International Journal of
Information and Education Technology

Abstract—Learning style detection is essential for developing adaptive learning tailored to learner preferences. Extensive research has been conducted to address the limitations of direct learner engagement, behavioral-based approaches, and the lack of learner-teacher interaction caused by the shift to online learning. Many learning style models have been widely used and evaluated for their performance in supporting adaptive learning. However, implementing Multiple Intelligence Learning Styles (MILS) remains limited in online learning due to low detection performance. This study employs machine learning approaches to enhance the detection performance of Multiple Intelligence Learning Styles. This study proposes an integrated machine learning framework to enhance MILS classification by combining data preprocessing, log-modulus transformation, and class imbalance handling via Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling Approach (ADASYN), and SMOTE-Tomek. Recursive Feature Elimination (RFE) with Support Vector Machine (SVM) is used for feature selection while eight machine learning models are used for classification. Interactions between learners and the Learning Management System (LMS) comprise the dataset, which are labeled using the Multiple Intelligence Inventory. The SVM model using ADASYN outperforms previous studies and other models, achieving the highest F1-Score of 89%, according to experimental data using 5-fold cross-validation. Statistical tests (Shapiro-Wilk, Analysis of variance (ANOVA), and paired t-tests) confirm significant differences in performance between models. The proposed approach demonstrates enhanced detection performance and can be adapted to broader e-learning applications, supporting adaptive and personalized learning systems.

Keywords—personalized learning, e-learning, learning styles, machine learning, multiple intelligence

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Cite: Gerzon J. Maulany, Paulus I. Santosa, and Indriana Hidayah, "Enhancing the Performance of Multiple Intelligence Learning Styles Prediction in e-Learning Systems Using Machine Learning Techniques," International Journal of Information and Education Technology, vol. 15, no. 12, pp. 2673-2685, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).