ISSN: 2225-8329
Open access
These factors affect students' retention in open online flexible distance learning environments, focusing on performance feedback, lecturer quality, course design, and academic support services. Understanding retention is crucial as it impacts educational outcomes and institutional success. The study uses Self-Determination Theory as the theoretical basis to investigate how these elements, mediated by student satisfaction, influence retention. Data was gathered through a survey distributed to distance learning students, yielding 433 valid responses for analysis. Structural Equation Modeling (SEM) via Smartpls4 was used for data analysis due to its efficiency with complex multivariate data. Hypotheses testing showed that while performance feedback had a less significant direct effect, lecturer quality, course design, and academic support services substantially impacted student satisfaction and retention. Student satisfaction emerged as a critical mediator with the most significant effect on retention. The study suggests that future research should investigate integrating advanced technologies like AI to enhance feedback and engagement in distance learning. Comparative studies in diverse educational settings could deepen understanding of cultural influences on retention, while longitudinal studies might reveal the long-term effects of these strategies. The study's implications are significant for educators and policymakers, guiding efforts to enhance retention rates by improving lecturer quality, course design, and support services while focusing on student satisfaction.
Al Hassani, A. A., & Wilkins, S. (2022). Student retention in higher education: the influences of organizational identification and institution reputation on student satisfaction and behaviors. International Journal of Educational Management, 36(6), 1046-1064.
Bangert, A. W. (2004). The seven principles of good practice: A framework for evaluating online teaching. The Internet and Higher Education, 7(3), 217–232.
Bukhatir, A., Al-Hawari, M. A., Aderibigbe, S., Omar, M., & Alotaibi, E. (2023). Improving student retention in higher education institutions–Exploring the factors influencing employees' extra-role behavior. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100128.
Chang, D. F., & Chou, W. C. (2021). Detecting the institutional mediation of push–pull factors on international students’ satisfaction during the COVID-19 pandemic. Sustainability, 13(20), 11405.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033- 2909.112.1.155
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.
Dubey, P., & Sahu, K. K. (2022). Mediation analysis of students' perceived benefits in predicting their satisfaction to technology-enhanced learning. Journal of Research in Innovative Teaching & Learning, 16(1), 82-99.
Fisher, R., Perényi, A., & Birdthistle, N. (2021). The positive relationship between flipped and blended learning and student engagement, performance and satisfaction. Active Learning in Higher Education, 22(2), 97-113.
Geier, M. T. (2021). Students’ expectations and students’ satisfaction: The mediating role of excellent teacher behaviors. Teaching of Psychology, 48(1), 9-17.
Geier, M. T. (2021). Students’ expectations and students’ satisfaction: The mediating role of excellent teacher behaviors. Teaching of Psychology, 48(1), 9-17.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least quares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: SAGE.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3 ed.). Thousand Oaks, CA: Sage.
Hair, J.F., M. Sarstedt, C.M. Ringle, and S.P. Gudergan. (2018). Advanced issues in partial least squares structural equation modeling. Thousand Oakes, CA: Sage Publications
Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 43(1): 115-135.
Intaratat, K., Osman, Z., Nguyen, H. A. T., Suhandoko, A. D. J., & Sultana, N. (2024). Peer and tutor interaction effects on collaborative learning: The role of learning self-efficacy. Edelweiss Applied Science and Technology, 8(4), 2108-2121.
Jain, A., Sharma, P., & Meher, J. R. (2023). Effects of online platforms on learner's satisfaction: a serial mediation analysis with instructor presence and student engagement. The International Journal of Information and Learning Technology, 40(5), 453-466.
Kebah, M., Raju, V., & Osman, Z. (2019). Growth of online purchase in Saudi Arabia retail industry. International Journal of Recent Technology and Engineering, 8(3), 869-872.. ISSN: 2277-3878
Kebah, M., Raju, V., & Osman, Z. (2019). Online purchasing trend in the retail industry in Saudi. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 865-868. ISSN: 2277-3878
Khan, A. U., Khan, K. U., Atlas, F., Akhtar, S., & Khan, F. (2021). Critical factors influencing MOOCs retention: The mediating role of information technology. Turkish Online Journal of Distance Education, 22(4), 82-101.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.
Kock, N., & Lynn, G.S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.
Leoparjo, F., Harianto, E., Mas’ud, R., Ilyas, G. B., & Hasanah, Y. N. (2023). ASSESSING THE EFFECT OF ONLINE LEARNING SERVICE QUALITY ON CUSTOMER RETENTION THROUGH CUSTOMER SATISFACTION AS MEDIATION VARIABLE IN THE CULINARY STUDY PROGRAM BACHELOR DEGREE DURING THE COVID-19 PANDEMIC. Jurnal Aplikasi Manajemen, 21(2), 534-552.
Li, X. T., Rahman, A., Connie, G., & Osman, Z. (2020). Examining customers' perception of electronic shopping mall's e-service quality. International Journal of Services, Economics and Management, 11(4), 329-346.
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392.
McNaughton-Cassill, M. E., Lopez, S., Knight, C., Perrotte, J., Mireles, N., Cassill, C. K., ... & Cassill, A. (2021). Social Support, Coping, Life Satisfaction, and Academic Success Among College Students. Psi Chi Journal of Psychological Research, 26(2).
Nashaat, N., Abd El Aziz, R., & Abdel Azeem, M. (2021). The mediating role of student satisfaction in the relationship between determinants of online student satisfaction and student commitment. Journal of e-Learning and Higher Education, 2021, 1-13.
Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom. Theory and Research in Education, 7(2), 133-144.
Nieuwoudt, J. E., & Pedler, M. L. (2023). Student retention in higher education: Why students choose to remain at university. Journal of College Student Retention: Research, Theory & Practice, 25(2), 326-349.
Osman, Z., Mohamad, W., Mohamad, R. K., Mohamad, L., & Sulaiman, T. F. T. (2018). Enhancing students’ academic performance in Malaysian online distance learning institutions. Asia Pacific Journal of Educators and Education, 33, 19-28.
Otero, M. J. F., Moledo, M. L., Otero, A. G., & Rego, M. A. S. (2021). Students’ mediator variables in the relationship between family involvement and academic performance: Effects of the styles of involvement. Psicología Educativa. Revista de Los Psicólogos de La Educación, 27(1), 85-92.
Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021). Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in Chile. Entropy, 23(4), 485.
Pham, T. T. H., Ho, T. T. Q., Nguyen, B. T. N., Nguyen, H. T., & Nguyen, T. H. (2024). Academic motivation and academic satisfaction: a moderated mediation model of academic engagement and academic self-efficacy. Journal of Applied Research in Higher Education.
Ringle, C. M. 2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems. 116: 1865–1886
Ringle, Christian, M., Wende, S., & Becker, Jan-Michael. (2022). SmartPLS 4. Oststeinbek: SmartPLS. Retrieved from https://www.smartpls.com
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78.
Sánchez?Cardona, I., Ortega?Maldonado, A., Salanova, M., & Martínez, I. M. (2021). Learning goal orientation and psychological capital among students: A pathway to academic satisfaction and performance. Psychology in the Schools, 58(7), 1432-1445.
Santika, R. R., Liswandi, L., & Hidayah, Z. (2021). Mediating Role of Job Satisfaction In Relationship Between Retention, Commitment, Competence In Improving Performance. Jhss (Journal of Humanities and Social Studies), 5(2), 184-189.
Seery, K., Barreda, A. A., Hein, S. G., & Hiller, J. L. (2021). Retention strategies for online students: A systematic literature review. Journal of Global Education and Research, 5(1), 72-84.
Shah, M., Kift, S., & Thomas, L. (2021). Student retention and success in higher education. Springer International Publishing.
Nia, H., Marôco, J., She, L., Khoshnavay Fomani, F., Rahmatpour, P., Stepanovic Ilic, I., ... & Reardon, J. (2023). Student satisfaction and academic efficacy during online learning with the mediating effect of student engagement: A multi-country study. Plos one, 18(10), e0285315.
Shmueli, G., M. Sarstedt, J.F. Hair, J.-H. Cheah, H., Ting, S. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing. 53: 2322–2347.
Shmueli, G., S. Ray, J. M., Estrada, V. (2016). The elephant in the room: predictive performance of PLS models. Journal of Business Research, 69: 4552–4564.
Stone, C., Downing, J., & Dyment, J. (2022). Improving student retention and success within the context of complex lives and diverse circumstances. In Online postgraduate education in a postdigital world: Beyond technology (pp. 161-178). Cham: Springer International Publishing.
Susilawati, E., Khaira, I., & Pratama, I. (2021). Antecedents to student loyalty in Indonesian higher education institutions: the mediating role of technology innovation. Kuram ve Uygulamada Egitim Bilimleri, 21(3), 40-56.
Swani, K., Wamwara, W., Goodrich, K., Schiller, S., & Dinsmore, J. (2022). Understanding business student retention during covid-19: roles of service quality, college brand, and academic satisfaction, and stress. Services Marketing Quarterly, 43(3), 329-352.
Thomas, L., Kift, S., & Shah, M. (2021). Student retention and success in higher education. Student retention and success in higher education: Institutional change for the 21st century, 1-16.
Trivedi, S. (2022). Improving students’ retention using machine learning: Impacts and implications. ScienceOpen Preprints.
Osman, Z., Ali, A., Aziz, R. C., & Yusoof, F. (2025). Improving Distance Learning Students’ Retention: The Role of Students’ Satisfaction through Self-Determination Theory Principles. International Journal of Academic Research in Accounting, Finance and Management Sciences, 15(1), 272–287.
Copyright: © 2025 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode