Self-Directed Learning Readiness and Student Satisfaction in Modular Mathematics Education in Philippine Tertiary Institutions

Self-Directed Learning Readiness and Student Satisfaction in Modular Mathematics Education in Philippine Tertiary Institutions | 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 Short Report Self-Directed Learning Readiness and Student Satisfaction in Modular Mathematics Education in Philippine Tertiary Institutions LALIN TUGUIC This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7327750/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 relationship between self-directed learning readiness (SDLR), learning experiences, and student satisfaction in modular mathematics education among tertiary students in the Philippines. Using a mixed-method design, quantitative data were collected through validated survey instruments, while qualitative insights were gathered from semi-structured interviews. Structural equation modeling revealed that SDLR, particularly self-management and adaptability, significantly predicted student satisfaction, both directly and indirectly through the mediating effects of teaching, social, and cognitive presence as outlined in the Community of Inquiry framework. Qualitative findings reinforced these results, highlighting the dual nature of modular learning as both a source of flexibility and a challenge due to the limited feedback and isolation it entails. Institutional and socioeconomic factors further moderated outcomes, with disparities in resources and support contributing to uneven experiences across students. The study highlights the importance of adaptive module design, responsive feedback systems, and targeted institutional interventions in enhancing satisfaction and equity in modular mathematics education. Self-Directed Learning Readiness Modular Mathematics Education Student Satisfaction Community of Inquiry Socioeconomic Factors Introduction The COVID-19 pandemic brought about an unparalleled wave of disruption in education systems across the globe, forcing educators and students alike to rapidly adapt to remote and distance learning methods almost overnight. In the Philippine context, the realities of geographic isolation and persistent infrastructural limitations, most notably unreliable internet access, meant that many tertiary institutions, especially those situated in the nation’s remote and rural communities, had little option but to adopt modular learning as their primary approach to instruction. This modality, rooted in the distribution of carefully structured self-learning modules aligned with essential curricular standards, was delivered in both print and digital form, depending on what resources could be accessed locally, as outlined by the Department of Education in 2020. For the field of mathematics, where learning often hinges on sequential, scaffolded problem solving and frequent feedback, the sudden reliance on modular materials presented a distinct set of challenges. While research has shown that online and blended learning approaches can be effective under optimal conditions, such as those commonly found in well-connected, urbanized environments, there is a notable scarcity of studies probing the effects of purely modular instruction on mathematics performance and student satisfaction among college learners confronting resource constraints. This gap is especially pronounced in the Philippine setting, where the digital divide persists and modular learning has become the default mode for many tertiary mathematics students navigating their studies in the wake of a global crisis. Review of Related Literature The sudden transition to modular learning in Philippine tertiary institutions during the COVID-19 pandemic highlighted the importance of student adaptability and readiness for independent study. Central to this discussion is self-directed learning readiness (SDLR ), which refers to learners’ capacity to plan, implement, and evaluate their own learning activities. Building on Knowles’ principles of adult learning, SDLR encompasses motivation, self-management, and self-monitoring, all of which are critical in higher education settings where autonomy is expected (Lim et al., 2024 ). In mathematics education, these traits become particularly significant, as the discipline often requires persistence and reflective problem-solving (Theobald et al., 2021 ). To better understand how learning experiences shape satisfaction, numerous studies have drawn on the Community of Inquiry (CoI) framework, developed by Garrison, Anderson, and Archer ( 2000 ). The CoI highlights the roles of teaching, social, and cognitive presence in supporting meaningful learning in environments with reduced face-to-face interaction. Recent evidence suggests that these presences are not only related to perceived learning but also to satisfaction, with teaching presence being manifested through clarity, guidance, and feedback, which exert the strongest influence (Martin et al., 2022 ; Li et al., 2024 ). Social presence helps sustain engagement by fostering a sense of belonging, while cognitive presence encourages deep engagement with course content, an element that is particularly vital in mathematics (Jou et al., 2022 ). In the Philippine context, modular learning was adopted as a practical solution to maintain instructional continuity, especially for students without reliable internet access. The modality’s flexibility has been widely appreciated, but challenges persist. Reports emphasize that limited teacher-student interaction, delayed feedback, and heavy reliance on student discipline often reduce satisfaction (Bustillo, 2022 ). These issues align with findings from global studies, which suggest that modular learners with strong self-directed skills thrive, while those lacking them struggle with motivation and comprehension (Kong, 2023 ). Consequently, SDLR appears to function as a key moderator of student experiences and satisfaction under modular delivery. Another layer influencing student outcomes involves institutional and socioeconomic factors. Research has consistently shown that disparities in access to resources, conducive study environments, and technological devices create uneven experiences of modular education (World Bank, 2021 ). In the Philippines, students from rural or economically disadvantaged backgrounds often face greater barriers to success, further widening learning gaps (Bustillo, 2022 ). Institutional interventions such as resource support, peer collaboration initiatives, and responsive feedback mechanisms have been shown to mitigate these inequities and reinforce SDLR (Kong, 2023 ). These contextual considerations are crucial for mathematics, a subject often perceived as difficult, where a lack of support can quickly lead to disengagement. Overall, existing literature provides important insights but also reveals several gaps. While SDLR and student satisfaction have been examined in online and blended environments, there is a limited empirical focus on mathematics within modular learning in Philippine higher education. Moreover, although the CoI framework has been validated in various contexts, few studies have explored how its presences interact with SDLR to shape satisfaction in resource-constrained settings. Finally, while socioeconomic and institutional challenges are well documented, their role in moderating satisfaction and learning outcomes in mathematics remains underexplored. Addressing these gaps can help institutions design more equitable and effective modular programs that align with students’ readiness for self-directed learning. Problem Statement Despite the widespread implementation of modular distance learning in the Philippines, student experiences and levels of satisfaction remain uneven. While some learners demonstrate adaptability and perseverance, others struggle with isolation, reduced feedback, and limited institutional support. These inconsistencies suggest that the effectiveness of modular mathematics education is not solely a matter of instructional design but also hinges on learners’ readiness to direct their own learning. Research has highlighted the significance of self-directed learning readiness (SDLR) in influencing student engagement and persistence in independent learning contexts (Lim et al., 2024 ; Theobald et al., 2021 ). However, its role in mathematics education delivered through modular formats has received little systematic investigation, particularly within the Philippine tertiary context. Furthermore, although the Community of Inquiry (CoI) framework identifies teaching, social, and cognitive presence as critical mediators of learning satisfaction (Garrison et al., 2000 ; Martin et al., 2022 ), there is limited empirical evidence on how these constructs interact with SDLR in resource-constrained modular environments. Adding to this complexity are institutional and socioeconomic factors that often amplify disparities. Unequal access to resources, varying quality of modules, and differences in institutional support contribute to inequities in learning outcomes and satisfaction (Bustillo, 2022 ; World Bank, 2021 ). Addressing these gaps is crucial to ensure that modular mathematics education fosters not only continuity of instruction but also equitable and meaningful learning experiences. Objectives of the Study This study seeks to investigate the interplay between self-directed learning readiness, learning experience, and student satisfaction in modular mathematics education within Philippine tertiary institutions. Specifically, it aims to: Examine the relationship between students’ SDLR and their satisfaction with modular mathematics learning. Analyze the mediating role of teaching, social, cognitive, and learning presence in linking learning experiences to satisfaction. Identify institutional and socioeconomic factors that contribute to disparities in satisfaction and learning outcomes and explore how these factors may mitigate or exacerbate challenges in modular mathematics education. Methods This study employed a mixed-methods design, combining quantitative survey data with qualitative interviews to investigate the interplay between self-directed learning readiness (SDLR), learning experiences, and student satisfaction in modular mathematics education. The quantitative strand tested hypothesized relationships through structural equation modeling (SEM), while the qualitative strand provided contextual depth, capturing students’ perspectives and lived experiences. Respondents were undergraduate students enrolled in mathematics courses across selected tertiary institutions in the Philippines, including Kalinga State University. These sites was chosen to represent geographically isolated areas where modular distance learning was the primary mode of instruction during the COVID-19 pandemic. A purposive sampling approach was used to include students actively engaged in modular learning. In total, students completed the survey, with a smaller group participating in follow-up interviews to enrich and validate the quantitative findings. Three validated scales were employed: (1) the Self-Directed Learning Readiness Scale for Nursing Education (SDLRNE) by Fisher and King ( 2010 ) and Martin’s (2012) Adaptability Scale to measure self-management, self-control, and adaptability; (2) the WebTalk instrument (Wertz, 2022 ), based on the Community of Inquiry framework, to assess teaching, social, cognitive, and learning presence; and (3) Walker’s ( 2003 ) Satisfaction Scale of Enjoyment of Distance Education, adapted for modular mathematics learning. All instruments demonstrated strong internal consistency, with Cronbach’s alpha values ranging from 0.78 to 0.95. Data collection was conducted using both printed and electronic survey formats to accommodate students with limited internet access. Ethical approval was secured from the Kalinga State University Ethics Committee, and informed consent was obtained before participation. Confidentiality and anonymity were assured. Semi-structured interviews were conducted with a subset of respondents to explore themes related to autonomy, adaptability, and institutional support, complementing the survey data. Quantitative data were analyzed through SEM using WarpPLS. Reliability, convergent validity, and discriminant validity were assessed, and common method bias was minimized using full collinearity variance inflation factors, all below the recommended threshold of 3.3 (Kock, 2020 ). Qualitative interview transcripts were analyzed thematically, with codes grouped into categories reflecting patterns in learning strategies, satisfaction, and institutional factors. Integration of quantitative and qualitative results ensured a robust and comprehensive interpretation of the findings. Results A. Quantitative Findings Analysis revealed significant variation in student satisfaction across institutions. Students from schools with refined modular materials and stronger support mechanisms-feedback systems, instructor guidance reported higher satisfaction, while those with fragmented modules and limited teacher interaction expressed lower satisfaction and greater frustration. SDLR indicators, particularly self-management and adaptability, emerged as strong predictors of satisfaction. Students with higher levels of SDLR demonstrated greater persistence and engagement, confirming the central role of learner readiness in modular mathematics education. Teaching presence and social presence also showed significant positive associations with learning satisfaction, underscoring the applicability of the Community of Inquiry framework in modular contexts. Socioeconomic variables, particularly parental educational attainment and resource access at home, were consistently linked to disparities in satisfaction and outcomes. B. Qualitative Insights Thematic analysis of interviews highlighted the dual nature of modular learning. On the one hand, students valued its flexibility and the independence it fostered. On the other hand, they reported feelings of isolation and difficulty when feedback was delayed or unclear. Many described adopting adaptive strategies such as peer collaboration, seeking alternative resources, and proactively engaging with content. These narratives reinforced the quantitative results, illustrating how both SDLR and institutional support shaped satisfaction. Discussion Implications for Modular Mathematics Pedagogy Findings demonstrate that the effectiveness of modular learning in mathematics depends not only on the quality of module design but also on students’ readiness for self-directed learning. Students with strong self-management and adaptability were better positioned to succeed, while those lacking these skills often struggled. This aligns with international evidence that SDLR is a critical predictor of persistence and satisfaction in distance education (Theobald et al., 2021 ; Lim et al., 2024 ). The results further validate the Community of Inquiry framework, showing that teaching, social, and cognitive presence remain relevant even in modular environments. Teaching presence in the form of clear instructions, guidance, and timely feedback proved especially influential. Social presence helped reduce feelings of isolation, while cognitive presence supported deeper engagement with mathematical problem-solving. These findings echo recent meta-analyses highlighting the strong link between presence and satisfaction (Martin et al., 2022 ; Li et al., 2024 ). Institutional and Socioeconomic Considerations Institutional support emerged as a decisive factor in mitigating disparities. Schools that invested in module refinement, provided feedback mechanisms, and encouraged peer support created more positive learning experiences. Conversely, under-resourced institutions intensified inequities, especially for students from disadvantaged backgrounds. These findings mirror broader concerns about the digital divide and unequal access to educational resources in developing contexts (Bustillo, 2022 ; World Bank, 2021 ). Recommendations To strengthen modular mathematics education, institutions should prioritize adaptive module design, embed iterative feedback mechanisms, and create opportunities for collaborative learning. Training programs that enhance students’ SDLR and self-regulated learning strategies may further improve outcomes. At a policy level, targeted interventions addressing socioeconomic inequities such as resource subsidies and community-based learning hubs are essential to ensure equitable access and satisfaction across diverse student populations. Limitations and Future Directions The study’s reliance on self-reported data introduces the potential for response bias. Moreover, the focus on tertiary institutions in specific Philippine regions limits generalizability. Future research should consider longitudinal approaches to examine how SDLR and satisfaction evolve and test interventions designed to strengthen both learner readiness and institutional support. Exploring the integration of digital platforms with modular instruction may also offer new insights for hybrid models of mathematics education. Conclusion This study examined the relationship between self-directed learning readiness (SDLR), learning experiences, and student satisfaction in modular mathematics education within Philippine tertiary institutions. Results demonstrated that SDLR, particularly self-management and adaptability, significantly predicted satisfaction, while teaching, social, and cognitive presences mediated these effects. Institutional and socioeconomic conditions further shaped disparities, revealing that resource availability and institutional support are critical to ensuring equitable learning experiences. By integrating quantitative and qualitative findings, the study confirmed that the effectiveness of modular mathematics education is best understood through the interplay of learner readiness, pedagogical presence, and contextual factors. The findings carry important implications for both policy and practice. Institutions must prioritize adaptive module design, timely feedback systems, and collaborative learning opportunities to strengthen teaching and social presence. At the same time, targeted interventions to enhance students’ SDLR and address socioeconomic barriers are necessary to reduce inequities. For policymakers, the study highlights the urgency of resource support and institutional capacity-building to ensure that modular learning fulfills its promise of inclusive and resilient education. In advancing these recommendations, this research contributes to the broader discourse on sustaining quality and equity in crisis-responsive educational systems, offering lessons that extend beyond mathematics to other disciplines and contexts facing similar challenges. Practical Implications The findings of this study underscore the need for higher education institutions to design modular mathematics programs that go beyond content delivery by intentionally cultivating students’ self-directed learning readiness. Practical steps include embedding structured feedback mechanisms, integrating peer collaboration opportunities, and providing orientation or training sessions that strengthen students’ self-management and adaptability skills. Administrators should invest in module refinement and faculty capacity-building to enhance teaching presence, while also ensuring that resource support reaches socioeconomically disadvantaged learners. For policymakers, the results highlight the importance of addressing systemic inequities—such as uneven access to technology and learning materials—through targeted subsidies and infrastructure support. Collectively, these measures can transform modular learning into a more inclusive and effective approach, equipping students with both the competencies and support systems needed to thrive in mathematics education. Declarations Ethics approval The study was conducted in accordance with internationally accepted ethical standards for research involving human participants. While Kalinga State University does not currently operate a formal institutional review board, the research adhered to established ethical principles, including voluntary participation, informed consent, confidentiality, and the right to withdraw at any stage without penalty. Competing interests The author declares that there are no competing financial or non-financial interests that could have influenced the outcomes of this research. Consent for publication Not applicable, as no person’s data are included in this article. Funding This research was supported by Kalinga State University, which provided logistical assistance such as transportation to field sites and other essential expenses necessary for data collection. No external funding was received from commercial or private sources. Author Contribution Lalin Abbacan-Tuguic(L.T.) conceived and designed the study, performed data analysis, drafted the manuscript, collected data, conducted literature review search, and manuscript revision. The author reviewed and approved the final version of the manuscript and is accountable for all aspects of the work. Acknowledgement The author extends sincere appreciation to the Department of Education and collaborating higher education institutions for their valuable assistance in data collection and constructive feedback, which strengthened the quality of this study. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. No publicly available datasets were used in this research. References Alabdulaziz M. The role of digital technology in mathematics education during the COVID-19 pandemic. Int J Eng Educ. 2021;37(2):456–70. https://doi.org/10.1504/IJEE.2021.100425 . Amora J, Ochoco M, Anicete R. Student engagement and college experience as the mediators of the relationship between institutional support and academic performance. Digit J Lasallian Res. 2016;12:15–30. https://doi.org/10.1016/j.dlasr.2016.06.003 . Attard C, Holmes K. Blended learning in mathematics education: Lessons from the pandemic. Math Educ Res J. 2021;33(4):699–721. https://doi.org/10.1007/s10639-021-10543-x . Ballad K, Fajardo M, De Guzman J. Global disruptions in education: Impact and innovation. J Global Educ Policy. 2021;9(1):34–45. https://doi.org/10.1007/s10639-021-10543-x . Bustillo E. The challenges of modular learning in the wake of COVID. Educ Sci. 2022;12(10):1–12. https://doi.org/10.3390/educsci12100677 . Cabuquin JC. Modular and online learning satisfaction in mathematics amid COVID-19: Implications for new normal teaching practices. Am J Multidisciplinary Res Innov. 2023;1(6):30–40. https://doi.org/10.54536/ajmri.v1i6.954 . Das A. Integrating technology in mathematics education: Challenges and opportunities. J STEM Educ. 2021;22(3):156–65. https://doi.org/10.1080/15391523.2021.1929335 . Department of Education. (2020). Basic Education Learning Continuity Plan in the time of COVID-19 (BE-LCP). https://www.deped.gov.ph/wp-content/uploads/2020/07/DepEd_LCP_July3.pdf Fisher M, King J. The self-directed learning readiness scale for nursing education. Nurse Educ Today. 2010;30(1):65–72. https://doi.org/10.1016/j.nedt.2009.06.013 . Garrison DR, Anderson T, Archer W. Critical inquiry in a text-based environment: Computer conferencing in higher education. Internet High Educ. 2000;2(2–3):87–105. https://doi.org/10.1016/S1096-7516(00)00016-6 . Jou Y-T, et al. Assessing cognitive factors of modular distance learning and student satisfaction. Int J Educational Res Open. 2022;3:100215. https://doi.org/10.1016/j.ijedro.2021.100215 . Jung I, Lee Y. Attributes, antecedents, and consequences of student satisfaction in online education. Online Learn J. 2018;22(2):36–49. https://doi.org/10.24059/olj.v22i2.123 . Kock N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int J e-Collaboration. 2020;16(1):1–10. https://doi.org/10.4018/IJeC.2020010101 . Kong SC. Developing self-regulated learning as pedagogy: Implications for institutional practice. Br J Edu Technol. 2023;54(2):469–83. https://doi.org/10.1111/bjet.13277 . Li W, Chen Y, Zhang J. The impact of teaching presence on students’ online learning satisfaction. Front Psychol. 2024;15:1192832. https://doi.org/10.3389/fpsyg.2024.1192832 . Lim YS, Chen NS, Wong CS. Evaluation and refinement of the self-directed learning readiness scale (SDLRS). Educ Inform Technol. 2024;29(3):3415–32. https://doi.org/10.1007/s10639-023-11892-4 . Martin F, Wu T, Wan L, Xie K. A meta-analysis on the Community of Inquiry presences and learning outcomes in online and blended learning environments. Online Learn J. 2022;26(1):325–51. https://doi.org/10.24059/olj.v26i1.2566 . Springer W. Digital education and self-directed learning readiness: Implications for modular pedagogy post-pandemic. Discover Educ J. 2024;23(1):49–70. https://doi.org/10.1007/s10639-024-12755-3 . Theobald M, et al. Self-regulated learning training programs enhance academic performance and SRL strategies: A meta-analysis. Educational Psychol Rev. 2021;33(2):559–96. https://doi.org/10.1007/s10648-020-09546-z . Turk M. How can presences and basic needs happily meet online? Int Rev Res Open Distrib Learn. 2022;23(4):49–65. https://doi.org/10.19173/irrodl.v23i4.6320 . Walker R. Satisfaction scale of enjoyment of distance education. J Distance Learn Evaluation. 2003;21(2):56–74. https://doi.org/10.1016/j.jde.2003.03.005 . Wertz FJ. Web-based teaching and learning link to social and cognitive presence. J Online Learn Res. 2022;8(3):211–24. https://doi.org/10.1016/j.jolr.2022.09.012 . World Bank. Digital divide and education: Lessons from COVID-19. World Bank Policy Note; 2021. https://documents.worldbank.org . Additional Declarations No competing interests reported. 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-7327750","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":547783679,"identity":"4c9ead89-b3fb-4c97-9a0f-38815389b5b9","order_by":0,"name":"LALIN TUGUIC","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACPgglwcDPA2YwAzEPfi1sYEVALZI9JGphYDA4Q7QW9vPHJL+2Wcgbnzn+TIKhwjqxQbr3AH4tPMls0rJtEobbzvaYSTCcSU9skDmXQMBhQC2SbRIJZud52CQY2w4nNkjkGODXwv8YosW4n/2ZBOM/YrRIJLNJfgRqMeBtMJNgbCBKy2Nja4ZzEoYzzpwxtkg4lm7cJnMGvxZ+/sSHN3+U1cnz96Q/vPGhxlq2X7oHvxYQYIZHRALYXoIaGBgYf6BwidEyCkbBKBgFIwoAAJsLOnqP5zVPAAAAAElFTkSuQmCC","orcid":"","institution":"Kalinga State University","correspondingAuthor":true,"prefix":"","firstName":"LALIN","middleName":"","lastName":"TUGUIC","suffix":""}],"badges":[],"createdAt":"2025-08-08 13:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7327750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7327750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96350999,"identity":"bc3e84af-ead6-4f15-a14e-4dceeedd1e9d","added_by":"auto","created_at":"2025-11-20 07:31:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30123,"visible":true,"origin":"","legend":"","description":"","filename":"PUBLISHSelfDirectedLearningReadiness.docx","url":"https://assets-eu.researchsquare.com/files/rs-7327750/v1/1fca5c1f20fc965f191c19e3.docx"},{"id":96351002,"identity":"a4f9af48-289c-49c5-900c-a3b405bd0eb6","added_by":"auto","created_at":"2025-11-20 07:31:03","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3823,"visible":true,"origin":"","legend":"","description":"","filename":"0b19c243de5a4736970a31d2be8d12bf.json","url":"https://assets-eu.researchsquare.com/files/rs-7327750/v1/da4b7a2b155865c4114936fc.json"},{"id":96367466,"identity":"af896295-6140-473e-86e5-ba64238fc706","added_by":"auto","created_at":"2025-11-20 10:12:52","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":58668,"visible":true,"origin":"","legend":"","description":"","filename":"0b19c243de5a4736970a31d2be8d12bf1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7327750/v1/6d881a17015e3f9c2f60c372.xml"},{"id":96351003,"identity":"d6a14e2c-c9db-4191-80da-20f9de5436fb","added_by":"auto","created_at":"2025-11-20 07:31:03","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54168,"visible":true,"origin":"","legend":"","description":"","filename":"0b19c243de5a4736970a31d2be8d12bf1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7327750/v1/19cf6f4bdda3321c07915f4e.xml"},{"id":96351000,"identity":"1e237080-49bc-46e0-b19f-6de9809c7cf7","added_by":"auto","created_at":"2025-11-20 07:31:03","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63567,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7327750/v1/989de879159a26b4720a334a.html"},{"id":100238759,"identity":"723bc858-a34e-46dc-97c7-311c9e0ef8e0","added_by":"auto","created_at":"2026-01-14 12:55:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":430401,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7327750/v1/3a9e12c5-65be-41d9-be0f-b677502dd01f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSelf-Directed Learning Readiness and Student Satisfaction in Modular Mathematics Education in Philippine Tertiary Institutions\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic brought about an unparalleled wave of disruption in education systems across the globe, forcing educators and students alike to rapidly adapt to remote and distance learning methods almost overnight. In the Philippine context, the realities of geographic isolation and persistent infrastructural limitations, most notably unreliable internet access, meant that many tertiary institutions, especially those situated in the nation\u0026rsquo;s remote and rural communities, had little option but to adopt modular learning as their primary approach to instruction. This modality, rooted in the distribution of carefully structured self-learning modules aligned with essential curricular standards, was delivered in both print and digital form, depending on what resources could be accessed locally, as outlined by the Department of Education in 2020.\u003c/p\u003e\u003cp\u003eFor the field of mathematics, where learning often hinges on sequential, scaffolded problem solving and frequent feedback, the sudden reliance on modular materials presented a distinct set of challenges. While research has shown that online and blended learning approaches can be effective under optimal conditions, such as those commonly found in well-connected, urbanized environments, there is a notable scarcity of studies probing the effects of purely modular instruction on mathematics performance and student satisfaction among college learners confronting resource constraints. This gap is especially pronounced in the Philippine setting, where the digital divide persists and modular learning has become the default mode for many tertiary mathematics students navigating their studies in the wake of a global crisis.\u003c/p\u003e"},{"header":"Review of Related Literature","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe sudden transition to modular learning in Philippine tertiary institutions during the COVID-19 pandemic highlighted the importance of student adaptability and readiness for independent study. Central to this discussion is \u003cem\u003eself-directed learning readiness (SDLR\u003c/em\u003e), which refers to learners\u0026rsquo; capacity to plan, implement, and evaluate their own learning activities. Building on Knowles\u0026rsquo; principles of adult learning, SDLR encompasses motivation, self-management, and self-monitoring, all of which are critical in higher education settings where autonomy is expected (Lim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In mathematics education, these traits become particularly significant, as the discipline often requires persistence and reflective problem-solving (Theobald et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo better understand how learning experiences shape satisfaction, numerous studies have drawn on the Community of Inquiry (CoI) framework, developed by Garrison, Anderson, and Archer (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The CoI highlights the roles of teaching, social, and cognitive presence in supporting meaningful learning in environments with reduced face-to-face interaction. Recent evidence suggests that these presences are not only related to perceived learning but also to satisfaction, with teaching presence being manifested through clarity, guidance, and feedback, which exert the strongest influence (Martin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Social presence helps sustain engagement by fostering a sense of belonging, while cognitive presence encourages deep engagement with course content, an element that is particularly vital in mathematics (Jou et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the Philippine context, \u003cem\u003emodular learning\u003c/em\u003e was adopted as a practical solution to maintain instructional continuity, especially for students without reliable internet access. The modality\u0026rsquo;s flexibility has been widely appreciated, but challenges persist. Reports emphasize that limited teacher-student interaction, delayed feedback, and heavy reliance on student discipline often reduce satisfaction (Bustillo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These issues align with findings from global studies, which suggest that modular learners with strong self-directed skills thrive, while those lacking them struggle with motivation and comprehension (Kong, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, SDLR appears to function as a key moderator of student experiences and satisfaction under modular delivery.\u003c/p\u003e\u003cp\u003eAnother layer influencing student outcomes involves institutional and socioeconomic factors. Research has consistently shown that disparities in access to resources, conducive study environments, and technological devices create uneven experiences of modular education (World Bank, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Philippines, students from rural or economically disadvantaged backgrounds often face greater barriers to success, further widening learning gaps (Bustillo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Institutional interventions such as resource support, peer collaboration initiatives, and responsive feedback mechanisms have been shown to mitigate these inequities and reinforce SDLR (Kong, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These contextual considerations are crucial for mathematics, a subject often perceived as difficult, where a lack of support can quickly lead to disengagement.\u003c/p\u003e\u003cp\u003eOverall, existing literature provides important insights but also reveals several gaps. While SDLR and student satisfaction have been examined in online and blended environments, there is a limited empirical focus on mathematics within modular learning in Philippine higher education. Moreover, although the CoI framework has been validated in various contexts, few studies have explored how its presences interact with SDLR to shape satisfaction in resource-constrained settings. Finally, while socioeconomic and institutional challenges are well documented, their role in moderating satisfaction and learning outcomes in mathematics remains underexplored. Addressing these gaps can help institutions design more equitable and effective modular programs that align with students\u0026rsquo; readiness for self-directed learning.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eProblem Statement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite the widespread implementation of modular distance learning in the Philippines, student experiences and levels of satisfaction remain uneven. While some learners demonstrate adaptability and perseverance, others struggle with isolation, reduced feedback, and limited institutional support. These inconsistencies suggest that the effectiveness of modular mathematics education is not solely a matter of instructional design but also hinges on learners\u0026rsquo; readiness to direct their own learning.\u003c/p\u003e\u003cp\u003eResearch has highlighted the significance of self-directed learning readiness (SDLR) in influencing student engagement and persistence in independent learning contexts (Lim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Theobald et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, its role in mathematics education delivered through modular formats has received little systematic investigation, particularly within the Philippine tertiary context. Furthermore, although the Community of Inquiry (CoI) framework identifies teaching, social, and cognitive presence as critical mediators of learning satisfaction (Garrison et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there is limited empirical evidence on how these constructs interact with SDLR in resource-constrained modular environments.\u003c/p\u003e\u003cp\u003eAdding to this complexity are institutional and socioeconomic factors that often amplify disparities. Unequal access to resources, varying quality of modules, and differences in institutional support contribute to inequities in learning outcomes and satisfaction (Bustillo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Addressing these gaps is crucial to ensure that modular mathematics education fosters not only continuity of instruction but also equitable and meaningful learning experiences.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjectives of the Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study seeks to investigate the interplay between self-directed learning readiness, learning experience, and student satisfaction in modular mathematics education within Philippine tertiary institutions. Specifically, it aims to:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExamine the relationship between students\u0026rsquo; SDLR and their satisfaction with modular mathematics learning.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAnalyze the mediating role of teaching, social, cognitive, and learning presence in linking learning experiences to satisfaction.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIdentify institutional and socioeconomic factors that contribute to disparities in satisfaction and learning outcomes and explore how these factors may mitigate or exacerbate challenges in modular mathematics education.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed a mixed-methods design, combining quantitative survey data with qualitative interviews to investigate the interplay between self-directed learning readiness (SDLR), learning experiences, and student satisfaction in modular mathematics education. The quantitative strand tested hypothesized relationships through structural equation modeling (SEM), while the qualitative strand provided contextual depth, capturing students\u0026rsquo; perspectives and lived experiences.\u003c/p\u003e\u003cp\u003eRespondents were undergraduate students enrolled in mathematics courses across selected tertiary institutions in the Philippines, including Kalinga State University. These sites was chosen to represent geographically isolated areas where modular distance learning was the primary mode of instruction during the COVID-19 pandemic. A purposive sampling approach was used to include students actively engaged in modular learning. In total, students completed the survey, with a smaller group participating in follow-up interviews to enrich and validate the quantitative findings.\u003c/p\u003e\u003cp\u003eThree validated scales were employed: (1) the Self-Directed Learning Readiness Scale for Nursing Education (SDLRNE) by Fisher and King (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Martin\u0026rsquo;s (2012) Adaptability Scale to measure self-management, self-control, and adaptability; (2) the WebTalk instrument (Wertz, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), based on the Community of Inquiry framework, to assess teaching, social, cognitive, and learning presence; and (3) Walker\u0026rsquo;s (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) Satisfaction Scale of Enjoyment of Distance Education, adapted for modular mathematics learning. All instruments demonstrated strong internal consistency, with Cronbach\u0026rsquo;s alpha values ranging from 0.78 to 0.95.\u003c/p\u003e\u003cp\u003eData collection was conducted using both printed and electronic survey formats to accommodate students with limited internet access. Ethical approval was secured from the Kalinga State University Ethics Committee, and informed consent was obtained before participation. Confidentiality and anonymity were assured. Semi-structured interviews were conducted with a subset of respondents to explore themes related to autonomy, adaptability, and institutional support, complementing the survey data.\u003c/p\u003e\u003cp\u003eQuantitative data were analyzed through SEM using WarpPLS. Reliability, convergent validity, and discriminant validity were assessed, and common method bias was minimized using full collinearity variance inflation factors, all below the recommended threshold of 3.3 (Kock, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Qualitative interview transcripts were analyzed thematically, with codes grouped into categories reflecting patterns in learning strategies, satisfaction, and institutional factors. Integration of quantitative and qualitative results ensured a robust and comprehensive interpretation of the findings.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA. \u003cstrong\u003eQuantitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis revealed significant variation in student satisfaction across institutions. Students from schools with refined modular materials and stronger support mechanisms-feedback systems, instructor guidance reported higher satisfaction, while those with fragmented modules and limited teacher interaction expressed lower satisfaction and greater frustration. SDLR indicators, particularly self-management and adaptability, emerged as strong predictors of satisfaction. Students with higher levels of SDLR demonstrated greater persistence and engagement, confirming the central role of learner readiness in modular mathematics education.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTeaching presence and social presence\u003c/em\u003e also showed significant positive associations with learning satisfaction, underscoring the applicability of the Community of Inquiry framework in modular contexts. Socioeconomic variables, particularly parental educational attainment and resource access at home, were consistently linked to disparities in satisfaction and outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. \u0026nbsp;Qualitative Insights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThematic analysis of interviews highlighted the dual nature of modular learning. On the one hand, students valued its flexibility and the independence it fostered. On the other hand, they reported feelings of isolation and difficulty when feedback was delayed or unclear. Many described adopting adaptive strategies such as peer collaboration, seeking alternative resources, and proactively engaging with content. These narratives reinforced the quantitative results, illustrating how both SDLR and institutional support shaped satisfaction.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eImplications for Modular Mathematics Pedagogy\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFindings demonstrate that the effectiveness of modular learning in mathematics depends not only on the quality of module design but also on students\u0026rsquo; readiness for self-directed learning. Students with strong self-management and adaptability were better positioned to succeed, while those lacking these skills often struggled. This aligns with international evidence that SDLR is a critical predictor of persistence and satisfaction in distance education (Theobald et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results further validate the Community of Inquiry framework, showing that teaching, social, and cognitive presence remain relevant even in modular environments. Teaching presence in the form of clear instructions, guidance, and timely feedback proved especially influential. Social presence helped reduce feelings of isolation, while cognitive presence supported deeper engagement with mathematical problem-solving. These findings echo recent meta-analyses highlighting the strong link between presence and satisfaction (Martin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstitutional and Socioeconomic Considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInstitutional support emerged as a decisive factor in mitigating disparities. Schools that invested in module refinement, provided feedback mechanisms, and encouraged peer support created more positive learning experiences. Conversely, under-resourced institutions intensified inequities, especially for students from disadvantaged backgrounds. These findings mirror broader concerns about the digital divide and unequal access to educational resources in developing contexts (Bustillo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRecommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo strengthen modular mathematics education, institutions should prioritize adaptive module design, embed iterative feedback mechanisms, and create opportunities for collaborative learning. Training programs that enhance students\u0026rsquo; SDLR and self-regulated learning strategies may further improve outcomes. At a policy level, targeted interventions addressing socioeconomic inequities such as resource subsidies and community-based learning hubs are essential to ensure equitable access and satisfaction across diverse student populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study\u0026rsquo;s reliance on self-reported data introduces the potential for response bias. Moreover, the focus on tertiary institutions in specific Philippine regions limits generalizability. Future research should consider longitudinal approaches to examine how SDLR and satisfaction evolve and test interventions designed to strengthen both learner readiness and institutional support. Exploring the integration of digital platforms with modular instruction may also offer new insights for hybrid models of mathematics education.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the relationship between self-directed learning readiness (SDLR), learning experiences, and student satisfaction in modular mathematics education within Philippine tertiary institutions. Results demonstrated that SDLR, particularly self-management and adaptability, significantly predicted satisfaction, while teaching, social, and cognitive presences mediated these effects. Institutional and socioeconomic conditions further shaped disparities, revealing that resource availability and institutional support are critical to ensuring equitable learning experiences. By integrating quantitative and qualitative findings, the study confirmed that the effectiveness of modular mathematics education is best understood through the interplay of learner readiness, pedagogical presence, and contextual factors.\u003c/p\u003e\u003cp\u003eThe findings carry important implications for both policy and practice. Institutions must prioritize adaptive module design, timely feedback systems, and collaborative learning opportunities to strengthen teaching and social presence. At the same time, targeted interventions to enhance students\u0026rsquo; SDLR and address socioeconomic barriers are necessary to reduce inequities. For policymakers, the study highlights the urgency of resource support and institutional capacity-building to ensure that modular learning fulfills its promise of inclusive and resilient education. In advancing these recommendations, this research contributes to the broader discourse on sustaining quality and equity in crisis-responsive educational systems, offering lessons that extend beyond mathematics to other disciplines and contexts facing similar challenges.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePractical Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings of this study underscore the need for higher education institutions to design modular mathematics programs that go beyond content delivery by intentionally cultivating students\u0026rsquo; self-directed learning readiness. Practical steps include embedding structured feedback mechanisms, integrating peer collaboration opportunities, and providing orientation or training sessions that strengthen students\u0026rsquo; self-management and adaptability skills. Administrators should invest in module refinement and faculty capacity-building to enhance teaching presence, while also ensuring that resource support reaches socioeconomically disadvantaged learners. For policymakers, the results highlight the importance of addressing systemic inequities\u0026mdash;such as uneven access to technology and learning materials\u0026mdash;through targeted subsidies and infrastructure support. Collectively, these measures can transform modular learning into a more inclusive and effective approach, equipping students with both the competencies and support systems needed to thrive in mathematics education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003e The study was conducted in accordance with internationally accepted ethical standards for research involving human participants. While Kalinga State University does not currently operate a formal institutional review board, the research adhered to established ethical principles, including voluntary participation, informed consent, confidentiality, and the right to withdraw at any stage without penalty.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe author declares that there are no competing financial or non-financial interests that could have influenced the outcomes of this research.\u003c/p\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003eNot applicable, as no person\u0026rsquo;s data are included in this article.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by Kalinga State University, which provided logistical assistance such as transportation to field sites and other essential expenses necessary for data collection. No external funding was received from commercial or private sources.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLalin Abbacan-Tuguic(L.T.) conceived and designed the study, performed data analysis, drafted the manuscript, collected data, conducted literature review search, and manuscript revision. The author reviewed and approved the final version of the manuscript and is accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author extends sincere appreciation to the Department of Education and collaborating higher education institutions for their valuable assistance in data collection and constructive feedback, which strengthened the quality of this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. No publicly available datasets were used in this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlabdulaziz M. The role of digital technology in mathematics education during the COVID-19 pandemic. Int J Eng Educ. 2021;37(2):456\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1504/IJEE.2021.100425\u003c/span\u003e\u003cspan address=\"10.1504/IJEE.2021.100425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmora J, Ochoco M, Anicete R. Student engagement and college experience as the mediators of the relationship between institutional support and academic performance. Digit J Lasallian Res. 2016;12:15\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dlasr.2016.06.003\u003c/span\u003e\u003cspan address=\"10.1016/j.dlasr.2016.06.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAttard C, Holmes K. Blended learning in mathematics education: Lessons from the pandemic. Math Educ Res J. 2021;33(4):699\u0026ndash;721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-021-10543-x\u003c/span\u003e\u003cspan address=\"10.1007/s10639-021-10543-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBallad K, Fajardo M, De Guzman J. Global disruptions in education: Impact and innovation. J Global Educ Policy. 2021;9(1):34\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-021-10543-x\u003c/span\u003e\u003cspan address=\"10.1007/s10639-021-10543-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBustillo E. The challenges of modular learning in the wake of COVID. Educ Sci. 2022;12(10):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/educsci12100677\u003c/span\u003e\u003cspan address=\"10.3390/educsci12100677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCabuquin JC. Modular and online learning satisfaction in mathematics amid COVID-19: Implications for new normal teaching practices. Am J Multidisciplinary Res Innov. 2023;1(6):30\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.54536/ajmri.v1i6.954\u003c/span\u003e\u003cspan address=\"10.54536/ajmri.v1i6.954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDas A. Integrating technology in mathematics education: Challenges and opportunities. J STEM Educ. 2021;22(3):156\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15391523.2021.1929335\u003c/span\u003e\u003cspan address=\"10.1080/15391523.2021.1929335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDepartment of Education. (2020). \u003cem\u003eBasic Education Learning Continuity Plan in the time of COVID-19 (BE-LCP).\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.deped.gov.ph/wp-content/uploads/2020/07/DepEd_LCP_July3.pdf\u003c/span\u003e\u003cspan address=\"https://www.deped.gov.ph/wp-content/uploads/2020/07/DepEd_LCP_July3.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFisher M, King J. The self-directed learning readiness scale for nursing education. Nurse Educ Today. 2010;30(1):65\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nedt.2009.06.013\u003c/span\u003e\u003cspan address=\"10.1016/j.nedt.2009.06.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarrison DR, Anderson T, Archer W. Critical inquiry in a text-based environment: Computer conferencing in higher education. Internet High Educ. 2000;2(2\u0026ndash;3):87\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1096-7516(00)00016-6\u003c/span\u003e\u003cspan address=\"10.1016/S1096-7516(00)00016-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJou Y-T, et al. Assessing cognitive factors of modular distance learning and student satisfaction. Int J Educational Res Open. 2022;3:100215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijedro.2021.100215\u003c/span\u003e\u003cspan address=\"10.1016/j.ijedro.2021.100215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJung I, Lee Y. Attributes, antecedents, and consequences of student satisfaction in online education. Online Learn J. 2018;22(2):36\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24059/olj.v22i2.123\u003c/span\u003e\u003cspan address=\"10.24059/olj.v22i2.123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKock N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int J e-Collaboration. 2020;16(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4018/IJeC.2020010101\u003c/span\u003e\u003cspan address=\"10.4018/IJeC.2020010101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKong SC. Developing self-regulated learning as pedagogy: Implications for institutional practice. Br J Edu Technol. 2023;54(2):469\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bjet.13277\u003c/span\u003e\u003cspan address=\"10.1111/bjet.13277\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi W, Chen Y, Zhang J. The impact of teaching presence on students\u0026rsquo; online learning satisfaction. Front Psychol. 2024;15:1192832. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2024.1192832\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2024.1192832\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim YS, Chen NS, Wong CS. Evaluation and refinement of the self-directed learning readiness scale (SDLRS). Educ Inform Technol. 2024;29(3):3415\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-023-11892-4\u003c/span\u003e\u003cspan address=\"10.1007/s10639-023-11892-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin F, Wu T, Wan L, Xie K. A meta-analysis on the Community of Inquiry presences and learning outcomes in online and blended learning environments. Online Learn J. 2022;26(1):325\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24059/olj.v26i1.2566\u003c/span\u003e\u003cspan address=\"10.24059/olj.v26i1.2566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpringer W. Digital education and self-directed learning readiness: Implications for modular pedagogy post-pandemic. Discover Educ J. 2024;23(1):49\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-024-12755-3\u003c/span\u003e\u003cspan address=\"10.1007/s10639-024-12755-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTheobald M, et al. Self-regulated learning training programs enhance academic performance and SRL strategies: A meta-analysis. Educational Psychol Rev. 2021;33(2):559\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10648-020-09546-z\u003c/span\u003e\u003cspan address=\"10.1007/s10648-020-09546-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurk M. How can presences and basic needs happily meet online? Int Rev Res Open Distrib Learn. 2022;23(4):49\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.19173/irrodl.v23i4.6320\u003c/span\u003e\u003cspan address=\"10.19173/irrodl.v23i4.6320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker R. Satisfaction scale of enjoyment of distance education. J Distance Learn Evaluation. 2003;21(2):56\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jde.2003.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jde.2003.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWertz FJ. Web-based teaching and learning link to social and cognitive presence. J Online Learn Res. 2022;8(3):211\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jolr.2022.09.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jolr.2022.09.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Bank. Digital divide and education: Lessons from COVID-19. World Bank Policy Note; 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://documents.worldbank.org\u003c/span\u003e\u003cspan address=\"https://documents.worldbank.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Self-Directed Learning Readiness, Modular Mathematics Education, Student Satisfaction, Community of Inquiry, Socioeconomic Factors","lastPublishedDoi":"10.21203/rs.3.rs-7327750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7327750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the relationship between self-directed learning readiness (SDLR), learning experiences, and student satisfaction in modular mathematics education among tertiary students in the Philippines. Using a mixed-method design, quantitative data were collected through validated survey instruments, while qualitative insights were gathered from semi-structured interviews. Structural equation modeling revealed that SDLR, particularly self-management and adaptability, significantly predicted student satisfaction, both directly and indirectly through the mediating effects of teaching, social, and cognitive presence as outlined in the Community of Inquiry framework. Qualitative findings reinforced these results, highlighting the dual nature of modular learning as both a source of flexibility and a challenge due to the limited feedback and isolation it entails. Institutional and socioeconomic factors further moderated outcomes, with disparities in resources and support contributing to uneven experiences across students. The study highlights the importance of adaptive module design, responsive feedback systems, and targeted institutional interventions in enhancing satisfaction and equity in modular mathematics education.\u003c/p\u003e","manuscriptTitle":"Self-Directed Learning Readiness and Student Satisfaction in Modular Mathematics Education in Philippine Tertiary Institutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 07:30:58","doi":"10.21203/rs.3.rs-7327750/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":"3aa21eda-b73a-462e-8892-05a117f602e2","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T12:55:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 07:30:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7327750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7327750","identity":"rs-7327750","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}