# HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing

arXiv: https://arxiv.org/abs/2006.16915 · HTML: https://mlsanity.com/paper/2006.16915/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://mlsanity.com/paper/2302.07942/) (2023)
- [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)
- [Programming Knowledge Tracing: A Comprehensive Dataset and A New Model](https://mlsanity.com/paper/2112.08273/) (2021)
- [Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy](https://mlsanity.com/paper/2109.01753/) (2021)
- [DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing](https://mlsanity.com/paper/2210.15470/) (2022)
- [LLM-driven Effective Knowledge Tracing by Integrating Dual-channel Difficulty](https://mlsanity.com/paper/2502.19915/) (2025)
- [APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing](https://mlsanity.com/paper/2210.08971/) (2022)
- [Personalized Knowledge Tracing through Student Representation Reconstruction and Class Imbalance Mitigation](https://mlsanity.com/paper/2409.06745/) (2024)
- [Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing](https://mlsanity.com/paper/2506.02949/) (2025)
- [Extracting Causal Relations in Deep Knowledge Tracing](https://mlsanity.com/paper/2511.03948/) (2025)
- [Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing](https://mlsanity.com/paper/2608.24005/) (2026)

## Abstract (arXiv)

Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. In recent years, many deep learning models have been applied to tackle the KT task, which have shown promising results. However, limitations still exist. Most existing methods simplify the exercising records as knowledge sequences, which fail to explore rich information that existed in exercises. Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect prior relations between exercises. To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent hierarchical relations between exercises. Specifically, we introduce the concept of problem schema to construct a hierarchical exercise graph that could model the exercise learning dependencies. Moreover, we employ two attention mechanisms to highlight the important historical states of learners. In the testing stage, we present a K&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed models.