# pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models

arXiv: https://arxiv.org/abs/2206.11460 · HTML: https://mlsanity.com/paper/2206.11460/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing](https://mlsanity.com/paper/2002.07033/) (2020)
- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (2020)
- [RKT : Relation-Aware Self-Attention for Knowledge Tracing](https://mlsanity.com/paper/2008.12736/) (2020)
- [Knowledge Tracing with Sequential Key-Value Memory Networks](https://mlsanity.com/paper/1910.13197/) (2019)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)
- [Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization](https://mlsanity.com/paper/1806.02180/) (2018)
- [GIKT: A Graph-based Interaction Model for Knowledge Tracing](https://mlsanity.com/paper/2009.05991/) (2020)
- [Improving Knowledge Tracing via Pre-training Question Embeddings](https://mlsanity.com/paper/2012.05031/) (2020)
- [Enhancing Knowledge Tracing via Adversarial Training](https://mlsanity.com/paper/2108.04430/) (2021)
- [DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills](https://mlsanity.com/paper/1905.06873/) (2019)
- [Enhancing Item Response Theory for Cognitive Diagnosis](https://mlsanity.com/paper/1905.10957/) (2019)
- [Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing](https://mlsanity.com/paper/1809.08713/) (2018)
- [Knowledge Query Network: How Knowledge Interacts with Skills](https://mlsanity.com/paper/1908.02146/) (2019)
- [Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation](https://mlsanity.com/paper/1604.02336/) (2016)
- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [Option Tracing: Beyond Correctness Analysis in Knowledge Tracing](https://mlsanity.com/paper/2104.09043/) (2021)

## Built on by (most cited first, 30 of 52)

- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations](https://mlsanity.com/paper/2302.06885/) (2023)
- [Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://mlsanity.com/paper/2302.07942/) (2023)
- [Towards Robust Knowledge Tracing Models via k-Sparse Attention](https://mlsanity.com/paper/2407.17097/) (2024)
- [CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer](https://mlsanity.com/paper/2406.10296/) (2024)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [Disentangled Knowledge Tracing for Alleviating Cognitive Bias](https://mlsanity.com/paper/2503.02539/) (2025)
- [Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing](https://mlsanity.com/paper/2410.01727/) (2024)
- [Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation](https://mlsanity.com/paper/2405.12442/) (2024)
- [SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model](https://mlsanity.com/paper/2407.01245/) (2024)
- [Explainable Few-shot Knowledge Tracing](https://mlsanity.com/paper/2405.14391/) (2024)
- [Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model](https://mlsanity.com/paper/2405.16542/) (2024)
- [Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing](https://mlsanity.com/paper/2308.07779/) (2023)
- [A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models](https://mlsanity.com/paper/2403.07322/) (2024)
- [Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI](https://mlsanity.com/paper/2511.06078/) (2025)
- [DKT2: Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale Data](https://mlsanity.com/paper/2501.14256/) (2025)
- [Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing](https://mlsanity.com/paper/2507.11060/) (2025)
- [Addressing Label Leakage in Knowledge Tracing Models](https://mlsanity.com/paper/2403.15304/) (2024)
- [Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information](https://mlsanity.com/paper/2409.20167/) (2024)
- [Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues](https://mlsanity.com/paper/2605.01097/) (2026)
- [Uncertainty-preserving deep knowledge tracing with state-space models](https://mlsanity.com/paper/2407.17427/) (2024)
- [Sparse Binary Representation Learning for Knowledge Tracing](https://mlsanity.com/paper/2501.09893/) (2025)
- [AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling](https://mlsanity.com/paper/2502.11817/) (2025)
- [LLM-driven Effective Knowledge Tracing by Integrating Dual-channel Difficulty](https://mlsanity.com/paper/2502.19915/) (2025)
- [A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings](https://mlsanity.com/paper/2506.09393/) (2025)
- [Language Bottleneck Models for Qualitative Knowledge State Modeling](https://mlsanity.com/paper/2506.16982/) (2025)
- [MemoryKT: An Integrative Memory-and-Forgetting Method for Knowledge Tracing](https://mlsanity.com/paper/2508.08122/) (2025)
- [KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data](https://mlsanity.com/paper/2606.10358/) (2026)
- [SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners](https://mlsanity.com/paper/2206.12036/) (2022)

## Abstract (arXiv)

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which limits experimental standardization. Furthermore, existing DLKT studies often differ in terms of the evaluation protocol and are far away real-world educational contexts. To address these problems, we introduce a comprehensive python based benchmark platform, textscpyKT, to guarantee valid comparisons across DLKT methods via thorough evaluations. The textscpyKT library consists of a standardized set of integrated data preprocessing procedures on 7 popular datasets across different domains, and 10 frequently compared DLKT model implementations for transparent experiments. Results from our fine-grained and rigorous empirical KT studies yield a set of observations and suggestions for effective DLKT, e.g., wrong evaluation setting may cause label leakage that generally leads to performance inflation; and the improvement of many DLKT approaches is minimal compared to the very first DLKT model proposed by Piech et al. citepiech2015deep. We have open sourced textscpyKT and our experimental results at https://pykt.org/. We welcome contributions from other research groups and practitioners.