# DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing

arXiv: https://arxiv.org/abs/2502.10396 · HTML: https://mlsanity.com/paper/2502.10396/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [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)
- [Knowledge Tracing with Sequential Key-Value Memory Networks](https://mlsanity.com/paper/1910.13197/) (2019)
- [GIKT: A Graph-based Interaction Model for Knowledge Tracing](https://mlsanity.com/paper/2009.05991/) (2020)
- [Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations](https://mlsanity.com/paper/2302.06885/) (2023)
- [Enhancing Knowledge Tracing via Adversarial Training](https://mlsanity.com/paper/2108.04430/) (2021)
- [qDKT: Question-centric Deep Knowledge Tracing](https://mlsanity.com/paper/2005.12442/) (2020)

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

- [WDMIR: Wavelet-Driven Multimodal Intent Recognition](https://mlsanity.com/paper/2506.10011/) (2025)
- [MemoryKT: An Integrative Memory-and-Forgetting Method for Knowledge Tracing](https://mlsanity.com/paper/2508.08122/) (2025)
- [Multi-view Attention Fusion of Heterogeneous Hypergraph with Dynamic Behavioral Profiling for Personalized Learning Resource Recommendation](https://mlsanity.com/paper/2603.13310/) (2026)

## Abstract (arXiv)

Knowledge Tracing (KT) predicts future performance by modeling students' historical interactions, and understanding students' affective states can enhance the effectiveness of KT, thereby improving the quality of education. Although traditional KT values students' cognition and learning behaviors, efficient evaluation of students' affective states and their application in KT still require further exploration due to the non-affect-oriented nature of the data and budget constraints. To address this issue, we propose a computation-driven approach, Dynamic Affect Simulation Knowledge Tracing (DASKT), to explore the impact of various student affective states (such as frustration, concentration, boredom, and confusion) on their knowledge states. In this model, we first extract affective factors from students' non-affect-oriented behavioral data, then use clustering and spatiotemporal sequence modeling to accurately simulate students' dynamic affect changes when dealing with different problems. Subsequently, colorbluewe incorporate affect with time-series analysis to improve the model's ability to infer knowledge states over time and space. Extensive experimental results on two public real-world educational datasets show that DASKT can achieve more reasonable knowledge states under the effect of students' affective states. Moreover, DASKT outperforms the most advanced KT methods in predicting student performance. Our research highlights a promising avenue for future KT studies, focusing on achieving high interpretability and accuracy.