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All Journal Studies in Learning and Teaching The Indonesian Journal of Computer Science
Elegbeleye, Femi
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Other

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Journal : Studies in Learning and Teaching

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Evaluating Student Activity and Learning on Moodle: A Data-Driven Analysis and Insights of Usage Reports Mbodila, Muneinge; Elegbeleye, Femi
Studies in Learning and Teaching Vol. 6 No. 2 (2025): August
Publisher : CV Sinergi Ilmu dan Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46627/silet.v6i2.623

Abstract

Learning Management Systems (LMS) are extensively utilized to enhance teaching and learning in higher education institutions. These platforms provide invaluable insights into users' usage data and behavior within the online environment. Therefore, evaluating users' activity on these platforms is essential to maximize their effectiveness. This study aims to evaluate usage reports and engagement patterns and analyze online student learning activity using Moodle's tracking features. To achieve this, statistical and visualization techniques were employed to analyze student data from a year-long module delivered in a blended mode during the first semester at a South African university. The study utilized LMS log data to evaluate students' and instructors' usage patterns and engagement levels on the online platform, focusing on module-related activities. The data mining analysis revealed that LMS usage was significantly higher when students were on campus during the first semester and relatively lower when off-campus or in residence. In addition, no significant differences were observed in the type of LMS tools used or module activities across the eight months of the first semester. In conclusion, this data-driven approach and its findings underscore the importance of monitoring LMS activity.
Comparative Evaluation of Adaptive Learning Models for Trustworthy Personalised Education Elegbeleye, Femi; Isong, Bassey
Studies in Learning and Teaching Vol. 6 No. 3 (2025): December
Publisher : CV Sinergi Ilmu dan Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46627/silet.v6i3.698

Abstract

Adaptive learning systems (ALSs) aim to personalize education by adjusting content and learning pathways in response to learner performance and behavior. This study conducts a comparative evaluation of four widely adopted adaptive learning models: Item Response Theory (IRT), Bayesian Networks (BN), Collaborative Filtering (CF), and Reinforcement Learning (RL). The evaluation integrates conceptual analysis and empirical simulation, using the large-scale EdNet dataset comprising over 131 million learner interactions. Each model was implemented in Python and assessed with standard metrics, including accuracy, precision, recall, and F1-score, with class imbalance addressed through SMOTE. Results show that RL consistently achieves the strongest performance across personalization accuracy, adaptability, and responsiveness to learner feedback, particularly under balanced conditions. BN closely follows, offering robust predictive accuracy alongside interpretability and cognitive modelling. CF shows moderate effectiveness, with improvements under SMOTE but limited adaptability in sparse or dynamic environments. IRT consistently performs lowest, maintaining value primarily in assessment contexts. Based on these findings, the study proposes a hybrid RL–BN framework, combining RL’s dynamic personalization with BN’s interpretability to create transparent, scalable, and pedagogically grounded ALSs. The results contribute evidence-based guidance for educators and developers in selecting and integrating adaptive learning models to meet diverse learner and institutional needs.
Co-Authors Esan, Omobayo Ayokunle Ife Elegbeleye Isong, Bassey Jose Lukose Mbodila, Muneinge Mbodila, Munienge Olusegun Oguntona