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Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://ds.knu.edu.ua/jspui/handle/123456789/9291
Назва: Differential effects of Moodle course design on student subpopulations: advancing personalized learning in higher education
Автори: Semerikov, Serhiy O.
Nechypurenko, Pavlo P.
Vakaliuk, Tetiana A.
Mintii, Iryna S.
Fadieieva, Liliia O.
Ключові слова: Moodle
Course design
Student subpopulations
Personalized learning
Higher education
Educational equity
Дата публікації: 1-сер-2025
Видавництво: Springer
Бібліографічний опис: Differential effects of Moodle course design on student subpopulations: advancing personalized learning in higher education / Serhiy O. Semerikov, Pavlo P. Nechypurenko, Tetiana A. Vakaliuk, Iryna S. Mintii and Liliia O. Fadieieva // Smart Learning Environments. – 2025. – Vol. 12. – Article 46. – DOI : https://doi.org/10.1186/s40561-025-00400-6
Короткий огляд (реферат): Higher education institutions face increasing challenges in meeting the diverse needs of their student populations. While learning management systems (LMS) like Moodle have become ubiquitous in higher education, there remains limited understanding of how these platforms affect different student subpopulations. This study investigates the differential effects of Moodle course design elements on student outcomes across key demographic dimensions: form of education (full-time versus part-time) and educational level (undergraduate versus graduate). Using structural equation modeling and a dataset of 3684 courses from a European university (2020–2022), we identified significant differences in how Moodle components affect learning outcomes across student subgroups. Interactive activities and communication tools showed substantially stronger relationships with outcomes for part-time students, while comprehensive information and resources demonstrated stronger impacts for graduate students. We introduce a theoretical framework for personalization effects in LMS environments and identify specific design elements that promote more equitable outcomes across student subpopulations. This research addresses critical gaps in our understanding of personalized learning in higher education, providing evidence-based guidance for instructors and instructional designers seeking to create more inclusive, effective digital learning environments that respond to the specific needs of diverse student populations.
Опис: Alam, A., & Mohanty, A. (2022). Foundation for the Future of Higher Education or ‘Misplaced Optimism’? Being Human in the Age of Artificial Intelligence. M. Panda et al. (Eds.), Innovations in Intelligent Computing and Communication (Vol. 1737, pp. 17–29). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-23233-6_2 Alamri, H. A., Watson, S., & Watson, W. (2021). Learning technology models that support personalization within blended learning environments in higher education. TechTrends, 65(1), 62–78. https://doi.org/10.1007/s11528-020-00530-3 Alotaibi, N. S. (2024). The impact of AI and LMS integration on the future of higher education: Opportunities, challenges, and strategies for transformation. Sustainability, 16(23), 357. https://doi.org/10.3390/su162310357 Artino, A. R., Jr., & Stephens, J. M. (2009). Academic motivation and self-regulation: A comparative analysis of undergraduate and graduate students learning online. Internet and Higher Education, 12(3–4), 146–151. https://doi.org/10.1016/j.iheduc.2009.02.001 Bayer, V., Mulholland, P., Hlosta, M., Farrell, T., Herodotou, C., & Fernandez, M. (2024). Co-creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education. British Journal of Educational Technology, 55(5), 2058–2074. https://doi.org/10.1111/bjet.13509 Cavanagh, T., Chen, B., Lahcen, R.A.M., & Paradiso, J. (2020). Constructing a Design Framework and Pedagogical Approach for Adaptive Learning in Higher Education: A Practitioner’s Perspective. The International Review of Research in Open and Distributed Learning, 21(1), 173–197,https://doi.org/10.19173/irrodl.v21i1.4557 Clarida, B.H., Bobeva, M., Hutchings, M., & Taylor, J. (2013). Strategies for Digital Inclusion: Towards a Pedagogy for Embracing and Sustaining Student Diversity and Engagement with Online Learning. Proceedings of the European Conference on e-Learning, ECEL (573–580). https://papers.iafor.org/wp-content/uploads/papers/ece2014/ECE2014_00610.pdf Cristea, T. S., Heikkinen, S., Snijders, C., Saqr, M., Matzat, U., Conijn, R., & Kleingeld, A. (2025). Dynamics of self-regulated learning: The effectiveness of students’ strategies across course periods. Computers and Education, 228, Article 105233. https://doi.org/10.1016/j.compedu.2025.105233 Dalsgaard, C., & Ryberg, T. (2023). A theoretical framework for digital learning spaces: learning in individual spaces, working groups, communities of interest, and open connections. Research in Learning Technology, 31, 3084, https://doi.org/10.25304/rlt.v31.3084 Dieter, K.C., Studwell, J., & Vanacore, K.P. (2020). Differential Responses to Personalized Learning Recommendations Revealed by Event-Related Analysis. Proceedings of the 13th International Conference on Educational Data Mining, EDM 2020 (736–742). https://eric.ed.gov/?id=ED607826 Duncan, K., Kenworthy, A., & McNamara, R. (2012). The effect of synchronous and asynchronous participation on students’ performance in online accounting courses. Accounting Education, 21(4), 431–449. https://doi.org/10.1080/09639284.2012.673387 du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), Article e39630. https://doi.org/10.1016/j.heliyon.2024.e39630 Elliott, R., & Luo, X. (2022). Learning Management System Analytics to Examine the Behavior of Students in High Enrollment STEM Courses During the Transition to Online Instruction. 2022 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/FIE56618.2022.9962732 Fadieieva, L., & Semerikov, S. (2024). Exploring the Interplay of Moodle Tools and Student Learning Outcomes: A Composite-Based Structural Equation Modelling Approach. E. Faure et al. (Eds.), Information technology for education, science, and technics (222, 418–435). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-71804-5_28 Gebhardt, K., & Blake, C. D. (2024). Closing the gap? The ability of adaptive learning courseware to close outcome gaps in principles of microeconomics. Education Sciences, 14(9), 1025. https://doi.org/10.3390/educsci14091025 Gourlay, L., & Oliver, M. (2018). Student Engagement in the Digital University: Sociomaterial Assemblages. London: Routledge. https://doi.org/10.4324/9781315647524 Book Green, R. A., Farchione, D., Hughes, D. L., & Chan, S.-P. (2014). Participation in asynchronous online discussion forums does improve student learning of gross anatomy. Anatomical Sciences Education, 7(1), 71–76. https://doi.org/10.1002/ase.1376 Guo, Y., & Lee, D. (2023). Differential usage of learning management systems in chemistry courses in the time after COVID-19. Journal of Chemical Education, 100(5), 2033–2038. https://doi.org/10.1021/acs.jchemed.2c00850 Hardesty, J., McWilliams, J., & Plucker, J.A. (2014). Excellence gaps: what they are, why they are bad, and how smart contexts can address them... or make them worse. High Ability Studies, 25(1), 71–80 https://doi.org/10.1080/13598139.2014.907646 Hooshyar, D., Weng, X., Sillat, P. J., Tammets, K., Wang, M., & Hämäläinen, R. (2024). The effectiveness of personalized technology-enhanced learning in higher education: A meta-analysis with association rule mining. Computers and Education, 223, Article 105169. https://doi.org/10.1016/j.compedu.2024.105169 Inglis, M., Palipana, A., Trenholm, S., & Ward, J. (2011). Individual differences in students’ use of optional learning resources. Journal of Computer Assisted Learning, 27(6), 490–502. https://doi.org/10.1111/j.1365-2729.2011.00417.x Issa, T., Isaias, P., & Kommers, P. (Eds.). (2014). Multicultural Awareness and Technology in Higher Education: Global Perspectives. Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-4666-5876-9 Jordan, C. (2013). Comparison of International Baccalaureate (IB) chemistry students’ preferred vs actual experience with a constructivist style of learning in a Moodle e-learning environment. International Journal for Lesson and Learning Studies, 2(2), 155–167. https://doi.org/10.1108/20468251311323397 Kelly, A. (2015). Measuring equity in educational effectiveness research: the properties and possibilities of quantitative indicators. International Journal of Research and Method in Education, 38(2), 115–136. https://doi.org/10.1080/1743727X.2014.914486 Khor, E. T., & Mutthulakshmi, K. (2024). A systematic review of the role of learning analytics in supporting personalized learning. Education Sciences, 14(1), 51. https://doi.org/10.3390/educsci14010051 Li, Y., Wang, Q., & Campbell, J. (2015). Investigating gender and racial/ethnic invariance in use of a course management system in higher education. Education Sciences, 5(2), 179–198. https://doi.org/10.3390/educsci5020179 Lohr, A., Stadler, M., Schultz-Pernice, F., Chernikova, O., Sailer, M., Fischer, F., & Sailer, M. (2021). On powerpointers, clickerers, and digital pros: Investigating the initiation of digital learning activities by teachers in higher education. Computers in Human Behavior, 119, Article 106715. https://doi.org/10.1016/j.chb.2021.106715 Miller, S. M., Lauver, K. J., & Drum, D. M. (2011). Is this what I signed up for? MBA and undergraduate business student perspectives of online courses. International Journal of Management in Education, 5(4), 363–383. https://doi.org/10.1504/IJMIE.2011.042888 Mödritscher, F., Andergassen, M., & Neumann, G. (2013). Dependencies between E-Learning Usage Patterns and Learning Results. Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2494188.2494206 Mogase, R.C., & Alexander, P.M. (2018). An Interactive Mobile Computing Model to Enhance Personalized learning for At-risk Students in South African Higher Learning. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018 (pp. 1000–1006). https://doi.org/10.1109/IEMCON.2018.8614838 Moodle (2006). Philosophy. URL: https://web.archive.org/web/20060901040155/http://docs.moodle.org/en/Philosophy Pei, L., & Wu, H. (2019). Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Medical Education Online, 24(1), 1666538. https://doi.org/10.1080/10872981.2019.1666538 Pillai, K. R., Upadhyaya, P., Balachandran, A., & Nidadavolu, J. (2019). Versatile learning ecosystem: A conceptual framework. Higher Education for the Future, 6(1), 85–100. https://doi.org/10.1177/2347631118802653 Ramírez-Correa, P. E., Rondan-Cataluña, F. J., Arenas-Gaitán, J., & Alfaro-Perez, J. L. (2017). Moderating effect of learning styles on a learning management system’s success. Telematics and Informatics, 34(1), 272–286. https://doi.org/10.1016/j.tele.2016.04.006 Robinson, D. H. (2021). A complete SMOCkery: Daily online testing did not boost college performance. Educational Psychology Review, 33(3), 1213–1220. https://doi.org/10.1007/s10648-020-09588-0 Saqr, M., López-Pernas, S., Jovanović, J., & Gašević, D. (2023). Intense, turbulent, or wallowing in the mire: A longitudinal study of cross-course online tactics, strategies, and trajectories. Internet and Higher Education, 57, Article 100902. https://doi.org/10.1016/j.iheduc.2022.100902 Schmitz, M., van Limbeek, E., Greller, W., Sloep, P., & Drachsler, H. (2017). Opportunities and Challenges in Using Learning Analytics in Learning Design. É. Lavoué, H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Data Driven Approaches in Digital Education (Vol. 10474, pp. 209–223). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-66610-5_16 Schoenherr, J.R. (2021). Designing Ethical Agency for Adaptive Instructional Systems: The FATE of Learning and Assessment. R.A. Sottilare, & J. Schwarz (Eds.), Adaptive Instructional Systems. Design and Evaluation (Vol. 12792, pp. 265–283). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-77857-6_18 Shute, V.J., & Zapata-Rivera, D. (2012). Adaptive Educational Systems. P.J. Durlach, & A.M. Lesgold (Eds.), Adaptive Technologies for Training and Education (pp. 7–27). Cambridge University Press. https://doi.org/10.1017/CBO9781139049580.004 Stasolla, F., Zullo, A., Maniglio, R., Passaro, A., Di Gioia, M., Curcio, E., & Martini, E. (2025). Deep Learning and Reinforcement Learning for Assessing and Enhancing Academic Performance in University Students: A Scoping Review. AI, 6(2), 40, https://doi.org/10.3390/ai6020040 Swart, A. J., & Hertzog, P. E. (2022). Access to a learning management system by full-time and part-time students reveals notable differences. World Transactions on Engineering and Technology Education, 20(1), 66–70. Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing personalized education: A dynamic framework. Educational Psychology Review, 33(3), 863–882. https://doi.org/10.1007/s10648-020-09570-w Trayek, F. A. A., & Hassan, S. S. S. (2013). Attitude towards the use of learning management system among university students: A case study. Turkish Online Journal of Distance Education, 14(3), 91–103. Wood, E., De Pasquale, D., Mueller, J. L., Archer, K., Zivcakova, L., Walkey, K., & Willoughby, T. (2016). Exploration of the Relative Contributions of Domain Knowledge and Search Expertise for Conducting Internet Searches. Reference Librarian, 57(3), 182–204. https://doi.org/10.1080/02763877.2015.1122559 Wu, M.-D., & Chen, S.-C. (2012). How graduate students perceive, use, and manage electronic resources. Aslib Proceedings: New Information Perspectives, 64(6), 641–652. https://doi.org/10.1108/00012531211281779 Zheng, L., Long, M., Zhong, L., & Gyasi, J. F. (2022). The effectiveness of technology-facilitated personalized learning on learning achievements and learning perceptions: a meta-analysis. Education and Information Technologies, 27(8), 11807–11830. https://doi.org/10.1007/s10639-022-11092-7 Zhong, L. (2023). A systematic review of personalized learning in higher education: Learning content structure, learning materials sequence, and learning readiness support. Interactive Learning Environments, 31(10), 7053–7073. https://doi.org/10.1080/10494820.2022.2061006
URI (Уніфікований ідентифікатор ресурсу): https://doi.org/10.1186/s40561-025-00400-6
http://ds.knu.edu.ua/jspui/handle/123456789/9291
ISSN: 2196-7091
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