Enhancing Student Engagement and Performance Through Personalized Study Plans in Online Learning: A Proof-of-Concept Pilot Study

Abstract

This study examines how interaction data from Learning Management Systems (LMSs) can be leveraged to predict student performance and enhance academic outcomes through personalized study plans tailored to individual learning styles. The research followed three phases: (i) analyzing the relationship between engagement and performance, (ii) developing predictive models for academic outcomes, and (iii) generating customized study plan recommendations. Clustering analysis identified three distinct learner profiles—high-engagement–high-performance, low-engagement–high-performance, and low-engagement–low-performance—with no cases of high-engagement–low-performance, underscoring the pivotal role of engagement in academic success. Among clustering approaches, K-Means produced the most precise grouping. For prediction, Support Vector Machines (SVMs) achieved the highest accuracy (68.8%) in classifying students across 11 grade categories, supported by oversampling techniques to address class imbalance. Personalized study plans, derived using K-Nearest Neighbor (KNN) classifiers, significantly improved student performance in controlled experiments. To the best of our knowledge, this represents a novel attempt in this context to align predictive modeling with the full grading structure of undergraduate programs. These findings highlight the potential of integrating LMS data with machine learning to foster engagement and improve learning outcomes. Future work will focus on expanding datasets, refining predictive accuracy, and incorporating additional personalization features to strengthen adaptive learning.