JISARA

Abstract: This study presents a literature survey on the application of machine learning (ML) in learning management system (LMS) data analytics, aiming to provide insights into adaptive learning development and propose an agenda for future research. The literature survey is based on a proposed adaptive learning framework and critically analyzes the results within this context. The results reveal that machine learning methods can be used to evaluate the effectiveness of instructional interventions and combining online behaviors with textual data can improve the outcome of performance prediction. Key findings also highlight several open issues, including using small datasets and the need for comprehensive ML methods and algorithm development. Future research directions include improving the accuracy of student performance prediction, supporting instructional interventions, enriching student engagement through multimodal LMS data analytics, and leveraging big data and ML approaches for learning behavior pattern detection.

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Recommended Citation: Tu, C., Zhao, G., El-Gayar, O., (2025). Towards Adaptive Learning: A Review of Machine Learning on LMS Data. Journal of Information Systems Applied Research and Analytics 18(2) pp 4-19. https://doi.org/10.62273/IRUN3557