UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics
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Computer Science > Machine Learning
arXiv:2608.03811 (cs)
[Submitted on 4 Aug 2026]
Title:UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics
Authors:Carson J. Cook, Ahmed J. Zerouali, Anthony Schmidt, Reginald Ziedzor, Paul Lin, Luke G. Eglington
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Abstract:We introduce the Unified Neural Variational Measurement of Proficiency (UNVaMP) architecture, a knowledge tracing method that integrates observed student-item interactions with internal memory to produce evolving latent representations of student knowledge. These representations support accurate predictions of future responses while enabling explicit control over the smoothness of estimated learning trajectories. UNVaMP can be configured as either a purely neural model or a hybrid model that predicts responses through an interpretable measurement function over the latent space. We show that a pure neural configuration (UNVaMP-MLP) achieves the strongest predictive performance among compared models on three out of four datasets. Meanwhile, a hybrid configuration (UNVaMP-MIRT, using a 1PL MIRT measurement function) lags only slightly behind UNVaMP-MLP, indicating that the predictive cost of interpretability is modest.
Beyond predictive accuracy, UNVaMP provides the following: a principled mechanism for controlling volatility when estimating student latent variables, quantification of uncertainty over student knowledge state estimates, and flexible input specification that supports heterogeneous student-item interaction features. In addition, the hybrid UNVaMP-MIRT configuration generates interpretable moment-in-time student knowledge state estimates. Using an experimental dataset, we show that auxiliary inputs induce structured changes in the predictive behavior of UNVaMP-MIRT, consistent with sensitivity to underlying structure beyond response correctness. Furthermore, through a simulation study, we show that UNVaMP yields well-behaved knowledge state estimates under controlled measurement conditions. In total, these results indicate that UNVaMP is both useful for real-world education systems and capable of recovering underlying structure from student-item interactions.
| Comments: | 12 pages, 4 figures, Proceedings of the 19th International Conference on Educational Data Mining , Seoul, Republic of Korea, June-2026 |
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY) |
| Cite as: | arXiv:2608.03811 [cs.LG] |
| (or arXiv:2608.03811v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.03811
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 19th International Conference on Educational Data Mining, 353-364, 2026 |
| Related DOI: | https://doi.org/10.5281/zenodo.21040074
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View a PDF of the paper titled UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics, by Carson J. Cook and 5 other authors
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