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Institut Sains & Teknologi AKPRIND Yogyakarta
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SINGLE-LABEL LEARNING STYLE CLASSIFICATION USING MACHINE LEARNING WITH GRIDSEARCH-BASED HYPERPARAMETER TUNING ON LMS BEHAVIORAL DATA Lestari, Uning; Salam, Sazilah; Choo, Yun Huoy
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1876

Abstract

The rapid growth of online learning environments has increased the importance of Learning Management Systems (LMS) as a rich source of behavioral data for learning analytics. One learner characteristic that strongly influences learning effectiveness is learning style; however, traditional questionnaire based identification approaches suffer from subjectivity, limited scalability, and static representation. To address these limitations, this study proposes a machine learning-based approach for automatic learning style classification using LMS behavioral data grounded in the Felder–Silverman Learning Style Model (FSLSM). This study utilizes LMS activity log data collected from Universitas Siber Asia over three academic years (2022–2024). The dataset consists of 5,633 student interaction records with 72 raw behavioral attributes, which were preprocessed, aggregated, and transformed into 12 representative behavioral features reflecting students’ interactions with learning materials, assessments, discussions, multimedia resources, and navigation patterns. A rule-based FSLSM mapping mechanism was applied to generate 16 learning style profiles, which were treated as targets in a single-label classification setting. Support Vector Machine (SVM) and Gradient Boosting (GB) classifiers were implemented and optimized using feature selection and GridSearch-based hyperparameter tuning. The dataset was divided into 75% training data and 25% testing data using a stratified split to preserve class distribution. Experimental results show that Gradient Boosting consistently outperforms SVM across all evaluation metrics. The GB model achieved an accuracy of 0.84 and a macro F1-score of 0.79, demonstrating strong generalization capability and robustness to class imbalance. In contrast, SVM exhibited lower and less stable performance, particularly on minority learning style classes. These findings confirm that ensemble-based methods such as Gradient Boosting are more effective for LMS-based single-label learning style classification and support the feasibility of automatic FSLSM-based learning style detection for data-driven adaptive learning systems.
Co-Authors -, Marwoto -, Marwoto Abdulloh, Yusuf Agusalim Syamsudin Pure Ahmad Fesol, Siti Feirusz Ahmad Zarkasi Akhir, Muhammad Al Qallab, Kholoud Alomoush, Ashraf Amannu, Ramadhan Amir Hamzah Amir Hamzah Andri Harsono Andri Harsono, Andri Andung Febi Prakoso Anggraeni, Ari Puspratini Annafi’ Franz Aprilianti, Yunis Aprilianti Ardiansyah - Arga, Dwi Asih Sapta Arifuddin, Arham Ariyana, Renna Yanwastika Asti Widyaningsih Aziz Nurwahidin Bondan Prawiro Yudo, Bondan Prawiro Cardoso, Noel Adriano Catur Iswahyudi Choo, Yun Huoy Choo, Yun-Huoy Dani Heriyanto Dani Yulkarnain Debby Anugrahni Deby Saputra, Deby Dede Hernowo Deserius Marianus Oenunu Dina Andayati Dina Andayati Dini Pujiatin Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Effendy, Vina Ardelia Eko Budianto Erfanti Fatkhiyah Erfanty Fatkhiyah Erma Susanti Erna Kumalasari Erna Kumalasari Nurnawati Erna Kumalasari Nurnawati Erni Astuti Firmansyah Surwa Adi L Galuh Ayu Novilia Hari Wibowo Hendrati, Rr. Dina Oktavia Indra Kurniawan Ismail, Nurmaisarah iswanto Iswayudi, Catur Jepri Ardianto Joko Triyono Juliyanti, Nur Arifah Kar Mee, Cheong Laksono Trisnantoro Lip, Rashidah Listyaningrum, Desti Arghina Luay Nabila El Suffa M. Abdul Alim Alami Mohamad, Siti Nurul Mahfuzah Mohd Yusoff, Azizul Muchamad Rizal Rinaldi MUHAMMAD SHOLEH Muhammad Targiono Muntaha Nega Murien Nugraheni Naniek Widyastuti Naniek Widyastuti Nurmansyah Oktavina Marlina Roma Paays, Franco Albertino Karel Parasian D.P Silitonga Poh Ee, Tan Prastika, Dika Priska Prihsmoro1, Catur Dwi Prita Haryani Pujiatin, Dini Rendi Saputra Rr Yuliana Rachmawati K RR. Yuliana Rachmawati Salam, Sazilah Saldanha, Paulino Sholeh, Muhammad Siti Saudah Sony Cahyo Wibisono Sony Cahyo Wibisono, Sony Cahyo Sugiyatno Sugiyatno Supriyanri, Sri Suraya Suraya Suwanto Raharjo Triyono, Joko Utami Hayati Victor Motumona Wafikulinuha Wafikulinuha Waliadi, Julfikar Wandy Damarullah Widodo Widodo Wiwik Handayani Yeremias Budi Liman Hege Yeremias Budi Liman Hege, Yeremias Budi Liman Yunanto, Prasetyo Wibowo Yunis Aprilianti Yusron - Zulfikar .L, Fauzul Rachman