer Science. Creative Educ. 2012;3(October):773\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnastasia L, Abidin Z. Self-Regulated Learning in Students Who Pass the SNBT Using E-Learning as a Learning Media. Int J Sci Soc. 2024;6(2):602\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandura A. Social Learning Theory of Aggression. J Od Communication. 1978;39(11):13\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBem SL. (1981). Gender schema theory: A cognitive account of sex typing. In \u003cem\u003ePsychological Review\u003c/em\u003e (Vol. 88, Issue 4, pp. 354\u0026ndash;364). American Psychological Association. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-295X.88.4.354\u003c/span\u003e\u003cspan address=\"10.1037/0033-295X.88.4.354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiggs J, Tang C. Teaching for quality learning at university. McGraw-Hill Education; 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBovill C. Co-creation in learning and teaching: the case for a whole-class approach in higher education. Content courtesy of Springer Nature, terms of use apply. Rights reserved. High Educ. 2020;79(1):1023\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCreswell JW. Research Design: Qualitative, Quantitative, and Mixed Method Approaches. SAGE; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeci EL. Effects of externally mediated rewards on intrinsic motivation. J Personal Soc Psychol. 1971;18(1):105\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/h0030644\u003c/span\u003e\u003cspan address=\"10.1037/h0030644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunn JC, Zimmer C. Self-determination theory. Routledge Handb Adapted Phys Educ. 2020;55(1):296\u0026ndash;312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9780429052675-23\u003c/span\u003e\u003cspan address=\"10.4324/9780429052675-23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEskasasnanda IDP, Kurniawan NC, Rahmad- N. (2025). Gen Z in Higher Education: Harnessing Digital Strengths, Overcoming Learning Barriers, and Redefining the Educator\u0026rsquo;s Role. \u003cem\u003eThe International Conference on Social Studies and Educational Issues Research\u003c/em\u003e, 510\u0026ndash;521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18502/kss.v10i31.20405\u003c/span\u003e\u003cspan address=\"10.18502/kss.v10i31.20405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Cano Torrico M, Jim\u0026eacute;nez Mill\u0026aacute;n A, Hinojosa Pareja EF. Talking for the sake of talking? Imagining inclusive universities in Spain through their leaders. Int J Incl Educ. 2024;1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13603116.2024.2335377\u003c/span\u003e\u003cspan address=\"10.1080/13603116.2024.2335377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Black WC, Babin BJ, Anderson RE. (2014). \u003cem\u003eMultivariate Data Analysis\u003c/em\u003e. Pearson New International Edition, Pearson Education.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Risher JJ, Sarstedt M, Ringle CM. The Results of PLS-SEM Article Information. Eur Bus Rev. 2018;31(1):2\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;31(1):2\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/EBR-11-2018-0203\u003c/span\u003e\u003cspan address=\"10.1108/EBR-11-2018-0203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenseler J, Sarstedt M. Goodness-of-fit indices for partial least squares path modeling. Comput Stat. 2013;28(2):565\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00180-012-0317-1\u003c/span\u003e\u003cspan address=\"10.1007/s00180-012-0317-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorlin C, Hronska B, Nordmann E. I can be a normal student: the role of lecture capture in supporting the participation of disabled and neurodivergent students in higher education. High Educ. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10734-024-01201-5\u003c/span\u003e\u003cspan address=\"10.1007/s10734-024-01201-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Z, Liu H, Peng Y, Luo M, Peng J, Peng X, Wang Y, Zeng L, Zeng Z. Competence-based curriculum reform of Principles of Genetic Engineering in biomedical education for promoting the construction of first- class majors and disciplines: a qualitative study Competence-based curriculum reform of Principles of Genetic Engineering in biomedical education for promoting the construction. Cogent Educ. 2025;12(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/2331186X.2025.2466306\u003c/span\u003e\u003cspan address=\"10.1080/2331186X.2025.2466306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyde JS. (2005). The gender similarities hypothesis. In \u003cem\u003eAmerican Psychologist\u003c/em\u003e (Vol. 60, Issue 6, pp. 581\u0026ndash;592). American Psychological Association. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0003-066X.60.6.581\u003c/span\u003e\u003cspan address=\"10.1037/0003-066X.60.6.581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur S, Pahuja J. Best Classroom Management Practices. 2019 Ijrti |. 2019;4(4):82. www.ijrti.org.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegault L. (2017). Self-Determination Theory. In \u003cem\u003eEncyclopedia ofPersonality and Individual Differences\u003c/em\u003e (pp. 1\u0026ndash;9). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-28099-8\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-28099-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Y, Yang XM, Hong L, Tang RJ. (2022). The Influence of Stress Perception on Academic Procrastination in Postgraduate Students: The Role of Self- Efficacy for Self-Regulated Learning and Self-Control. \u003cem\u003eInternational Journal of Digital Multimedia Broadcasting\u003c/em\u003e, \u003cem\u003e2022\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1155/2022/6722805\u003c/span\u003e\u003cspan address=\"10.1155/2022/6722805\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng X, Bin, Tao J, Shi NZ. An item response model for Likert-type data that incorporates response time in personality measurements. J Stat Comput Simul. 2014;84(1):1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00949655.2012.692368\u003c/span\u003e\u003cspan address=\"10.1080/00949655.2012.692368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMundia L. Psychology Teacher Education in Brunei: Evolution, Development, Challenges, and Growth. In \u003cem\u003eHandbook of Research on Teacher Education: Innovations and Practices in Asia\u003c/em\u003e. Springer Nat. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-16-9785-2_19\u003c/span\u003e\u003cspan address=\"10.1007/978-981-16-9785-2_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeliwati H, U., P., R., S., A., Arif M. Curriculum Management in Improving The Quality of Student Learning and Academic Achievement. Munaddhomah: J Islamic Educ Manage. 2023;4(1):115\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQureshi S, Ullah R. Learning Experiences of Higher Education Students: Approaches to Learning as Measures of Quality of Learning Outcomes. Bull Educ Res. 2014;36(1):79\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1037/0003-066X.55.1.68\u003c/span\u003e\u003cspan address=\"10.1037/0003-066X.55.1.68\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeemiller C, Grace M. (2017). Generation Z: Educating and Engaging the Next Generation of Students. In \u003cem\u003eAbout Campus: Enriching the Student Learning Experience\u003c/em\u003e (pp. 21\u0026ndash;26). Sage Publication Ltd. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1002/abc.2129\u003c/span\u003e\u003cspan address=\"10.1002/abc.2129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Y, Wu M, Wei Y, Chen J, Dong Q, Zhu K. (2025). A person-centered perspective in assessing college students \u0026prime; self-regulated learning in an online learning environment: potential profiles, antecedents, and outcomes. Int J Educational Technol High Educ, \u003cem\u003e22\u003c/em\u003e(66).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe C, Saito K. (2024). Designing a Self-Regulated and Constructive Database Course for Deaf and Hard-of-Hearing Students. In M. D., B. S.S., \u0026amp; L. M, editors, \u003cem\u003eACM International Conference Proceeding Series\u003c/em\u003e (pp. 27\u0026ndash;32). Association for Computing Machinery. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3663649.3664370\u003c/span\u003e\u003cspan address=\"10.1145/3663649.3664370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeimer M. Learner Centered Teaching: Five Key Changes to Practice. Jossey-Bass; 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Yang M. (2023). Effects of self-assessment and peer-assessment interventions on academic performance: A meta-analysis. \u003cem\u003eEducational Research Review\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(December 2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.edurev.2022.100484\u003c/span\u003e\u003cspan address=\"10.1016/j.edurev.2022.100484\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman B. (2016). Becoming a Self-Regulated Learner: An Overview Becoming a Self-Regulated Learner : An Overview. \u003cem\u003eTheory into Practice\u003c/em\u003e, \u003cem\u003eJUNE 2002\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15430421tip4102\u003c/span\u003e\u003cspan address=\"10.1207/s15430421tip4102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman B, Weimer M, Leeuwen A, Van, Janssen J, Conijn R, Kester L, Strategies RI, Learning AS, Alharbi A, Henskens F, Hannaford M, Mar\u0026iacute;n VI, Zarouk MY, Khaldi M, Anastasia L, Abidin Z, Carmen M, Torres G, Bergamin PB, Rifqoh F. Student-Centered Learning Objects to Support the Self-Regulated Learning of Computer Science. Smart Learn Environ. 2025;5(2):649\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40561-021-00184-5\u003c/span\u003e\u003cspan address=\"10.1186/s40561-021-00184-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Self-regulated learning, instructional support, student participation, curriculum relevance, gender moderation, PLS-SEM","lastPublishedDoi":"10.21203/rs.3.rs-8840560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8840560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines factors influencing students\u0026rsquo; self-regulation using Partial Least Squares\u0026ndash;Structural Equation Modeling (PLS-SEM) within the Self-Regulated Learning (SRL) framework, which conceptualizes self-regulation as a cyclical process comprising forethought, performance, and self-reflection. The proposed model integrates Instructional Support and Faculty Facilitation (ISFF), Curriculum Relevance and Flexibility (RFC), and Student Participation and Voice (SVP), with Gender tested as a moderating variable. Data were collected through an online survey of 508 students from Islamic universities across Sumatra, Indonesia. PLS-SEM was employed to assess both measurement and structural models. The findings indicate strong explanatory power, with R\u0026sup2; values exceeding 0.70 for all endogenous constructs. ISFF emerged as the most robust and consistent predictor across all three SRL phases, underscoring the critical role of instructional support and lecturer facilitation in students\u0026rsquo; learning planning, execution, and reflection. RFC showed a positive effect mainly on the forethought phase, with limited influence on performance and self-reflection. SVP negatively affected forethought but positively and significantly influenced performance and self-reflection, suggesting that student participation operates differently across SRL phases. Gender had no direct effect on self-regulation; however, it moderated the relationships between ISFF and SVP, as well as between ISFF and self-regulation. Theoretically, this study reinforces the contextual and phase-specific nature of self-regulation. Practically, the findings highlight the importance of adaptive instructional design, prioritizing instructional support while aligning student participation with specific stages of self-regulated learning.\u003c/p\u003e","manuscriptTitle":"Student-centered curriculum management predicts self-regulated learning among Generation Z students in Islamic higher education in Sumatra, Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 11:30:11","doi":"10.21203/rs.3.rs-8840560/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T06:12:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T06:40:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T14:29:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179039878322007204290361291077388124063","date":"2026-04-13T15:31:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194385071732401967104597828417356351508","date":"2026-04-13T12:43:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59923255366558971819325576694347358634","date":"2026-02-28T09:39:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T09:24:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-14T06:50:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T16:09:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2026-02-13T16:04:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f47b9f8e-269f-4762-ab3d-b16a20e68f71","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T07:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 11:30:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8840560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8840560","identity":"rs-8840560","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}