# Enhancing Knowledge Tracing with Concept Map and Response Disentanglement

arXiv: https://arxiv.org/abs/2408.12996 · HTML: https://mlsanity.com/paper/2408.12996/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [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)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (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)
- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization](https://mlsanity.com/paper/1806.02180/) (2018)
- [Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations](https://mlsanity.com/paper/2302.06885/) (2023)
- [Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions](https://mlsanity.com/paper/1501.04346/) (2015)
- [Disentangled Variational Auto-Encoder for Semi-supervised Learning](https://mlsanity.com/paper/1709.05047/) (2017)
- [No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option Tracing for Better Student Assessment](https://mlsanity.com/paper/2204.14006/) (2022)

## Built on by (most cited first, 3 of 3)

- [Language Bottleneck Models for Qualitative Knowledge State Modeling](https://mlsanity.com/paper/2506.16982/) (2025)
- [Enhancing knowledge tracing robustness for new question cold start in Intelligent Tutoring Systems](https://mlsanity.com/paper/2512.07179/) (2025)
- [Automated Domain Question Mapping (DQM) with Educational Learning Materials](https://mlsanity.com/paper/2601.07062/) (2026)

## Abstract (arXiv)

In the rapidly advancing realm of educational technology, it becomes critical to accurately trace and understand student knowledge states. Conventional Knowledge Tracing (KT) models have mainly focused on binary responses (i.e., correct and incorrect answers) to questions. Unfortunately, they largely overlook the essential information in students' actual answer choices, particularly for Multiple Choice Questions (MCQs), which could help reveal each learner's misconceptions or knowledge gaps. To tackle these challenges, we propose the Concept map-driven Response disentanglement method for enhancing Knowledge Tracing (CRKT) model. CRKT benefits KT by directly leveraging answer choices--beyond merely identifying correct or incorrect answers--to distinguish responses with different incorrect choices. We further introduce the novel use of unchosen responses by employing disentangled representations to get insights from options not selected by students. Additionally, CRKT tracks the student's knowledge state at the concept level and encodes the concept map, representing the relationships between them, to better predict unseen concepts. This approach is expected to provide actionable feedback, improving the learning experience. Our comprehensive experiments across multiple datasets demonstrate CRKT's effectiveness, achieving superior performance in prediction accuracy and interpretability over state-of-the-art models.