# Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation

arXiv: https://arxiv.org/abs/1604.02336 · HTML: https://mlsanity.com/paper/1604.02336/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2016.

## Built on by (most cited first, 17 of 17)

- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models](https://mlsanity.com/paper/2206.11460/) (2022)
- [Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations](https://mlsanity.com/paper/2112.11209/) (2021)
- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Privacy-Preserving Synthetic Educational Data Generation](https://mlsanity.com/paper/2207.03202/) (2022)
- [Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization](https://mlsanity.com/paper/2210.09013/) (2022)
- [LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences](https://mlsanity.com/paper/2105.06266/) (2021)
- [A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining](https://mlsanity.com/paper/2309.04761/) (2023)
- [Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency](https://mlsanity.com/paper/2311.08594/) (2023)
- [Cross-domain-aware Worker Selection with Training for Crowdsourced Annotation](https://mlsanity.com/paper/2406.06977/) (2024)
- [CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog](https://mlsanity.com/paper/2605.19501/) (2026)
- [Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model](https://mlsanity.com/paper/2309.12334/) (2023)
- [Improving Question Embeddings with Cognitive Representation Optimization for Knowledge Tracing](https://mlsanity.com/paper/2504.04121/) (2025)
- [Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing](https://mlsanity.com/paper/2506.02949/) (2025)
- [Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space](https://mlsanity.com/paper/2509.18231/) (2025)
- [Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models](https://mlsanity.com/paper/2602.06542/) (2026)

## Abstract (arXiv)

Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNN-based method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models.