# Enhancing Knowledge Tracing via Adversarial Training

arXiv: https://arxiv.org/abs/2108.04430 · HTML: https://mlsanity.com/paper/2108.04430/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [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)
- [RKT : Relation-Aware Self-Attention for Knowledge Tracing](https://mlsanity.com/paper/2008.12736/) (2020)
- [Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization](https://mlsanity.com/paper/1806.02180/) (2018)
- [Adversarial Training Towards Robust Multimedia Recommender System](https://mlsanity.com/paper/1809.07062/) (2018)
- [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)
- [Robust Multilingual Part-of-Speech Tagging via Adversarial Training](https://mlsanity.com/paper/1711.04903/) (2017)
- [Engaging with Massive Online Courses](https://mlsanity.com/paper/1403.3100/) (2014)
- [Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification](https://mlsanity.com/paper/2007.15072/) (2020)
- [Improving Machine Reading Comprehension via Adversarial Training](https://mlsanity.com/paper/1911.03614/) (2019)

## Built on by (most cited first, 30 of 31)

- [pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models](https://mlsanity.com/paper/2206.11460/) (2022)
- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [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)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [Forgetting-aware Linear Bias for Attentive Knowledge Tracing](https://mlsanity.com/paper/2309.14796/) (2023)
- [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)
- [HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing](https://mlsanity.com/paper/2212.12139/) (2022)
- [Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model](https://mlsanity.com/paper/2405.16542/) (2024)
- [Adversarial Training: A Survey](https://mlsanity.com/paper/2410.15042/) (2024)
- [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)
- [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)
- [Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning](https://mlsanity.com/paper/2403.06725/) (2024)
- [DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing](https://mlsanity.com/paper/2502.10396/) (2025)
- [AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling](https://mlsanity.com/paper/2502.11817/) (2025)
- [MemoryKT: An Integrative Memory-and-Forgetting Method for Knowledge Tracing](https://mlsanity.com/paper/2508.08122/) (2025)
- [Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space](https://mlsanity.com/paper/2602.22879/) (2026)
- [DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments](https://mlsanity.com/paper/2206.12034/) (2022)
- [Personalized Knowledge Tracing through Student Representation Reconstruction and Class Imbalance Mitigation](https://mlsanity.com/paper/2409.06745/) (2024)
- [Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method](https://mlsanity.com/paper/2501.05605/) (2025)
- [RouterKT: Mixture-of-Experts for Knowledge Tracing](https://mlsanity.com/paper/2504.08989/) (2025)
- [Constructing a Question-Answering Simulator through the Distillation of LLMs](https://mlsanity.com/paper/2509.09226/) (2025)
- [CleverBirds: A Multiple-Choice Benchmark for Fine-grained Human Knowledge Tracing](https://mlsanity.com/paper/2511.08512/) (2025)
- [Tracing Mathematical Proficiency Through Problem-Solving Processes](https://mlsanity.com/paper/2512.00311/) (2025)
- [KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing](https://mlsanity.com/paper/2512.18709/) (2025)
- [Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing](https://mlsanity.com/paper/2605.05958/) (2026)
- [MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing](https://mlsanity.com/paper/2605.08697/) (2026)

## Abstract (arXiv)

We study the problem of knowledge tracing (KT) where the goal is to trace the students' knowledge mastery over time so as to make predictions on their future performance. Owing to the good representation capacity of deep neural networks (DNNs), recent advances on KT have increasingly concentrated on exploring DNNs to improve the performance of KT. However, we empirically reveal that the DNNs based KT models may run the risk of overfitting, especially on small datasets, leading to limited generalization. In this paper, by leveraging the current advances in adversarial training (AT), we propose an efficient AT based KT method (ATKT) to enhance KT model's generalization and thus push the limit of KT. Specifically, we first construct adversarial perturbations and add them on the original interaction embeddings as adversarial examples. The original and adversarial examples are further used to jointly train the KT model, forcing it is not only to be robust to the adversarial examples, but also to enhance the generalization over the original ones. To better implement AT, we then present an efficient attentive-LSTM model as KT backbone, where the key is a proposed knowledge hidden state attention module that adaptively aggregates information from previous knowledge hidden states while simultaneously highlighting the importance of current knowledge hidden state to make a more accurate prediction. Extensive experiments on four public benchmark datasets demonstrate that our ATKT achieves new state-of-the-art performance. Code is available at: colorblue urlhttps://github.com/xiaopengguo/ATKT.