# Knowledge Tracing with Sequential Key-Value Memory Networks

arXiv: https://arxiv.org/abs/1910.13197 · HTML: https://mlsanity.com/paper/1910.13197/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 30 of 60)

- [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)
- [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)
- [A Survey of Knowledge Tracing: Models, Variants, and Applications](https://mlsanity.com/paper/2105.15106/) (2021)
- [Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://mlsanity.com/paper/2302.07942/) (2023)
- [Bringing Generative AI to Adaptive Learning in Education](https://mlsanity.com/paper/2402.14601/) (2024)
- [Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing](https://mlsanity.com/paper/2201.09020/) (2022)
- [Towards Modeling Learner Performance with Large Language Models](https://mlsanity.com/paper/2403.14661/) (2024)
- [Adaptive and Personalized Exercise Generation for Online Language Learning](https://mlsanity.com/paper/2306.02457/) (2023)
- [Towards Robust Knowledge Tracing Models via k-Sparse Attention](https://mlsanity.com/paper/2407.17097/) (2024)
- [Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing](https://mlsanity.com/paper/2108.08105/) (2021)
- [Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs](https://mlsanity.com/paper/2207.00551/) (2022)
- [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)
- [Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations](https://mlsanity.com/paper/2112.11209/) (2021)
- [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)
- [MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://mlsanity.com/paper/2304.02205/) (2023)
- [Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing](https://mlsanity.com/paper/2410.01727/) (2024)
- [SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model](https://mlsanity.com/paper/2407.01245/) (2024)
- [DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing](https://mlsanity.com/paper/2211.12881/) (2022)
- [HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing](https://mlsanity.com/paper/2212.12139/) (2022)
- [Learning Data Teaching Strategies Via Knowledge Tracing](https://mlsanity.com/paper/2111.07083/) (2021)
- [Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability](https://mlsanity.com/paper/2112.15072/) (2021)
- [DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation](https://mlsanity.com/paper/2208.12651/) (2022)
- [Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review](https://mlsanity.com/paper/2301.10231/) (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)
- [Cold Start Problem: An Experimental Study of Knowledge Tracing Models with New Students](https://mlsanity.com/paper/2505.21517/) (2025)
- [Large Language Model Augmented Exercise Retrieval for Personalized Language Learning](https://mlsanity.com/paper/2402.16877/) (2024)
- [A Review of Data Mining in Personalized Education: Current Trends and Future Prospects](https://mlsanity.com/paper/2402.17236/) (2024)

## Abstract (arXiv)

Can machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in relation to different learning concepts through their interactions with learning activities. Recently, several attempts have been made to use deep learning models for tackling the KT problem. Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence. In this paper, we address these limitations by proposing a novel deep learning model for knowledge tracing, namely Sequential Key-Value Memory Networks (SKVMN). This model unifies the strengths of recurrent modelling capacity and memory capacity of the existing deep learning KT models for modelling student learning. We have extensively evaluated our proposed model on five benchmark datasets. The experimental results show that (1) SKVMN outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can better discover the correlation between latent concepts and questions, and (3) SKVMN can trace the knowledge state of students dynamics, and a leverage sequential dependencies in an exercise sequence for improved predication accuracy.