# Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency

arXiv: https://arxiv.org/abs/2311.08594 · HTML: https://mlsanity.com/paper/2311.08594/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [A Conceptual Introduction to Hamiltonian Monte Carlo](https://mlsanity.com/paper/1701.02434/) (2017)
- [Composing graphical models with neural networks for structured representations and fast inference](https://mlsanity.com/paper/1603.06277/) (2016)
- [DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills](https://mlsanity.com/paper/1905.06873/) (2019)
- [Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation](https://mlsanity.com/paper/1604.02336/) (2016)
- [Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling](https://mlsanity.com/paper/2005.00869/) (2020)
- [pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models](https://mlsanity.com/paper/2105.00385/) (2021)
- [T-SKIRT: Online Estimation of Student Proficiency in an Adaptive Learning System](https://mlsanity.com/paper/1702.04282/) (2017)

## Built on by (most cited first, 3 of 3)

- [Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing](https://mlsanity.com/paper/2406.14230/) (2024)
- [Parametric Constraints for Bayesian Knowledge Tracing from First Principles](https://mlsanity.com/paper/2401.09456/) (2023)
- [Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage](https://mlsanity.com/paper/2606.14123/) (2026)

## Abstract (arXiv)

Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that scale poorly to massive datasets. In this work, we propose Variational Temporal IRT (VTIRT) for fast and accurate inference of dynamic learner proficiency. VTIRT offers orders of magnitude speedup in inference runtime while still providing accurate inference. Moreover, the proposed algorithm is intrinsically interpretable by virtue of its modular design. When applied to 9 real student datasets, VTIRT consistently yields improvements in predicting future learner performance over other learner proficiency models.