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All Journal International Journal of Machine Learning (IJOML)
Othman Bin Mohd
Center for Advanced Computing Technology (C-ACT), Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
Computer Science & IT Decision Sciences, Operations Research & Management

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Cold-Start Generalization in Educational Interaction Data: Comparing Student-Wise and Question-Wise Splits with Probabilistic Calibration Purwadi Purwadi; Othman Bin Mohd; Nor Azman Bin Abu
International Journal of Machine Learning (IJOML) Vol. 1 No. 1 (2026): IJOML Volume 1, Number 1, June 2026
Publisher : APJIKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ijoml.v1i1.4

Abstract

Predictive models in Intelligent Tutoring Systems often face performance degradation due to sparse data and the cold-start problem, further compounded by a lack of probability calibration in standard evaluations. This study bridges this gap by systematically evaluating the trade-off between discriminative accuracy and probabilistic reliability through student-wise and question-wise splits, utilizing interaction data from the MathE platform across eight countries. By comparing identifier-based and metadata-based Logistic Regression models under a Leave-One-Country-Out protocol, we assessed generalization capabilities against distribution shifts. The results reveal a fundamental dichotomy: while identifier-based models achieve superior precision (AUC 0.687) and calibration in scenarios with historical context, they suffer from significant performance drops in student cold-start settings and exhibit negative transfer during cross-country deployment. Conversely, metadata-based models demonstrate higher robustness and invariance across varying demographics. We conclude that relying solely on accuracy metrics masks model uncertainty in new domains and recommend a "safe-start" strategy that prioritizes metadata-based features for system initialization to ensure reliable pedagogical decision-making before personalizing based on accumulated user history.
Co-Authors Nor Azman Bin Abu Purwadi Purwadi