# Exercise Hierarchical Feature Enhanced Knowledge Tracing

arXiv: https://arxiv.org/abs/2011.09867 · HTML: https://mlsanity.com/paper/2011.09867/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [A quantitative study of NLP approaches to question difficulty estimation](https://mlsanity.com/paper/2305.10236/) (2023)
- [Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction](https://mlsanity.com/paper/2312.11890/) (2023)
- [Active Learning to Guide Labeling Efforts for Question Difficulty Estimation](https://mlsanity.com/paper/2409.09258/) (2024)
- [Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space](https://mlsanity.com/paper/2602.22879/) (2026)

## Abstract (arXiv)

Knowledge tracing is a fundamental task in the computer-aid educational system. In this paper, we propose a hierarchical exercise feature enhanced knowledge tracing framework, which could enhance the ability of knowledge tracing by incorporating knowledge distribution, semantic features, and difficulty features from exercise text. Extensive experiments show the high performance of our framework.