Accurate Predictions in Education with Discrete Variational Inference

• URL: http://arxiv.org/abs/2509.23484v2
• Date: Tue, 30 Sep 2025 09:43:08 GMT
• Title: Accurate Predictions in Education with Discrete Variational Inference
• Authors: Tom Quilter, Anastasia Ilick, Karen Poon, Richard Turner,
• Abstract summary: Affordable, effective AI tutors offer a scalable solution.<n>We focus on adaptive learning, predicting whether a student will answer a question correctly.<n>We release the largest open dataset of professionally marked formal mathematics exam responses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the largest drivers of social inequality is unequal access to personal tutoring, with wealthier individuals able to afford it, while the majority cannot. Affordable, effective AI tutors offer a scalable solution. We focus on adaptive learning, predicting whether a student will answer a question correctly, a key component of any effective tutoring system. Yet many platforms struggle to achieve high prediction accuracy, especially in data-sparse settings. To address this, we release the largest open dataset of professionally marked formal mathematics exam responses to date. We introduce a probabilistic modelling framework rooted in Item Response Theory (IRT) that achieves over 80 percent accuracy, setting a new benchmark for mathematics prediction accuracy of formal exam papers. Extending this, our collaborative filtering models incorporate topic-level skill profiles, but reveal a surprising and educationally significant finding, a single latent ability parameter alone is needed to achieve the maximum predictive accuracy. Our main contribution though is deriving and implementing a novel discrete variational inference framework, achieving our highest prediction accuracy in low-data settings and outperforming all classical IRT and matrix factorisation baselines.

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