Uncertainty-Aware Knowledge Tracing Models

• URL: http://arxiv.org/abs/2509.21514v1
• Date: Thu, 25 Sep 2025 20:06:02 GMT
• Title: Uncertainty-Aware Knowledge Tracing Models
• Authors: Joshua Mitton, Prarthana Bhattacharyya, Ralph Abboud, Simon Woodhead,
• Abstract summary: We show an approach to add new capabilities to Knowledge Tracing models by capturing predictive uncertainty.<n>We show that uncertainty in KT models is informative and that this signal would be pedagogically useful for application in an educational learning platform.
• Score: 3.8834950760134657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The main focus of research on Knowledge Tracing (KT) models is on model developments with the aim of improving predictive accuracy. Most of these models make the most incorrect predictions when students choose a distractor, leading to student errors going undetected. We present an approach to add new capabilities to KT models by capturing predictive uncertainty and demonstrate that a larger predictive uncertainty aligns with model incorrect predictions. We show that uncertainty in KT models is informative and that this signal would be pedagogically useful for application in an educational learning platform that can be used in a limited resource setting where understanding student ability is necessary.

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