Abstract

The rapid growth and widespread adoption of e-learning platforms have transformed access to education, yet most systems still rely on one-size-fits-all instructional strategies that fail to account for individual cognitive differences. In addressing this limitation, adaptive e-learning environments increasingly depend on learner models—particularly learning styles—to personalize content delivery and instructional pathways. However, existing methods for identifying learning styles, such as self-report questionnaires and log-based LMS analytics, suffer from subjectivity, sparsity, and limited sensitivity to real-time cognitive engagement. This study develops a machine learning approach that integrates biometric sensors and behavioural data to infer learner preferences more robustly and objectively. Using a custom e-learning platform, we collected synchronized eye-tracking and mouse interaction data alongside assessment performance from undergraduate students engaging with a networking course material. An NBTree-based classification model fused these multi-source features to identify learning styles aligned with the Felder–Silverman framework. The system achieved an accuracy of 88.06% and an R² value of 0.7307, outperforming traditional questionnaire-based methods. The platform’s effectiveness was further evaluated using the four levels of the Kirkpatrick Model, demonstrating high learner satisfaction, substantial learning gains, positive behavioural transfer, and strong perceived relevance. This study contributes a validated baseline for multi-source, ML-driven learner modeling and provides an approach for dynamic, non-intrusive, near real-time personalization in adaptive e-learning systems.

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