The Influence of Instructors' Technological Pedagogical and Content Knowledge (TPaCK) on the Self-Regulated Learning-Online (SRL-O) of the Students

The Influence of Instructors' Technological Pedagogical and Content Knowledge (TPaCK) on the Self-Regulated Learning-Online (SRL-O) of the Students | 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 The Influence of Instructors' Technological Pedagogical and Content Knowledge (TPaCK) on the Self-Regulated Learning-Online (SRL-O) of the Students Joseline M. Santos, PhD This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8800976/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the impact of instructors' Technological Pedagogical and Content Knowledge (TPACK) on students' Self-Regulated Learning-Online (SRL-O) in a state university in Bulacan, the Philippines. Employing a quantitative correlational research design, this study aimed to provide evidence-based recommendations for enhancing online education. Participants included 299 instructors and 381 students from six campuses selected through random sampling. Data were collected using validated survey instruments tailored to the TPACK and SRL-O. The results revealed a significant positive correlation between instructors' TPACK and students' SRL-O, with regression analysis showing that TPACK accounted for 90.2% of the variance in SRL-O. This underscores the critical role of instructors' abilities to integrate technology, pedagogy, and content in fostering students' self-regulation in online learning. Key findings highlight that, while instructors generally possess a moderate understanding of TPACK, areas such as Technological Content Knowledge (TCK) and Technological Pedagogical Content Knowledge (TPCK) require further enhancement. For students, the dimensions of SRL-O, including task strategies, effort regulation, and planning, were positively rated, but challenges remained in intrinsic motivation and managing negative emotions. The study concludes that strengthening instructors' TPACK through targeted training programs can significantly boost students' self-regulatory capacity. Simultaneously, initiatives to support students’ emotional well-being and motivation are crucial for optimizing online learning outcomes. These findings provide actionable insights for developing professional development initiatives and student support programs to ensure an effective and adaptive online learning ecosystem. Educational Philosophy and Theory technology pedagogy content knowledge self-regulated online learning Figures Figure 1 Introduction The rapid shift to online learning accelerated by the global pandemic has transformed the educational landscape, placing significant emphasis on the need for effective teaching strategies in virtual environments. Online learning, characterized by its flexibility and accessibility, has simultaneously introduced challenges in student engagement, motivation, and self-regulation. Self-regulated learning (SRL), a process in which learners actively control their cognitive, metacognitive, and motivational processes, has emerged as a critical factor in academic success in online environments (Edisherashvili et al., 2022 ). In this context, the role of instructors becomes crucial, as they are not only facilitators of content, but also designers of learning experiences that foster autonomy, engagement, and academic achievement. The Technological Pedagogical and Content Knowledge (TPaCK) framework (Koehler, 2016 ), offers a comprehensive lens for examining how instructors integrate technology with pedagogical and content expertise in their teaching practices. TPaCK encompasses three core domains–technology knowledge (TK), pedagogy knowledge (PK), and content knowledge (CK)–and their intersections, enabling educators to create meaningful learning experiences by strategically leveraging technological tools. In online learning environments, where the absence of physical proximity requires a more deliberate instructional design, TPaCK has become increasingly relevant. Instructors who effectively integrate TPaCK principles can enhance the learning environment and promote deeper student engagement, collaboration, and self-directed learning behaviors (Ni et al., 2023 ; Sim et al., 2023 ). Despite the increasing recognition of TPaCK’s importance in online education, there remains a gap in understanding how instructors' TPaCK proficiency directly influences students' SRL in online learning (SRL-O) performance. SRL-O, encompassing self-efficacy, intrinsic motivation, extrinsic motivation, negative achievement emotion, planning and time management, metacognition, effort regulation, social support, and task strategies, is essential for students to navigate the online learning landscape. Therefore, this study explores the influence of instructors’ TPaCK capabilities on self-regulated learners in online learning (SRL-O) contexts. In the post-pandemic era, educational institutions have been compelled to explore alternative approaches to ensure continuity of learning, especially when onsite classes are disrupted. These disruptions may occur because of inclement weather, local events, or other unforeseen circumstances requiring the cancellation of face-to-face sessions. Online learning has emerged as a key modality that enables institutions to maintain educational delivery despite such interruptions. By leveraging digital platforms, schools and universities can ensure that learning remains uninterrupted, providing flexible and accessible solutions to meet the demands of modern education. After reviewing related studies on TPaCK and SRL-O, the author observed that research involving college instructors across various programs is limited. Most studies on TPaCK have focused on pre-service teachers. Additionally, there is a lack of studies comparing instructors’ TPaCK with students' SRL-O. This is the first study to explore whether instructors' TPaCK affects students' SRL-O. By investigating the influence of instructors' TPaCK on SRL-O performance, this study aims to provide insights into how the integration of technology, pedagogy, and content knowledge impacts students' self-regulatory behaviors in an online learning environment. These findings will contribute to the growing body of knowledge on online education and offer practical recommendations for improving teaching strategies that foster self-regulated learning, ultimately enhancing student success in digital learning environments. Statement of the Problem Despite the critical role of instructor readiness and student autonomy in the efficacy of online education, a significant empirical gap persists in understanding how instructors' specific integrated competencies influence student learning behaviors in digital environments. This study addresses this deficiency by first establishing the pedagogical context through a descriptive analysis of instructors' full Technological Pedagogical Content Knowledge (TPaCK) profile, detailing their capacity across seven domains (CK, PK, TK, PCK, TCK, TPK, and TPACK). Concurrently, the research characterizes students' Self-Regulated Learning in Online courses (SRL-O) across six dimensions, including goal setting, environment structuring, and self-evaluation. The central problem this investigation aims to resolve is determining the extent to which the instructors' multifaceted TPACK significantly influences the performance of students' SRL-O, culminating in the proposal of a comprehensive training program designed to strategically enhance both instructor TPACK and student SRL-O for improved online learning outcomes. Review of Related Literature The Technological Pedagogical Content Knowledge (TPACK) framework has emerged as a crucial model for integrating technology, pedagogy, and content knowledge in education, particularly in online learning environments. This review synthesizes the literature into two central themes: the impact of TPACK on teaching effectiveness and student engagement and the importance of ongoing support and tailored development for educators in applying TPACK. TPACK and Teaching Effectiveness in Online Learning Several studies have underscored the effectiveness of the TPACK framework in enhancing teaching practices, particularly in online courses. Instructors who have undergone TPACK-based training have demonstrated significant improvement in their ability to design and deliver online courses. Brinkley-Etzkorn ( 2018 , 2020 ) observed that TPACK-trained instructors developed more effective course designs, reflecting deeper integration of technology with pedagogy and content. However, these improvements were not always mirrored in student evaluations, suggesting that, while TPACK enhances instructional quality, its immediate impact on student perception may be less evident. Nonetheless, the framework clearly supports the development of teaching strategies that foster student engagement, self-regulated learning (SRL), and better learning outcomes, especially in technology-driven environments (Willermark, 2016 ; Zhang et al., 2019 ). Active involvement in lesson design also plays a pivotal role in the effective application of the TPACK. Instructors who actively design technology-enhanced lessons tend to better integrate TPACK components, resulting in more meaningful and engaging learning experiences for students (Brinkley-Etzkorn, 2020 ). This hands-on approach allows instructors to explore and experiment with technology in ways that align with pedagogical goals and content delivery. By doing so, TPACK-trained instructors are better equipped to promote SRL strategies among students, which is critical for success in online learning environments where self-directed study is key. Ongoing Support and Tailored Development for Effective TPACK Integration While TPACK training boosts instructor confidence and competence in using technology, research shows that initial gains in optimism often diminish once instructors begin teaching online (Brinkley-Etzkorn, 2020 ). This highlights the importance of providing ongoing support to help educators continuously refine their TPACK skills and adapt to the dynamic challenges in online education. Without sustained professional development, instructors may struggle to maintain confidence and effectiveness during initial TPACK training. Continuous training, mentorship, and peer collaboration can provide the long-term support needed by instructors to fully leverage TPACK in their teaching practices. Literature on TPACK demonstrates its crucial role in enhancing teaching effectiveness and student engagement, particularly in online learning environments. However, for instructors to fully benefit from TPACK, ongoing support and tailored development are necessary to sustain confidence and competence. Active participation in lesson design is vital for successfully integrating TPACK, whereas differences in knowledge domain integration highlight the need for differentiated approaches to professional development. Methodology The study utilized a quantitative approach to research using a descriptive-correlational approach. The TPACK of the instructors and the SRL for online learning were determined using survey questionnaires. The TPACK instrument consists of a total of 28 items, with four items each for pedagogical content (pk), content knowledge (ck), technology knowledge (tk), pedagogical content knowledge (pck), technology pedagogy knowledge (tpk), technology content knowledge (tck), and technology pedagogy and content knowledge (tpck). The instrument was adopted from the study of Schmid et al. ( 2020 ) which undergone the test of reliability wherein each of the seven subscales emerged as reliable, with Cronbach's alphas between .77 and .91 and McDonald's omegas between 0.79 and 0.92. The SRL for Online Learning was adopted from the study by Broadbent et al. ( 2022 ) on the development of self-regulation for learning online (SRL-O) questionnaire. These data were used to test statistically whether the TPACK of the instructor significantly influenced the SRL of the students in online learning. This study was conducted on the main campus and five other external campuses of a state university in Bulacan. The respondents available during the data collection period were 299 faculty members and 381 students. The faculty members based on their sex at birth revealed that the majority were female, accounting for 52.51% (157 respondents), while male respondents constituted 47.49% (142 respondents). The students, based on their sex at birth, showed a slight majority of 53.28% (203 respondents) in one group, whereas the other group represented 46.72% (178 respondents). The total number of respondents is 381. This nearly equal distribution indicates a well-balanced sample in terms of sex, allowing for a representative analysis of the responses across both groups. These close percentages suggest minimal disparity, which can help ensure a more inclusive and diverse understanding of the data. The study strictly adhered to ethical research standards to ensure the rights, privacy, and well-being of all participants. Prior to data collection, formal approval was obtained from the university's ethics review committee. Informed consent was secured from all participants, emphasizing the voluntary nature of their participation and their right to withdraw at any time without penalty. The anonymity and confidentiality of the respondents were maintained throughout the research process by assigning unique codes and securing all data in password-protected files. Additionally, the researchers ensured that the information gathered was used solely for academic and research purposes. All procedures followed were in accordance with ethical guidelines for research involving human participants. Results This section presents the findings of the study on the Technological Pedagogical and Content Knowledge (TPACK) of instructors and the Self-Regulated Learning in Online Learning (SRL-O) of students. It includes the statistical analyses conducted to determine the level of instructors’ TPACK and students’ SRL-O, as well as the relationship between these two variables. The section also outlines the proposed training program developed based on the results to enhance instructors’ TPACK and improve students’ SRL-O. Table 1 Technological Pedagogical and Content Knowledge (TPACK) of the Instructors Indicator Mean SD Description 1. Pedagogical Content (PC) 3.15 0.86 Has some knowledge 2. Content Knowledge (CK) 3.15 0.86 Has some knowledge 3. Technology Knowledge (TK) 3.11 0.84 Has some knowledge 4. Pedagogy Content Knowledge (PCK) 3.12 0.85 Has some knowledge 5. Technology Pedagogy Knowledge (TPK) 3.14 0.86 Has some knowledge 6. Technology Content Knowledge (TCK) 3.08 0.84 Has some knowledge 7. Technology Pedagogy Content Knowledge (TPCK) 3.07 0.85 Has some knowledge OVERALL 3.12 0.85 Has some knowledge Table 1 summarizes the instructors' self-assessment of their Technological Pedagogical and Content Knowledge (TPACK), with an overall mean score of 3.12 and a standard deviation of 0.85, indicating that they "have some knowledge" in all key areas of TPACK. Pedagogical Content Knowledge (PC) and Content Knowledge (CK) both scored the highest mean of 3.15, suggesting that instructors feel moderately confident in their understanding of pedagogy and content. Technology Knowledge (TK) scored slightly lower at 3.11, indicating a similar but slightly less confident perception of technological skills. The lowest score (mean = 3.07) is for Technology Pedagogy Content Knowledge (TPCK), which reflects the integration of technology, pedagogy, and content in teaching. This suggests that, while instructors feel that they have some knowledge in all areas, their ability to fully integrate these elements into a cohesive teaching strategy may require further development. Overall, the results highlight that instructors possess foundational knowledge across the TPACK framework, but may benefit from additional training to strengthen their integration of technology into their teaching practices. 2. Self-Regulated Learning for Online Learning of the Students The findings on students’ Self-Regulated Learning in Online Learning (SRL-O) encompass various dimensions, including online academic self-efficacy, motivation, effort regulation, metacognition, study environment, and task strategies. Table 2 presents the data that illustrate how students organize, monitor, and control their learning behaviors within the online learning environment. Table 2 Self-Regulated Learning-Online (SRL-O) of the Students Self-Regulated Learning-Online (SRL-O) of the Students Indicator Mean SD Description 1. Online Academic Self-efficacy 3.01 0.79 True of me 2. Online Intrinsic Motivation 2.98 0.82 True of me 3. Online Extrinsic Motivation 3.01 0.82 True of me 4. Online Negative Achievement Emotion 3.02 0.81 True of me 5. Planning and time management 3.03 0.81 True of me 6. Metacognition 3.05 0.80 True of me 7. Study Environment 3.06 0.78 True of me 8. Online Effort Regulation 3.12 0.75 True of me 9. Online Social Support 3.06 0.74 True of me 10. Online Task Strategies 3.16 0.67 True of me OVERALL 3.05 0.78 True of me The overall Self-Regulated Learning-Online (SRL-O) of students across various dimensions is summarized in Table 2 . The mean scores for the different indicators indicate that students generally agree with the statements across all dimensions, as each is described as "True of me’." The indicator with the highest mean score was Online Task Strategies (M = 3.16, SD = 0.67), suggesting that students often employed proactive strategies for their online learning tasks. Online Effort Regulation followed closely (M = 3.12, SD = 0.75), indicating that students were committed to maintaining effort and focus. Other areas such as the Study Environment (M = 3.06, SD = 0.78), Online Social Support (M = 3.06, SD = 0.74), and metacognition (M = 3.05, SD = 0.80) show that students effectively manage their learning environment, seek support, and reflect on their learning processes. Planning and Time Management (M = 3.03, SD = 0.81) and Online Academic Self-Efficacy (M = 3.01, SD = 0.79) suggest that students feel capable and organized in their online studies. Indicators such as Online Intrinsic Motivation (M = 2.98, SD = 0.82) and Online Extrinsic Motivation (M = 3.01, SD = 0.82) reveal that students are driven by both personal interest and external factors. However, the presence of negative online achievement emotions (M = 3.02, SD = 0.81) indicates some negative emotional experiences related to online learning. Overall, the mean score across all dimensions was 3.05, with a standard deviation of 0.78, suggesting that students generally self-regulated their online learning effectively, with some variability in specific areas. 3. Influence of the Technology Pedagogical and Content Knowledge (TPaCK) of Instructors to the Self-Regulated Learning-Online (SRL-O) of the Students The relationship between the Technological Pedagogical and Content Knowledge (TPaCK) of instructors and the Self-Regulated Learning-Online (SRL-O) of students was examined to determine the extent to which instructors’ knowledge influences students’ learning behaviors in the online setting. Table 3 presents the statistical analysis that explores this relationship through regression analysis, highlighting the interaction between instructional competence and students’ capacity for self-regulated learning. Table 3 Regression Analysis Independent Variables Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta Interpretation TPACK 0.317 0.025 0.95 12.512 0.000 TPaCK of instructors significantly affect the SRL-O of the students r = 0.950 R-squared = 0.902 F-value = 156.561 p-value = 0.000 alpha = 0.05 Dependent Variable: SRL-O The results of the regression analysis, as shown in Table 3 , examine the relationship between the independent variable, Technological Pedagogical Content Knowledge (TPaCK) of instructors, and the dependent variable, Self-Regulated Learning-Online (SRL-O) of students. The unstandardized coefficient (B = 0.317) and standardized coefficient (Beta = 0.95) indicate a strong positive relationship between instructors’ TPACK and students’ SRL-O. The t-value (t = 12.512) and the corresponding significance level (p = 0.000) show that this effect is statistically significant, meaning that instructors’ TPACK significantly affects students’ OSRL. With an r-value of 0.950 and an R-squared value of 0.902, the model explains 90.2% of the variance in students' OSRL based on their instructors’ TPACK, further reinforcing the strong influence of instructor knowledge on student self-regulated learning. The F-value (F = 156.561) and p-value (p = 0.000) also indicated that the overall model was statistically significant. Since the p-value is less than the alpha level of 0.05, the relationship between instructors’ TPACK and SRL-O is highly significant. 4. Proposed Training Program to enhance Technological Pedagogical and Content Knowledge (TPaCK) of the Instructors and Improve the Online Self-Regulated Learning (O-SRL) of the Students The ever-evolving landscape of education demands that instructors not only master content, but also integrate technology and pedagogy effectively, while students must adapt to the online learning environment with strong self-regulation. This proposed training program addresses two critical areas identified through recent assessments: instructors’ Technological Pedagogical and Content Knowledge (TPaCK) and the Self-Regulated Learning-Online (SRL-O) of students. Improving TPaCK for Instructors The data revealed that instructors currently have only a moderate understanding of how to integrate technology with pedagogy and content. The lowest means from the TPaCK assessment (ranging from 3.07 to 3.14) point to gaps in areas such as Technology Pedagogy Content Knowledge (TPCK), Technology Content Knowledge (TCK), Technology Knowledge (TK), Pedagogy Content Knowledge (PCK), and Technology Pedagogy Knowledge (TPK). These gaps hinder instructors' ability to design and implement innovative, technology-driven instruction that engages students and enhances their learning outcomes. This training program was designed to equip instructors with the necessary skills to effectively integrate technology with pedagogy and content. Through workshops, hands-on training, and collaborative teaching exercises, instructors can become more proficient in utilizing technology to enhance their teaching strategies, leading to improved student engagement and learning outcomes. Each of these activities aims to increase instructors' confidence and competence in incorporating educational technologies into their lessons, thereby fostering a more dynamic and interactive learning environment. Enhancing SRL-O for Students On the student side, the assessment of their Self-Regulated Learning-Online (SRL-O) shows weaknesses in intrinsic motivation, extrinsic motivation, academic self-efficacy, negative achievement emotions, and planning and time management, with means ranging from 2.98 3.03. These areas indicate that students may struggle with the self-discipline, motivation, and emotional regulation required for success in an online learning environment. The lack of intrinsic motivation, in particular, points to a disengagement with the learning process, while the presence of negative emotions and weak time management skills further impedes their ability to succeed in online education. This programme aims to provide students with the tools and strategies they need to thrive in online learning environments. Through motivational talks, workshops on emotional regulation, and time management activities, students develop stronger self-regulation skills, enhance their confidence in online learning, and manage negative emotions more effectively. These skills are essential for students to take ownership of their learning, stay engaged, and achieve success in the digital learning context. Alignment with Institutional Goals By addressing the lowest-performing areas of TPaCK and SRL-O, this program directly supports the institution's mission to provide high-quality, technology-driven education and to promote student success. The outcomes expected from this program include improved teaching practices that leverage technology effectively, and a student body that is better equipped to manage the challenges of online learning. These improvements will ultimately contribute to the overall academic performance and satisfaction of both the instructors and students. This comprehensive training program is not just a response to identified weaknesses, but a proactive step towards fostering a more engaged, motivated, and technologically adept learning community. It represents an investment in the long-term growth and development of both instructors and students, ensuring that they are well prepared to meet the demands of 21st-century education. Figure 1 . Training Program to enhance Technological Pedagogical and Content Knowledge (TPaCK) of the Instructors and Improve the Online Self-Regulated Learning (O-SRL) of the Students Figure 1 identifies the major weaknesses, proposed solutions, expected outcomes, and institutional alignment for both components of the program. For TPaCK improvement, the identified weakness is instructors’ moderate understanding of technology integration . To address this, the matrix proposes workshops, hands-on training, and collaborative exercises as the main interventions. These activities aim to strengthen instructors’ ability to effectively integrate technology with pedagogy and content. The expected outcome is improved teaching practices leveraging technology , which aligns with the institution’s mission of delivering high-quality education . For SRL-O improvement, the noted weaknesses include low motivation, limited self-efficacy, and poor time management among students. The proposed solutions consist of motivational talks, emotional regulation workshops, and time management activities . These interventions are intended to build stronger self-regulation skills and greater confidence in students as they navigate online learning. Like the TPaCK component, this outcome also aligns with the institution’s commitment to high-quality education and holistic student development . The matrix illustrates a cohesive framework that links identified needs to actionable strategies and expected outcomes. It emphasizes a dual focus—empowering instructors through enhanced technological competence and equipping students with stronger self-regulation skills—to ensure a sustainable and technology-driven learning environment. Discussion The results indicate that instructors generally possess "some knowledge" across various components of the Technological Pedagogical and Content Knowledge (TPaCK) framework, as reflected by an overall mean score of 3.12. Instructors rated themselves as most confident in Pedagogical Content Knowledge (PC) and Content Knowledge (CK), with a mean of 3.15. The integration of Pedagogical Content Knowledge (PC) and Content Knowledge (CK) is essential for effective teaching in the digital age, enabling educators to design engaging learning experiences that align with student needs (Ajani, 2024). However, Galindo's (2023) study revealed that teachers have moderately low levels of content knowledge. This suggests that teachers may lack confidence in their subject-matter expertise, which is essential for effective teaching. The instructors were less confident in the (TPCK), which had the lowest mean score of 3.07. Although both novice and experienced translation instructors at Saudi universities generally hold positive attitudes toward TPACK, their confidence is limited by a moderate level of knowledge and the need for additional professional development (Alian & Alhaj, 2024). Kwakye's (2017) study revealed that while student teachers possess Technological Knowledge, they lack technological pedagogical and content knowledge. Furthermore, the study found that they lacked Technological Pedagogical Content Knowledge. The findings of these studies suggest that, while instructors have a foundational understanding of pedagogy, content, and technology, there is room for growth in integrating these elements, particularly in effectively using technology in teaching practices. Strengthening instructors' abilities to combine content, pedagogy, and technology into a cohesive strategy will enhance their teaching performance and benefit students, particularly self-regulated learners. The students' self-regulation skills across various domains, such as academic self-efficacy, intrinsic and extrinsic motivation, and metacognitive strategies, showed generally positive results, with mean scores averaging above 3.00 (out of 4.00). The highest score was recorded for task strategies (M=3.16), highlighting that students often employ strategies, such as creating examples and organizing their thoughts, to enhance their learning. Ganieva et al. (2020) revealed that English as a Foreign Language (EFL) learners demonstrated a high level of engagement with task strategies. These strategies are vital for effectively managing specific learning tasks, which are particularly important in an online learning environment where students frequently work independently. However, online intrinsic motivation (M=2.98) scored slightly lower, suggesting that, while students are motivated, external factors still play a critical role in their engagement with online learning. Öztürk and Türker (2024) suggested that intrinsic motivation influences how pre-service teachers approach their online learning preparation. Through self-motivation, these students are likely to improve their engagement and dedication to the learning process. The findings also showed that effort regulation (M=3.12) and task strategies (M=3.16) were strong indicators of the students' ability to remain focused and diligent in online learning. Despite the challenges of online education, students consistently reported applying themselves, even in the face of distractions or difficulties. These behaviors are crucial for academic success in online settings, as they demonstrate a commitment to overcoming the inherent challenges of distance learning, such as isolation or reduced direct support from instructors. Öztürk and Türker (2024) revealed that effort regulation and task strategies, including effort management, self-discipline, task completion, and self-testing, are essential elements of self-regulated learning strategies used by pre-service teachers in a blended online learning environment. Students also reported positive engagement in planning and time management (M=3.03), and metacognitive strategies (M=3.05). This suggests that most students are aware of how to effectively structure their time and reflect on their learning processes. Darmiany et al. (2024) emphasized the significance of goal setting as a key self-regulated learning (SRL) strategy. Students who set clear learning goals tended to exhibit stronger planning skills, which are crucial for effectively managing their time and resources. However, the fact that planning and time management scored slightly lower than other domains indicates that students may still face difficulties in fully optimizing their study schedules for online learning. Su and Fung (2024) suggest that students may face challenges in optimizing their study schedules due to the complex relationship between emotional intelligence and self-regulated learning, its effect on academic performance, and the necessity for sufficient support from academic staff. Online social support (M=3.06) and study environment (M=3.06) scores suggested that students generally had access to supportive peers and teachers and a conducive learning environment. The data indicate that students actively seek and provide assistance to one another in online settings by utilizing tools such as discussion boards, social media, and email to foster communication. This highlights the importance of creating collaborative opportunities in online courses to sustain students’ engagement and motivation. Dewi et al. (2023) revealed that social support from peers and instructors, along with a conducive study environment, significantly enhances self-regulated learning. Features such as feedback, collaboration, online tools, and structured curricula in online writing classes improve students' engagement, with most reporting a high perception of self-regulated learning and access to resources. The presence of negative emotions (M=3.02) was also evident, with students reporting feelings of anxiety, helplessness, and stress related to online studies. This indicates that, while students are capable of self-regulation, the emotional toll of online learning cannot be overlooked. The need to manage anxiety and negative emotions, especially in an isolated online environment, may hinder students' ability to fully engage with course materials and achieve academic success. Dong et al. (2023) revealed several factors affecting students' emotions in online learning. Cognitive overload can cause frustration, whereas a lack of motivation stems from boredom or disinterest. Limited autonomy and inadequate feedback contribute to stress and confusion. In addition, low perceived control and social pressure can lead to anxiety, thereby hindering self-regulation. The results suggest that, while students generally exhibit strong self-regulation in online learning, there are areas where improvements can be made, particularly in intrinsic motivation and managing negative emotional experiences. Furthermore, instructors' TPACK significantly influences students' capacity for self-regulation, highlighting the need for continuous professional development in this area to ensure that instructors are well-equipped to support students' online learning journeys. This study highlights several critical findings regarding the relationship between instructors' Technological Pedagogical Content Knowledge (TPACK) and students' Self-Regulated Learning-Online (SRL-O). The overall results suggest that instructors' mastery of TPACK plays a significant role in fostering self-regulated learning among students in an online environment. Regression analysis showed a strong and significant positive correlation between instructors' TPACK and students' SRL-O, with an R-squared value of 0.902. This indicates that 90.2% of the variance in students' ability to self-regulate their online learning can be attributed to their instructors' proficiency in TPACK. The high beta coefficient (0.95) underscores that instructors' integration of technology, pedagogy, and content knowledge directly enhances students' ability to manage their own learning processes. This finding emphasizes the importance of well-rounded teacher expertise for the effective delivery of online education. Sulistiani et al.'s (2024) study, which focused on pre-service elementary teachers, found that self-regulation and its indicators were positively and significantly correlated with both technology integration self-efficacy and pre-service teachers' TPACK competence. Conclusion and Recommendations This study explores the influence of instructors’ Technological Pedagogical Content Knowledge (TPACK) on students' Self-Regulated Learning-Online (SRL-O) during online learning. The results demonstrated a strong positive influence of instructors' TPACK and students' ability to effectively regulate their own learning. With an R-squared value of 0.902, instructors' ability to integrate technology with pedagogy and content knowledge significantly enhances students' capacity to manage their learning processes in an online environment. Students generally exhibited strong self-regulation skills, particularly in the areas of task strategy, effort regulation, and social support. However, intrinsic motivation and management of negative emotions emerged as areas needing improvement, as students often reported feelings of anxiety and helplessness during online learning. This indicates that, while students have developed strategies to succeed in online learning, emotional challenges persist, which may affect their overall learning experience and performance. These findings suggest that fostering robust TPACK among instructors can directly benefit students' self-regulated learning capacities. The results also highlight the importance of providing emotional support and fostering intrinsic motivation among students to improve their overall learning experiences in online settings. Limitations and Recommendations of the Study To improve online learning outcomes, institutions should focus on enhancing instructors' Technological Pedagogical Content Knowledge (TPACK) through continuous professional development as it significantly influences students' self-regulated learning. Conducting the same study with college instructors across programs is recommended. Additionally, offering mental health support and strategies to manage anxiety can help address the emotional challenges that students face in online learning. Finally, integrating more interactive and engaging elements into courses will boost students' intrinsic motivation and overall engagement. References Ajani, O. A. (2024). Technological pedagogical content knowledge for twenty-first century learning skills. International Journal of Research in Business and Social Science (2147-4478) , 13 (4), 468–476. https://doi.org/10.20525/ijrbs.v13i4.3355 Alian, E., & Alhaj, A. A. M. (2024). Perceptions of Novice and Experienced Instructors of Translation at Selected Saudi Universities Toward Technological Pedagogical Content Knowledge for Teaching Professional Development. Theory and Practice in Language Studies , 14 (3), 844–853. https://doi.org/10.17507/tpls.1403.27 Brinkley-Etzkorn, K. E. (2018). Learning to teach online: Measuring the influence of faculty development training on teaching effectiveness through a TPACK lens. The Internet and Higher Education , 38 , 28–35. https://doi.org/10.1016/j.iheduc.2018.04.004 Brinkley-Etzkorn, K. E. (2020). The Effects of Training on Instructor Beliefs about and Attitudes toward Online Teaching. American Journal of Distance Education , 34 (1), 19–35. https://doi.org/10.1080//08923647.2020.1692553 Broadbent, J., E. Panadero, Lodge, J. M., & M. Fuller-Tyszkiewicz. (2022). The self-regulation for learning online (SRL-O) questionnaire. Metacognition and Learning , 18 (1), 135–163. https://doi.org/10.1007/s11409-022-09319-6 Darmiany, D., Istiningsih, S., Nurmawanti, I., Nurwahidah, N., & Mauldya, M. A. (2024). Student’s Self-Regulated Learning in Online Class Design Based on Reflective Learning. AL-ISHLAH Jurnal Pendidikan , 16 (1), 440–449. https://doi.org/10.35445/alishlah.v16i1.3947 Dewi, S., Masitoh, F., Afifi, N., & Qamaria, R. S. (2023). Students’ Perception Toward Self-Regulated Learning in Online Writing Class. Metathesis Journal of English Language Literature and Teaching , 7 (2), 229–239. https://doi.org/10.31002/metathesis.v7i2.159 Dong, X., Yuan, H., Xue, H., Li, Y., Jia, L., Chen, J., Shi, Y., & Zhang, X. (2023). Factors influencing college students’ self-regulated learning in online learning environment: A systematic review. Nurse Education Today , 133 , 106071–106071. https://doi.org/10.1016/j.nedt.2023.106071 Edisherashvili, N., Saks, K., Pedaste, M., & Leijen, Ä. (2022). Supporting Self-Regulated Learning in Distance Learning Contexts at Higher Education Level: Systematic Literature Review. Frontiers in Psychology , 12 . https://doi.org/10.3389/fpsyg.2021.792422 Galindo, J. N. (2023). Technological Pedagogical and Content Knowledge (TPACK) Assessment of Basic Education Teachers in St. Paul University Surigao. Cognizance Journal , 3 (6), 368–378. https://doi.org/10.47760/cognizance.2023.v03i06.023 Ganieva, M., Khorokhorina, G., Pletneva, N., & Fomina, S. (2020). EFL Students’ Use of Self-Regulated Learning Strategies in Online Educational Setting. 2020 the 4th International Conference on Education and Multimedia Technology , 3 , 156–160. https://doi.org/10.1145/3416797.3416834 Koehler, M. J. (2016). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge - Punya Mishra, Matthew J. Koehler, 2006 . Teachers College Record. https://journals.sagepub.com/doi/10.1111/j.1467-9620.2006.00684.x Kwakye, S. (2017). Technological Pedagogical Content Knowledge Preparedness of Student-Teachers of the Department of Arts and Social Sciences Education of University of Cape Coast. Journal of Education and Practice , 8 (10). https://files.eric.ed.gov/fulltext/EJ1139820.pdf Ni, K., Santosa, M. H., & Virginiya, T. (2023). Investigation of Hospitality and Business School English Instructors’ TPACK in the Online Learning Context. ResearchGate , 6 (3), 671–681. https://doi.org/10.33503/journey.v6i3.3748 Öztürk, M., & Türker, P. M. (2024). Analysis of self-regulated learning strategies used by online learners . E-Learning and Digital Media . https://doi.org/10.1177/2042753024125142 Schmid, M., Brianza, E., & Petko, D. (2020). Developing a short assessment instrument for Technological Pedagogical Content Knowledge (TPACK.xs) and comparing the factor structure of an integrative and a transformative model. Computers & Education , 157 , 103967–103967. https://doi.org/10.1016/j.compedu.2020.103967 Sim, M. S., Siok, T. H., Shuang, G. C., Sarifah, T., & Rahmat, N. H. (2023). Self- regulated Learners: What Drives Them? International Journal of Academic Research in Business and Social Sciences , 13 (3), 1152–1170. http://dx.doi.org/10.6007/IJARBSS/v13-i3/16476 Su, S. I., & Fung, C. Y. (2024). EXPLORING THE ROLES OF EMOTIONAL INTELLIGENCE AND SELF-REGULATED LEARNING DURING ONLINE LEARNING. International Journal of Modern Education , 6 (20), 58–68. https://doi.org/10.35631/ijmoe.620005 Sulistiani, I. R., Setyosari, P., Sa’dijah, C., & Praherdhiono, H. (2024). Technological Pedagogical Content Knowledge of Preservice Elementary Teachers: Relationship to Self-Regulation and Technology Integration Self-Efficacy. European Journal of Educational Research , 13 (1), 159–170. https://doi.org/10.12973/eu-jer.13.1.159 Willermark, S. (2016). Technological Pedagogical and Content Knowledge: A Review of Empirical Studies Published From 2011 to 2016 - Sara Willermark, 2018 . Journal of Educational Computing Research; Sage Journals. https://journals.sagepub.com/doi/10.1177/0735633117713114 Zhang, S., Liu, Q., & Cai, Z. (2019). Exploring primary school teachers’ technological pedagogical content knowledge (TPACK) in online collaborative discourse: An epistemic network analysis. British Journal of Educational Technology , 50 (6), 3437–3455. https://doi.org/10.1111/bjet.12751 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-8800976","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586599494,"identity":"90ed223b-5a87-4f42-9310-f2cca9623100","order_by":0,"name":"Joseline M. Santos, PhD","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6652-3117","institution":"Bulacan State University","correspondingAuthor":true,"prefix":"","firstName":"Joseline","middleName":"M.","lastName":"Santos","suffix":"PhD"}],"badges":[],"createdAt":"2026-02-05 21:56:34","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8800976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8800976/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102240146,"identity":"31260ccd-c9d9-4667-8a68-8dc5f77461f1","added_by":"auto","created_at":"2026-02-09 16:49:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTraining Program to enhance Technological Pedagogical and Content Knowledge (TPaCK) of the Instructors and Improve the Online Self-Regulated Learning (O-SRL) of the Students\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 identifies the major weaknesses, proposed solutions, expected outcomes, and institutional alignment for both components of the program.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8800976/v1/aa28d3c909c72feebc3720be.png"},{"id":102962051,"identity":"69284199-8675-4935-b7eb-fa19ab699dc5","added_by":"auto","created_at":"2026-02-19 03:57:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":939907,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8800976/v1/fc4bb3cb-c051-4a79-baeb-e2184ef794a4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Influence of Instructors' Technological Pedagogical and Content Knowledge (TPaCK) on the Self-Regulated Learning-Online (SRL-O) of the Students\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid shift to online learning accelerated by the global pandemic has transformed the educational landscape, placing significant emphasis on the need for effective teaching strategies in virtual environments. Online learning, characterized by its flexibility and accessibility, has simultaneously introduced challenges in student engagement, motivation, and self-regulation. Self-regulated learning (SRL), a process in which learners actively control their cognitive, metacognitive, and motivational processes, has emerged as a critical factor in academic success in online environments (Edisherashvili et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, the role of instructors becomes crucial, as they are not only facilitators of content, but also designers of learning experiences that foster autonomy, engagement, and academic achievement.\u003c/p\u003e \u003cp\u003eThe Technological Pedagogical and Content Knowledge (TPaCK) framework (Koehler, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), offers a comprehensive lens for examining how instructors integrate technology with pedagogical and content expertise in their teaching practices. TPaCK encompasses three core domains\u0026ndash;technology knowledge (TK), pedagogy knowledge (PK), and content knowledge (CK)\u0026ndash;and their intersections, enabling educators to create meaningful learning experiences by strategically leveraging technological tools. In online learning environments, where the absence of physical proximity requires a more deliberate instructional design, TPaCK has become increasingly relevant. Instructors who effectively integrate TPaCK principles can enhance the learning environment and promote deeper student engagement, collaboration, and self-directed learning behaviors (Ni et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sim et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the increasing recognition of TPaCK\u0026rsquo;s importance in online education, there remains a gap in understanding how instructors' TPaCK proficiency directly influences students' SRL in online learning (SRL-O) performance. SRL-O, encompassing self-efficacy, intrinsic motivation, extrinsic motivation, negative achievement emotion, planning and time management, metacognition, effort regulation, social support, and task strategies, is essential for students to navigate the online learning landscape. Therefore, this study explores the influence of instructors\u0026rsquo; TPaCK capabilities on self-regulated learners in online learning (SRL-O) contexts.\u003c/p\u003e \u003cp\u003eIn the post-pandemic era, educational institutions have been compelled to explore alternative approaches to ensure continuity of learning, especially when onsite classes are disrupted. These disruptions may occur because of inclement weather, local events, or other unforeseen circumstances requiring the cancellation of face-to-face sessions. Online learning has emerged as a key modality that enables institutions to maintain educational delivery despite such interruptions. By leveraging digital platforms, schools and universities can ensure that learning remains uninterrupted, providing flexible and accessible solutions to meet the demands of modern education.\u003c/p\u003e \u003cp\u003eAfter reviewing related studies on TPaCK and SRL-O, the author observed that research involving college instructors across various programs is limited. Most studies on TPaCK have focused on pre-service teachers. Additionally, there is a lack of studies comparing instructors\u0026rsquo; TPaCK with students' SRL-O. This is the first study to explore whether instructors' TPaCK affects students' SRL-O.\u003c/p\u003e \u003cp\u003eBy investigating the influence of instructors' TPaCK on SRL-O performance, this study aims to provide insights into how the integration of technology, pedagogy, and content knowledge impacts students' self-regulatory behaviors in an online learning environment. These findings will contribute to the growing body of knowledge on online education and offer practical recommendations for improving teaching strategies that foster self-regulated learning, ultimately enhancing student success in digital learning environments.\u003c/p\u003e\n\u003ch3\u003eStatement of the Problem\u003c/h3\u003e\n\u003cp\u003eDespite the critical role of instructor readiness and student autonomy in the efficacy of online education, a significant empirical gap persists in understanding how instructors' specific integrated competencies influence student learning behaviors in digital environments. This study addresses this deficiency by first establishing the pedagogical context through a descriptive analysis of instructors' full Technological Pedagogical Content Knowledge (TPaCK) profile, detailing their capacity across seven domains (CK, PK, TK, PCK, TCK, TPK, and TPACK). Concurrently, the research characterizes students' Self-Regulated Learning in Online courses (SRL-O) across six dimensions, including goal setting, environment structuring, and self-evaluation. The central problem this investigation aims to resolve is determining the extent to which the instructors' multifaceted TPACK significantly influences the performance of students' SRL-O, culminating in the proposal of a comprehensive training program designed to strategically enhance both instructor TPACK and student SRL-O for improved online learning outcomes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eReview of Related Literature\u003c/h2\u003e \u003cp\u003eThe Technological Pedagogical Content Knowledge (TPACK) framework has emerged as a crucial model for integrating technology, pedagogy, and content knowledge in education, particularly in online learning environments. This review synthesizes the literature into two central themes: the impact of TPACK on teaching effectiveness and student engagement and the importance of ongoing support and tailored development for educators in applying TPACK.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTPACK and Teaching Effectiveness in Online Learning\u003c/h3\u003e\n\u003cp\u003eSeveral studies have underscored the effectiveness of the TPACK framework in enhancing teaching practices, particularly in online courses. Instructors who have undergone TPACK-based training have demonstrated significant improvement in their ability to design and deliver online courses. Brinkley-Etzkorn (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) observed that TPACK-trained instructors developed more effective course designs, reflecting deeper integration of technology with pedagogy and content. However, these improvements were not always mirrored in student evaluations, suggesting that, while TPACK enhances instructional quality, its immediate impact on student perception may be less evident. Nonetheless, the framework clearly supports the development of teaching strategies that foster student engagement, self-regulated learning (SRL), and better learning outcomes, especially in technology-driven environments (Willermark, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eActive involvement in lesson design also plays a pivotal role in the effective application of the TPACK. Instructors who actively design technology-enhanced lessons tend to better integrate TPACK components, resulting in more meaningful and engaging learning experiences for students (Brinkley-Etzkorn, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This hands-on approach allows instructors to explore and experiment with technology in ways that align with pedagogical goals and content delivery. By doing so, TPACK-trained instructors are better equipped to promote SRL strategies among students, which is critical for success in online learning environments where self-directed study is key.\u003c/p\u003e\n\u003ch3\u003eOngoing Support and Tailored Development for Effective TPACK Integration\u003c/h3\u003e\n\u003cp\u003eWhile TPACK training boosts instructor confidence and competence in using technology, research shows that initial gains in optimism often diminish once instructors begin teaching online (Brinkley-Etzkorn, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This highlights the importance of providing ongoing support to help educators continuously refine their TPACK skills and adapt to the dynamic challenges in online education. Without sustained professional development, instructors may struggle to maintain confidence and effectiveness during initial TPACK training. Continuous training, mentorship, and peer collaboration can provide the long-term support needed by instructors to fully leverage TPACK in their teaching practices.\u003c/p\u003e \u003cp\u003eLiterature on TPACK demonstrates its crucial role in enhancing teaching effectiveness and student engagement, particularly in online learning environments. However, for instructors to fully benefit from TPACK, ongoing support and tailored development are necessary to sustain confidence and competence. Active participation in lesson design is vital for successfully integrating TPACK, whereas differences in knowledge domain integration highlight the need for differentiated approaches to professional development.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe study utilized a quantitative approach to research using a descriptive-correlational approach. The TPACK of the instructors and the SRL for online learning were determined using survey questionnaires. The TPACK instrument consists of a total of 28 items, with four items each for pedagogical content (pk), content knowledge (ck), technology knowledge (tk), pedagogical content knowledge (pck), technology pedagogy knowledge (tpk), technology content knowledge (tck), and technology pedagogy and content knowledge (tpck). The instrument was adopted from the study of Schmid et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) which undergone the test of reliability wherein each of the seven subscales emerged as reliable, with Cronbach's alphas between .77 and .91 and McDonald's omegas between 0.79 and 0.92. The SRL for Online Learning was adopted from the study by Broadbent et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) on the development of self-regulation for learning online (SRL-O) questionnaire. These data were used to test statistically whether the TPACK of the instructor significantly influenced the SRL of the students in online learning. This study was conducted on the main campus and five other external campuses of a state university in Bulacan.\u003c/p\u003e \u003cp\u003eThe respondents available during the data collection period were 299 faculty members and 381 students. The faculty members based on their sex at birth revealed that the majority were female, accounting for 52.51% (157 respondents), while male respondents constituted 47.49% (142 respondents).\u003c/p\u003e \u003cp\u003eThe students, based on their sex at birth, showed a slight majority of 53.28% (203 respondents) in one group, whereas the other group represented 46.72% (178 respondents). The total number of respondents is 381. This nearly equal distribution indicates a well-balanced sample in terms of sex, allowing for a representative analysis of the responses across both groups. These close percentages suggest minimal disparity, which can help ensure a more inclusive and diverse understanding of the data.\u003c/p\u003e \u003cp\u003e The study strictly adhered to ethical research standards to ensure the rights, privacy, and well-being of all participants. Prior to data collection, formal approval was obtained from the university's ethics review committee. Informed consent was secured from all participants, emphasizing the voluntary nature of their participation and their right to withdraw at any time without penalty. The anonymity and confidentiality of the respondents were maintained throughout the research process by assigning unique codes and securing all data in password-protected files. Additionally, the researchers ensured that the information gathered was used solely for academic and research purposes. All procedures followed were in accordance with ethical guidelines for research involving human participants.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section presents the findings of the study on the Technological Pedagogical and Content Knowledge (TPACK) of instructors and the Self-Regulated Learning in Online Learning (SRL-O) of students. It includes the statistical analyses conducted to determine the level of instructors\u0026rsquo; TPACK and students\u0026rsquo; SRL-O, as well as the relationship between these two variables. The section also outlines the proposed training program developed based on the results to enhance instructors\u0026rsquo; TPACK and improve students\u0026rsquo; SRL-O.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eTechnological Pedagogical and Content Knowledge (TPACK) of the Instructors\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\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\u003e1. Pedagogical Content (PC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHas some knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Content Knowledge (CK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHas some knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Technology Knowledge (TK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHas some knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Pedagogy Content Knowledge (PCK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHas some knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. Technology Pedagogy Knowledge (TPK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHas some knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Technology Content Knowledge (TCK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHas some knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. Technology Pedagogy Content Knowledge (TPCK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHas some knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOVERALL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHas some knowledge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the instructors\u0026apos; self-assessment of their Technological Pedagogical and Content Knowledge (TPACK), with an overall mean score of 3.12 and a standard deviation of 0.85, indicating that they \u0026quot;have some knowledge\u0026quot; in all key areas of TPACK. Pedagogical Content Knowledge (PC) and Content Knowledge (CK) both scored the highest mean of 3.15, suggesting that instructors feel moderately confident in their understanding of pedagogy and content. Technology Knowledge (TK) scored slightly lower at 3.11, indicating a similar but slightly less confident perception of technological skills. The lowest score (mean\u0026thinsp;=\u0026thinsp;3.07) is for Technology Pedagogy Content Knowledge (TPCK), which reflects the integration of technology, pedagogy, and content in teaching. This suggests that, while instructors feel that they have some knowledge in all areas, their ability to fully integrate these elements into a cohesive teaching strategy may require further development. Overall, the results highlight that instructors possess foundational knowledge across the TPACK framework, but may benefit from additional training to strengthen their integration of technology into their teaching practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Self-Regulated Learning for Online Learning of the Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings on students\u0026rsquo; Self-Regulated Learning in Online Learning (SRL-O) encompass various dimensions, including online academic self-efficacy, motivation, effort regulation, metacognition, study environment, and task strategies. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the data that illustrate how students organize, monitor, and control their learning behaviors within the online learning environment.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eSelf-Regulated Learning-Online (SRL-O) of the Students\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eSelf-Regulated Learning-Online (SRL-O) of the Students\u003c/em\u003e\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\u003e\u003cstrong\u003eIndicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Online Academic Self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Online Intrinsic Motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Online Extrinsic Motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Online Negative Achievement Emotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. Planning and time management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Metacognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. Study Environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8. Online Effort Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9. Online Social Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10. Online Task Strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue of me\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOVERALL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrue of me\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe overall Self-Regulated Learning-Online (SRL-O) of students across various dimensions is summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean scores for the different indicators indicate that students generally agree with the statements across all dimensions, as each is described as \u0026quot;True of me\u0026rsquo;.\u0026quot; The indicator with the highest mean score was Online Task Strategies (M\u0026thinsp;=\u0026thinsp;3.16, SD\u0026thinsp;=\u0026thinsp;0.67), suggesting that students often employed proactive strategies for their online learning tasks. Online Effort Regulation followed closely (M\u0026thinsp;=\u0026thinsp;3.12, SD\u0026thinsp;=\u0026thinsp;0.75), indicating that students were committed to maintaining effort and focus.\u003c/p\u003e\n\u003cp\u003eOther areas such as the Study Environment (M\u0026thinsp;=\u0026thinsp;3.06, SD\u0026thinsp;=\u0026thinsp;0.78), Online Social Support (M\u0026thinsp;=\u0026thinsp;3.06, SD\u0026thinsp;=\u0026thinsp;0.74), and metacognition (M\u0026thinsp;=\u0026thinsp;3.05, SD\u0026thinsp;=\u0026thinsp;0.80) show that students effectively manage their learning environment, seek support, and reflect on their learning processes. Planning and Time Management (M\u0026thinsp;=\u0026thinsp;3.03, SD\u0026thinsp;=\u0026thinsp;0.81) and Online Academic Self-Efficacy (M\u0026thinsp;=\u0026thinsp;3.01, SD\u0026thinsp;=\u0026thinsp;0.79) suggest that students feel capable and organized in their online studies.\u003c/p\u003e\n\u003cp\u003eIndicators such as Online Intrinsic Motivation (M\u0026thinsp;=\u0026thinsp;2.98, SD\u0026thinsp;=\u0026thinsp;0.82) and Online Extrinsic Motivation (M\u0026thinsp;=\u0026thinsp;3.01, SD\u0026thinsp;=\u0026thinsp;0.82) reveal that students are driven by both personal interest and external factors. However, the presence of negative online achievement emotions (M\u0026thinsp;=\u0026thinsp;3.02, SD\u0026thinsp;=\u0026thinsp;0.81) indicates some negative emotional experiences related to online learning.\u003c/p\u003e\n\u003cp\u003eOverall, the mean score across all dimensions was 3.05, with a standard deviation of 0.78, suggesting that students generally self-regulated their online learning effectively, with some variability in specific areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Influence of the Technology Pedagogical and Content Knowledge (TPaCK) of Instructors to the Self-Regulated Learning-Online (SRL-O) of the Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between the Technological Pedagogical and Content Knowledge (TPaCK) of instructors and the Self-Regulated Learning-Online (SRL-O) of students was examined to determine the extent to which instructors\u0026rsquo; knowledge influences students\u0026rsquo; learning behaviors in the online setting. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the statistical analysis that explores this relationship through regression analysis, highlighting the interaction between instructional competence and students\u0026rsquo; capacity for self-regulated learning.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eRegression Analysis\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndependent Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPACK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPaCK of instructors significantly affect the SRL-O of the students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u0026thinsp;=\u0026thinsp;0.950\u003c/strong\u003e\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-squared\u0026thinsp;=\u0026thinsp;0.902\u003c/strong\u003e\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u0026thinsp;=\u0026thinsp;156.561\u003c/strong\u003e\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u0026thinsp;=\u0026thinsp;0.000\u003c/strong\u003e\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ealpha\u0026thinsp;=\u0026thinsp;0.05\u003c/strong\u003e\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eDependent Variable: SRL-O\u003c/em\u003e\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 \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the regression analysis, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, examine the relationship between the independent variable, Technological Pedagogical Content Knowledge (TPaCK) of instructors, and the dependent variable, Self-Regulated Learning-Online (SRL-O) of students. The unstandardized coefficient (B\u0026thinsp;=\u0026thinsp;0.317) and standardized coefficient (Beta\u0026thinsp;=\u0026thinsp;0.95) indicate a strong positive relationship between instructors\u0026rsquo; TPACK and students\u0026rsquo; SRL-O. The t-value (t\u0026thinsp;=\u0026thinsp;12.512) and the corresponding significance level (p\u0026thinsp;=\u0026thinsp;0.000) show that this effect is statistically significant, meaning that instructors\u0026rsquo; TPACK significantly affects students\u0026rsquo; OSRL.\u003c/p\u003e\n\u003cp\u003eWith an r-value of 0.950 and an R-squared value of 0.902, the model explains 90.2% of the variance in students\u0026apos; OSRL based on their instructors\u0026rsquo; TPACK, further reinforcing the strong influence of instructor knowledge on student self-regulated learning. The F-value (F\u0026thinsp;=\u0026thinsp;156.561) and p-value (p\u0026thinsp;=\u0026thinsp;0.000) also indicated that the overall model was statistically significant. Since the p-value is less than the alpha level of 0.05, the relationship between instructors\u0026rsquo; TPACK and SRL-O is highly significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Proposed Training Program to enhance Technological Pedagogical and Content Knowledge (TPaCK) of the Instructors and Improve the Online Self-Regulated Learning (O-SRL) of the Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ever-evolving landscape of education demands that instructors not only master content, but also integrate technology and pedagogy effectively, while students must adapt to the online learning environment with strong self-regulation. This proposed training program addresses two critical areas identified through recent assessments: instructors\u0026rsquo; Technological Pedagogical and Content Knowledge (TPaCK) and the Self-Regulated Learning-Online (SRL-O) of students.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eImproving TPaCK for Instructors\u003c/h2\u003e\n \u003cp\u003eThe data revealed that instructors currently have only a moderate understanding of how to integrate technology with pedagogy and content. The lowest means from the TPaCK assessment (ranging from 3.07 to 3.14) point to gaps in areas such as Technology Pedagogy Content Knowledge (TPCK), Technology Content Knowledge (TCK), Technology Knowledge (TK), Pedagogy Content Knowledge (PCK), and Technology Pedagogy Knowledge (TPK). These gaps hinder instructors\u0026apos; ability to design and implement innovative, technology-driven instruction that engages students and enhances their learning outcomes.\u003c/p\u003e\n \u003cp\u003eThis training program was designed to equip instructors with the necessary skills to effectively integrate technology with pedagogy and content. Through workshops, hands-on training, and collaborative teaching exercises, instructors can become more proficient in utilizing technology to enhance their teaching strategies, leading to improved student engagement and learning outcomes. Each of these activities aims to increase instructors\u0026apos; confidence and competence in incorporating educational technologies into their lessons, thereby fostering a more dynamic and interactive learning environment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eEnhancing SRL-O for Students\u003c/h2\u003e\n \u003cp\u003eOn the student side, the assessment of their Self-Regulated Learning-Online (SRL-O) shows weaknesses in intrinsic motivation, extrinsic motivation, academic self-efficacy, negative achievement emotions, and planning and time management, with means ranging from 2.98 3.03. These areas indicate that students may struggle with the self-discipline, motivation, and emotional regulation required for success in an online learning environment. The lack of intrinsic motivation, in particular, points to a disengagement with the learning process, while the presence of negative emotions and weak time management skills further impedes their ability to succeed in online education.\u003c/p\u003e\n \u003cp\u003eThis programme aims to provide students with the tools and strategies they need to thrive in online learning environments. Through motivational talks, workshops on emotional regulation, and time management activities, students develop stronger self-regulation skills, enhance their confidence in online learning, and manage negative emotions more effectively. These skills are essential for students to take ownership of their learning, stay engaged, and achieve success in the digital learning context.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eAlignment with Institutional Goals\u003c/h2\u003e\n \u003cp\u003eBy addressing the lowest-performing areas of TPaCK and SRL-O, this program directly supports the institution\u0026apos;s mission to provide high-quality, technology-driven education and to promote student success. The outcomes expected from this program include improved teaching practices that leverage technology effectively, and a student body that is better equipped to manage the challenges of online learning. These improvements will ultimately contribute to the overall academic performance and satisfaction of both the instructors and students.\u003c/p\u003e\n \u003cp\u003eThis comprehensive training program is not just a response to identified weaknesses, but a proactive step towards fostering a more engaged, motivated, and technologically adept learning community. It represents an investment in the long-term growth and development of both instructors and students, ensuring that they are well prepared to meet the demands of 21st-century education.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTraining Program to enhance Technological Pedagogical and Content Knowledge (TPaCK) of the Instructors and Improve the Online Self-Regulated Learning (O-SRL) of the Students\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFigure 1 identifies the major weaknesses, proposed solutions, expected outcomes, and institutional alignment for both components of the program.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eFor TPaCK improvement, the identified weakness is instructors\u0026rsquo; \u003cem\u003emoderate understanding of technology integration\u003c/em\u003e. To address this, the matrix proposes \u003cem\u003eworkshops, hands-on training, and collaborative exercises\u003c/em\u003e as the main interventions. These activities aim to strengthen instructors\u0026rsquo; ability to effectively integrate technology with pedagogy and content. The expected outcome is \u003cem\u003eimproved teaching practices leveraging technology\u003c/em\u003e, which aligns with the institution\u0026rsquo;s mission of delivering \u003cem\u003ehigh-quality education\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eFor SRL-O improvement, the noted weaknesses include \u003cem\u003elow motivation, limited self-efficacy, and poor time management\u003c/em\u003e among students. The proposed solutions consist of \u003cem\u003emotivational talks, emotional regulation workshops, and time management activities\u003c/em\u003e. These interventions are intended to build \u003cem\u003estronger self-regulation skills and greater confidence\u003c/em\u003e in students as they navigate online learning. Like the TPaCK component, this outcome also aligns with the institution\u0026rsquo;s commitment to \u003cem\u003ehigh-quality education and holistic student development\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eThe matrix illustrates a cohesive framework that links identified needs to actionable strategies and expected outcomes. It emphasizes a dual focus\u0026mdash;empowering instructors through enhanced technological competence and equipping students with stronger self-regulation skills\u0026mdash;to ensure a sustainable and technology-driven learning environment.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results indicate that instructors generally possess \"some knowledge\" across various components of the Technological Pedagogical and Content Knowledge (TPaCK) framework, as reflected by an overall mean score of 3.12. Instructors rated themselves as most confident in Pedagogical Content Knowledge (PC) and Content Knowledge (CK), with a mean of 3.15. The integration of Pedagogical Content Knowledge (PC) and Content Knowledge (CK) is essential for effective teaching in the digital age, enabling educators to design engaging learning experiences that align with student needs (Ajani, 2024). However, Galindo's (2023) study revealed that teachers have moderately low levels of content knowledge. This suggests that teachers may lack confidence in their subject-matter expertise, which is essential for effective teaching.\u003c/p\u003e\n\u003cp\u003eThe instructors were less confident in the (TPCK), which had the lowest mean score of 3.07. Although both novice and experienced translation instructors at Saudi universities generally hold positive attitudes toward TPACK, their confidence is limited by a moderate level of knowledge and the need for additional professional development (Alian \u0026amp; Alhaj, 2024). Kwakye's (2017) study revealed that while student teachers possess Technological Knowledge, they lack technological pedagogical and content knowledge. Furthermore, the study found that they lacked Technological Pedagogical Content Knowledge. The findings of these studies suggest that, while instructors have a foundational understanding of pedagogy, content, and technology, there is room for growth in integrating these elements, particularly in effectively using technology in teaching practices. Strengthening instructors' abilities to combine content, pedagogy, and technology into a cohesive strategy will enhance their teaching performance and benefit students, particularly self-regulated learners.\u003c/p\u003e\n\u003cp\u003eThe students' self-regulation skills across various domains, such as academic self-efficacy, intrinsic and extrinsic motivation, and metacognitive strategies, showed generally positive results, with mean scores averaging above 3.00 (out of 4.00). The highest score was recorded for task strategies (M=3.16), highlighting that students often employ strategies, such as creating examples and organizing their thoughts, to enhance their learning. Ganieva et al. (2020) revealed that English as a Foreign Language (EFL) learners demonstrated a high level of engagement with task strategies. These strategies are vital for effectively managing specific learning tasks, which are particularly important in an online learning environment where students frequently work independently. However, online intrinsic motivation (M=2.98) scored slightly lower, suggesting that, while students are motivated, external factors still play a critical role in their engagement with online learning. Öztürk and Türker (2024) suggested that intrinsic motivation influences how pre-service teachers approach their online learning preparation. Through self-motivation, these students are likely to improve their engagement and dedication to the learning process.\u003c/p\u003e\n\u003cp\u003eThe findings also showed that effort regulation (M=3.12) and task strategies (M=3.16) were strong indicators of the students' ability to remain focused and diligent in online learning. Despite the challenges of online education, students consistently reported applying themselves, even in the face of distractions or difficulties. These behaviors are crucial for academic success in online settings, as they demonstrate a commitment to overcoming the inherent challenges of distance learning, such as isolation or reduced direct support from instructors. Öztürk and Türker (2024) revealed that effort regulation and task strategies, including effort management, self-discipline, task completion, and self-testing, are essential elements of self-regulated learning strategies used by pre-service teachers in a blended online learning environment.\u003c/p\u003e\n\u003cp\u003eStudents also reported positive engagement in planning and time management (M=3.03), and metacognitive strategies (M=3.05). This suggests that most students are aware of how to effectively structure their time and reflect on their learning processes. Darmiany et al. (2024) emphasized the significance of goal setting as a key self-regulated learning (SRL) strategy. Students who set clear learning goals tended to exhibit stronger planning skills, which are crucial for effectively managing their time and resources. However, the fact that planning and time management scored slightly lower than other domains indicates that students may still face difficulties in fully optimizing their study schedules for online learning. Su and Fung (2024) suggest that students may face challenges in optimizing their study schedules due to the complex relationship between emotional intelligence and self-regulated learning, its effect on academic performance, and the necessity for sufficient support from academic staff.\u003c/p\u003e\n\u003cp\u003eOnline social support (M=3.06) and study environment (M=3.06) scores suggested that students generally had access to supportive peers and teachers and a conducive learning environment. The data indicate that students actively seek and provide assistance to one another in online settings by utilizing tools such as discussion boards, social media, and email to foster communication. This highlights the importance of creating collaborative opportunities in online courses to sustain students’ engagement and motivation. Dewi et al. (2023) revealed that social support from peers and instructors, along with a conducive study environment, significantly enhances self-regulated learning. Features such as feedback, collaboration, online tools, and structured curricula in online writing classes improve students' engagement, with most reporting a high perception of self-regulated learning and access to resources.\u003c/p\u003e\n\u003cp\u003eThe presence of negative emotions (M=3.02) was also evident, with students reporting feelings of anxiety, helplessness, and stress related to online studies. This indicates that, while students are capable of self-regulation, the emotional toll of online learning cannot be overlooked. The need to manage anxiety and negative emotions, especially in an isolated online environment, may hinder students' ability to fully engage with course materials and achieve academic success. Dong et al. (2023) revealed several factors affecting students' emotions in online learning. Cognitive overload can cause frustration, whereas a lack of motivation stems from boredom or disinterest. Limited autonomy and inadequate feedback contribute to stress and confusion. In addition, low perceived control and social pressure can lead to anxiety, thereby hindering self-regulation.\u003c/p\u003e\n\u003cp\u003eThe results suggest that, while students generally exhibit strong self-regulation in online learning, there are areas where improvements can be made, particularly in intrinsic motivation and managing negative emotional experiences. Furthermore, instructors' TPACK significantly influences students' capacity for self-regulation, highlighting the need for continuous professional development in this area to ensure that instructors are well-equipped to support students' online learning journeys.\u003c/p\u003e\n\u003cp\u003eThis study highlights several critical findings regarding the relationship between instructors' Technological Pedagogical Content Knowledge (TPACK) and students' Self-Regulated Learning-Online (SRL-O). The overall results suggest that instructors' mastery of TPACK plays a significant role in fostering self-regulated learning among students in an online environment.\u003c/p\u003e\n\u003cp\u003eRegression analysis showed a strong and significant positive correlation between instructors' TPACK and students' SRL-O, with an R-squared value of 0.902. This indicates that 90.2% of the variance in students' ability to self-regulate their online learning can be attributed to their instructors' proficiency in TPACK. The high beta coefficient (0.95) underscores that instructors' integration of technology, pedagogy, and content knowledge directly enhances students' ability to manage their own learning processes. This finding emphasizes the importance of well-rounded teacher expertise for the effective delivery of online education. Sulistiani et al.'s (2024) study, which focused on pre-service elementary teachers, found that self-regulation and its indicators were positively and significantly correlated with both technology integration self-efficacy and pre-service teachers' TPACK competence.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eThis study explores the influence of instructors’ Technological Pedagogical Content Knowledge (TPACK) on students' Self-Regulated Learning-Online (SRL-O) during online learning. The results demonstrated a strong positive influence of instructors' TPACK and students' ability to effectively regulate their own learning. With an R-squared value of 0.902, instructors' ability to integrate technology with pedagogy and content knowledge significantly enhances students' capacity to manage their learning processes in an online environment.\u003c/p\u003e\n\u003cp\u003eStudents generally exhibited strong self-regulation skills, particularly in the areas of task strategy, effort regulation, and social support. However, intrinsic motivation and management of negative emotions emerged as areas needing improvement, as students often reported feelings of anxiety and helplessness during online learning. This indicates that, while students have developed strategies to succeed in online learning, emotional challenges persist, which may affect their overall learning experience and performance.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that fostering robust TPACK among instructors can directly benefit students' self-regulated learning capacities. The results also highlight the importance of providing emotional support and fostering intrinsic motivation among students to improve their overall learning experiences in online settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Recommendations of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo improve online learning outcomes, institutions should focus on enhancing instructors' Technological Pedagogical Content Knowledge (TPACK) through continuous professional development as it significantly influences students' self-regulated learning. Conducting the same study with college instructors across programs is recommended. Additionally, offering mental health support and strategies to manage anxiety can help address the emotional challenges that students face in online learning. Finally, integrating more interactive and engaging elements into courses will boost students' intrinsic motivation and overall engagement.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjani, O. A. (2024). Technological pedagogical content knowledge for twenty-first century learning skills. \u003cem\u003eInternational Journal of Research in Business and Social Science (2147-4478)\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 468\u0026ndash;476. https://doi.org/10.20525/ijrbs.v13i4.3355\u003c/li\u003e\n\u003cli\u003eAlian, E., \u0026amp; Alhaj, A. A. M. (2024). Perceptions of Novice and Experienced Instructors of Translation at Selected Saudi Universities Toward Technological Pedagogical Content Knowledge for Teaching Professional Development. \u003cem\u003eTheory and Practice in Language Studies\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 844\u0026ndash;853. https://doi.org/10.17507/tpls.1403.27\u003c/li\u003e\n\u003cli\u003eBrinkley-Etzkorn, K. E. (2018). Learning to teach online: Measuring the influence of faculty development training on teaching effectiveness through a TPACK lens. \u003cem\u003eThe Internet and Higher Education\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, 28\u0026ndash;35. https://doi.org/10.1016/j.iheduc.2018.04.004\u003c/li\u003e\n\u003cli\u003eBrinkley-Etzkorn, K. E. (2020). The Effects of Training on Instructor Beliefs about and Attitudes toward Online Teaching. \u003cem\u003eAmerican Journal of Distance Education\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 19\u0026ndash;35. https://doi.org/10.1080//08923647.2020.1692553\u003c/li\u003e\n\u003cli\u003eBroadbent, J., E. Panadero, Lodge, J. M., \u0026amp; M. Fuller-Tyszkiewicz. (2022). The self-regulation for learning online (SRL-O) questionnaire. \u003cem\u003eMetacognition and Learning\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 135\u0026ndash;163. https://doi.org/10.1007/s11409-022-09319-6\u003c/li\u003e\n\u003cli\u003eDarmiany, D., Istiningsih, S., Nurmawanti, I., Nurwahidah, N., \u0026amp; Mauldya, M. A. (2024). Student\u0026rsquo;s Self-Regulated Learning in Online Class Design Based on Reflective Learning. \u003cem\u003eAL-ISHLAH Jurnal Pendidikan\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 440\u0026ndash;449. https://doi.org/10.35445/alishlah.v16i1.3947\u003c/li\u003e\n\u003cli\u003eDewi, S., Masitoh, F., Afifi, N., \u0026amp; Qamaria, R. S. (2023). Students\u0026rsquo; Perception Toward Self-Regulated Learning in Online Writing Class. \u003cem\u003eMetathesis Journal of English Language Literature and Teaching\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 229\u0026ndash;239. https://doi.org/10.31002/metathesis.v7i2.159\u003c/li\u003e\n\u003cli\u003eDong, X., Yuan, H., Xue, H., Li, Y., Jia, L., Chen, J., Shi, Y., \u0026amp; Zhang, X. (2023). Factors influencing college students\u0026rsquo; self-regulated learning in online learning environment: A systematic review. \u003cem\u003eNurse Education Today\u003c/em\u003e, \u003cem\u003e133\u003c/em\u003e, 106071\u0026ndash;106071. https://doi.org/10.1016/j.nedt.2023.106071\u003c/li\u003e\n\u003cli\u003eEdisherashvili, N., Saks, K., Pedaste, M., \u0026amp; Leijen, \u0026Auml;. (2022). Supporting Self-Regulated Learning in Distance Learning Contexts at Higher Education Level: Systematic Literature Review. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e. https://doi.org/10.3389/fpsyg.2021.792422\u003c/li\u003e\n\u003cli\u003eGalindo, J. N. (2023). 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Technological Pedagogical Content Knowledge of Preservice Elementary Teachers: Relationship to Self-Regulation and Technology Integration Self-Efficacy. \u003cem\u003eEuropean Journal of Educational Research\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 159\u0026ndash;170. https://doi.org/10.12973/eu-jer.13.1.159\u003c/li\u003e\n\u003cli\u003eWillermark, S. (2016). \u003cem\u003eTechnological Pedagogical and Content Knowledge: A Review of Empirical Studies Published From 2011 to 2016 - Sara Willermark, 2018\u003c/em\u003e. Journal of Educational Computing Research; Sage Journals. https://journals.sagepub.com/doi/10.1177/0735633117713114\u003c/li\u003e\n\u003cli\u003eZhang, S., Liu, Q., \u0026amp; Cai, Z. (2019). Exploring primary school teachers\u0026rsquo; technological pedagogical content knowledge (TPACK) in online collaborative discourse: An epistemic network analysis. \u003cem\u003eBritish Journal of Educational Technology\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(6), 3437\u0026ndash;3455. https://doi.org/10.1111/bjet.12751\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bulacan State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"technology pedagogy, content, knowledge, self-regulated, online learning","lastPublishedDoi":"10.21203/rs.3.rs-8800976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8800976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of instructors' Technological Pedagogical and Content Knowledge (TPACK) on students' Self-Regulated Learning-Online (SRL-O) in a state university in Bulacan, the Philippines. Employing a quantitative correlational research design, this study aimed to provide evidence-based recommendations for enhancing online education. Participants included 299 instructors and 381 students from six campuses selected through random sampling. Data were collected using validated survey instruments tailored to the TPACK and SRL-O. The results revealed a significant positive correlation between instructors' TPACK and students' SRL-O, with regression analysis showing that TPACK accounted for 90.2% of the variance in SRL-O. This underscores the critical role of instructors' abilities to integrate technology, pedagogy, and content in fostering students' self-regulation in online learning. Key findings highlight that, while instructors generally possess a moderate understanding of TPACK, areas such as Technological Content Knowledge (TCK) and Technological Pedagogical Content Knowledge (TPCK) require further enhancement. For students, the dimensions of SRL-O, including task strategies, effort regulation, and planning, were positively rated, but challenges remained in intrinsic motivation and managing negative emotions. The study concludes that strengthening instructors' TPACK through targeted training programs can significantly boost students' self-regulatory capacity. Simultaneously, initiatives to support students\u0026rsquo; emotional well-being and motivation are crucial for optimizing online learning outcomes. These findings provide actionable insights for developing professional development initiatives and student support programs to ensure an effective and adaptive online learning ecosystem.\u003c/p\u003e","manuscriptTitle":"The Influence of Instructors' Technological Pedagogical and Content Knowledge (TPaCK) on the Self-Regulated Learning-Online (SRL-O) of the Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 16:49:02","doi":"10.21203/rs.3.rs-8800976/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e93cd18a-0e1f-4cab-bffb-567ae860adb0","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62413386,"name":"Educational Philosophy and Theory"}],"tags":[],"updatedAt":"2026-02-09T16:49:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 16:49:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8800976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8800976","identity":"rs-8800976","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}