# A Survey of Knowledge Tracing: Models, Variants, and Applications

arXiv: https://arxiv.org/abs/2105.15106 · HTML: https://mlsanity.com/paper/2105.15106/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 151 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing](https://mlsanity.com/paper/2002.07033/) (2020)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (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)
- [Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing](https://mlsanity.com/paper/1811.03388/) (2018)
- [Exploiting Cognitive Structure for Adaptive Learning](https://mlsanity.com/paper/1905.12470/) (2019)
- [Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing](https://mlsanity.com/paper/1809.08713/) (2018)
- [QuesNet: A Unified Representation for Heterogeneous Test Questions](https://mlsanity.com/paper/1905.10949/) (2019)
- [Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling](https://mlsanity.com/paper/2005.00869/) (2020)
- [Towards Interpretable Deep Learning Models for Knowledge Tracing](https://mlsanity.com/paper/2005.06139/) (2020)
- [Option Tracing: Beyond Correctness Analysis in Knowledge Tracing](https://mlsanity.com/paper/2104.09043/) (2021)
- [Tracing Player Knowledge in a Parallel Programming Educational Game](https://mlsanity.com/paper/1908.05632/) (2019)

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

- [Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task](https://mlsanity.com/paper/2406.02893/) (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)
- [Position: LLMs Can be Good Tutors in English Education](https://mlsanity.com/paper/2502.05467/) (2025)
- [Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating](https://mlsanity.com/paper/2505.19543/) (2025)
- [Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing](https://mlsanity.com/paper/2507.11060/) (2025)
- [A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining](https://mlsanity.com/paper/2309.04761/) (2023)
- [KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students](https://mlsanity.com/paper/2402.12291/) (2024)
- [From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education](https://mlsanity.com/paper/2409.00323/) (2024)
- [Language Bottleneck Models for Qualitative Knowledge State Modeling](https://mlsanity.com/paper/2506.16982/) (2025)
- [HISE-KT: Synergizing Heterogeneous Information Networks and LLMs for Explainable Knowledge Tracing with Meta-Path Optimization](https://mlsanity.com/paper/2511.15191/) (2025)
- [RAG-KT: Cross-platform Explainable Knowledge Tracing with Multi-view Fusion Retrieval Generation](https://mlsanity.com/paper/2604.10960/) (2026)
- [Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method](https://mlsanity.com/paper/2501.05605/) (2025)
- [Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education](https://mlsanity.com/paper/2506.00057/) (2025)
- [Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor](https://mlsanity.com/paper/2506.11107/) (2025)
- [Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests](https://mlsanity.com/paper/2506.20119/) (2025)
- [Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift](https://mlsanity.com/paper/2511.00704/) (2025)
- [Measuring the Impact of Student Gaming Behaviors on Learner Modeling](https://mlsanity.com/paper/2512.18659/) (2025)
- [Automated Domain Question Mapping (DQM) with Educational Learning Materials](https://mlsanity.com/paper/2601.07062/) (2026)
- [ALIGNAgent: Adaptive Learner Intelligence for Gap Identification and Next-step guidance](https://mlsanity.com/paper/2601.15551/) (2026)
- [MAGE-KT: Multi-Agent Graph-Enhanced Knowledge Tracing with Subgraph Retrieval and Asymmetric Fusion](https://mlsanity.com/paper/2601.16886/) (2026)
- [Causal Discovery Should Embrace the Wisdom of the Crowd](https://mlsanity.com/paper/2603.02678/) (2026)
- [PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training](https://mlsanity.com/paper/2603.05361/) (2026)
- [Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning](https://mlsanity.com/paper/2604.22770/) (2026)
- [Advanced Mathematics Learning Behavior Prediction and Academic Early Warning Model Based on Multimodal Data Analysis](https://mlsanity.com/paper/2606.01224/) (2026)
- [Estimating Learners' Skill Acquisition Without Temporal Information](https://mlsanity.com/paper/2606.20611/) (2026)
- [MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment](https://mlsanity.com/paper/2606.29049/) (2026)
- [Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing](https://mlsanity.com/paper/2607.13103/) (2026)
- [UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics](https://mlsanity.com/paper/2608.03811/) (2026)
- [Evaluating and Improving Pedagogical Fit in LLM-Based AI Tutors with the Pedagogical Suitability Index](https://mlsanity.com/paper/2608.05411/) (2026)
- [A Dataset for Modeling Iterative Problem-Solving](https://mlsanity.com/paper/2609.00940/) (2026)

## Abstract (arXiv)

Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge Tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In recent years, a substantial number of studies have concentrated on this rapidly growing field, significantly contributing to its advancements. In this survey, we will conduct a thorough investigation of these progressions. Firstly, we present three types of fundamental KT models with distinct technical routes. Subsequently, we review extensive variants of the fundamental KT models that consider more stringent learning assumptions. Moreover, the development of KT cannot be separated from its applications, thereby we present typical KT applications in various scenarios. To facilitate the work of researchers and practitioners in this field, we have developed two open-source algorithm libraries: EduData that enables the download and preprocessing of KT-related datasets, and EduKTM that provides an extensible and unified implementation of existing mainstream KT models. Finally, we discuss potential directions for future research in this rapidly growing field. We hope that the current survey will assist both researchers and practitioners in fostering the development of KT, thereby benefiting a broader range of students.