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All Journal Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan EDUMATIC: Jurnal Pendidikan Informatika Jurnal Review Pendidikan dan Pengajaran (JRPP) Progresif: Jurnal Ilmiah Komputer INFORMASI (Jurnal Informatika dan Sistem Informasi) Jurnal Sistem Informasi dan Sains Teknologi
Yanti Apriyani
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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An Object-Oriented Bayesian Knowledge Tracing Framework for Adaptive Learning Miftah Farid Adiwisastra; Yani Sri Mulyani; Yanti Apriyani
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i2.27174

Abstract

Background: Bayesian Knowledge Tracing (BKT) is widely used in adaptive learning systems to model students’ knowledge mastery over time. However, conventional procedural implementations of BKT often suffer from limited modularity, maintainability, and integration capability, making them less suitable for modern adaptive learning environments. Objective: This study aims to design and evaluate an Object-Oriented Programming (OOP)-based BKT framework to improve software modularity and predictive performance in adaptive learning systems. Methods: The proposed framework applies OOP principles by decomposing the BKT model into modular classes, including StudentModel, Skill, and BKTUpdater. The framework was implemented in Python and evaluated using student interaction datasets through a 5-fold cross-validation approach. Performance was assessed using Accuracy, Area Under the Curve (AUC), and Root Mean Square Error (RMSE). Results: Experimental results demonstrate that the proposed OOP-based BKT framework outperforms the conventional implementation. The model achieved an improvement of approximately 1.53% in Accuracy and 1.06% in AUC, while reducing RMSE by 2.85%. Although OOP does not alter the mathematical formulation of BKT, it enables more structured parameter management, reduces implementation inconsistencies, and facilitates systematic experimentation, which indirectly contributes to enhanced predictive performance. Conclusion: The integration of OOP principles into BKT development improves both software architecture quality and model reliability in adaptive learning systems. The proposed framework provides a scalable, maintainable, and extensible foundation for future adaptive learning technologies while achieving better predictive performance than conventional BKT implementations.
Co-Authors Agung Baitul Hikmah Ai Ilah Warnilah Alawiyah, Tuti Attarik Ramanda Atyla Azfa Al Harits Bambang Kelana Simpony Hendri Mardani Imam Amirulloh Iskandar, Iqbal Dzulfiqar Melisa Winda Pertiwi Miftah Farid Adiwisastra Mira Kusmira Muhammad Dhafa Alfareza Ratningsih, Ratningsih Rezkiani Rezkiani Shelin Ayuni Purwandhani Taufik Wibisono Vincent Christian Yani Sri Mulyani Yusuf Sumaryana