# Enhancing Item Response Theory for Cognitive Diagnosis

arXiv: https://arxiv.org/abs/1905.10957 · HTML: https://mlsanity.com/paper/1905.10957/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 25 of 25)

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- [Endowing Interpretability for Neural Cognitive Diagnosis by Efficient Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.14399/) (2024)
- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions](https://mlsanity.com/paper/2407.05458/) (2024)
- [ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness](https://mlsanity.com/paper/2401.10749/) (2023)
- [Survey of Computerized Adaptive Testing: A Machine Learning Perspective](https://mlsanity.com/paper/2404.00712/) (2024)
- [Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts](https://mlsanity.com/paper/2308.03377/) (2023)
- [DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis](https://mlsanity.com/paper/2410.17564/) (2024)
- [JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory](https://mlsanity.com/paper/2509.22888/) (2025)
- [Modeling Item Response Theory with Stochastic Variational Inference](https://mlsanity.com/paper/2108.11579/) (2021)
- [Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.04429/) (2023)
- [DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework](https://mlsanity.com/paper/2509.24821/) (2025)
- [Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction](https://mlsanity.com/paper/2106.00263/) (2021)
- [Quiz-based Knowledge Tracing](https://mlsanity.com/paper/2304.02413/) (2023)
- [Active Learning to Guide Labeling Efforts for Question Difficulty Estimation](https://mlsanity.com/paper/2409.09258/) (2024)
- [Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm](https://mlsanity.com/paper/2502.10650/) (2025)
- [LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis](https://mlsanity.com/paper/2505.13492/) (2025)
- [Difficulty-Controllable Multiple-Choice Question Generation Using Large Language Models and Direct Preference Optimization](https://mlsanity.com/paper/2510.19265/) (2025)
- [Augmenting Interpretable Knowledge Tracing by Ability Attribute and Attention Mechanism](https://mlsanity.com/paper/2302.02146/) (2023)
- [A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model](https://mlsanity.com/paper/2504.02643/) (2025)
- [Cognitive Structure Generation: From Educational Priors to Policy Optimization](https://mlsanity.com/paper/2508.12647/) (2025)
- [PersonaPath: Towards Knowledge-Centric Personalized Learning Path Planning](https://mlsanity.com/paper/2609.18861/) (2026)

## Abstract (arXiv)

Cognitive diagnosis is a fundamental and crucial task in many educational applications, e.g., computer adaptive test and cognitive assignments. Item Response Theory (IRT) is a classical cognitive diagnosis method which can provide interpretable parameters (i.e., student latent trait, question discrimination, and difficulty) for analyzing student performance. However, traditional IRT ignores the rich information in question texts, cannot diagnose knowledge concept proficiency, and it is inaccurate to diagnose the parameters for the questions which only appear several times. To this end, in this paper, we propose a general Deep Item Response Theory (DIRT) framework to enhance traditional IRT for cognitive diagnosis by exploiting semantic representation from question texts with deep learning. In DIRT, we first use a proficiency vector to represent students' proficiency in knowledge concepts and embed question texts and knowledge concepts to dense vectors by Word2Vec. Then, we design a deep diagnosis module to diagnose parameters in traditional IRT by deep learning techniques. Finally, with the diagnosed parameters, we input them into the logistic-like formula of IRT to predict student performance. Extensive experimental results on real-world data clearly demonstrate the effectiveness and interpretation power of DIRT framework.