Automated Detection of Learning Styles using Online Activities and Model Indicators

Abstract

Understanding learning styles is essential for learners and instructors to identify strengths and weaknesses in the education system. Although the Felder-Silverman Learning Style Model (FSLSM) is commonly used for this purpose, its reliance on in-person surveys can be time-consuming and prone to inaccuracies. This paper proposes an automated approach using Machine Learning (ML) to detect learning styles. This method extracts features from online activity data in Learning Management System (LMS) databases, aligning them with FSLSM indicators to label different learning styles. The dataset is divided into training and testing groups, respectively, to build and evaluate Support Vector Machine (SVM) classifiers. Feature selection is performed using the Recursive Feature Elimination (RFE) algorithm to improve the performance of the classifier, which results in the SVM-RFE algorithm. The experimental results showed promising accuracy for all model dimensions, i.e., 95.76% for processing, 85.88% for perception, 93.16% for input, and 96.42% for understanding dimensions. This approach offers a robust framework for automated learning style detection, which significantly reduces reliance on manual surveys and improves efficiency in educational settings.

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