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Performance Evaluation of a Multi-Level Approach to Predict Learning Styles In E-Learning System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 85 |
| Year of Publication: 2026 |
| Authors: Oluwatoyin C. Agbonifo, Daniel S. Faremi |
10.5120/ijca2026926139
|
Oluwatoyin C. Agbonifo, Daniel S. Faremi . Performance Evaluation of a Multi-Level Approach to Predict Learning Styles In E-Learning System. International Journal of Computer Applications. 187, 85 ( Feb 2026), 1-7. DOI=10.5120/ijca2026926139
Abstract
Machine learning algorithms have been widely used for predicting learning styles in personalized e-learning systems. This paper evaluates effectiveness of a multi-level model using K-Means and Decision Trees algorithms to cluster learners into groups based on their characteristics and classify learners into the learning style dimensions of the Felder-Silverman learning styles model (FSLSM). Learner interaction data extracted from a Moodle Learning Management System (LMS) was pre-processed and used as input for K-Means clustering to group learners according to behavioural similarities. The resulting clusters were used to train and test a Decision Tree classifier that labelled each learner’s preferred learning style based on the FSLSM. The model was evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. Evaluation results show that the proposed model achieved a 95% overall accuracy, with an emphasis on correctly identifying the learning style category across FSLSM dimensions, demonstrating the strong predictive performance of the proposed multi-level model in supporting automated learning style prediction.
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