# RKT : Relation-Aware Self-Attention for Knowledge Tracing

arXiv: https://arxiv.org/abs/2008.12736 · HTML: https://mlsanity.com/paper/2008.12736/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 30 of 62)

- [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)
- [Enhancing Knowledge Tracing via Adversarial Training](https://mlsanity.com/paper/2108.04430/) (2021)
- [Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://mlsanity.com/paper/2302.07942/) (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)
- [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)
- [Forgetting-aware Linear Bias for Attentive Knowledge Tracing](https://mlsanity.com/paper/2309.14796/) (2023)
- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [Option Tracing: Beyond Correctness Analysis in Knowledge Tracing](https://mlsanity.com/paper/2104.09043/) (2021)
- [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)
- [DyGKT: Dynamic Graph Learning for Knowledge Tracing](https://mlsanity.com/paper/2407.20824/) (2024)
- [HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing](https://mlsanity.com/paper/2212.12139/) (2022)
- [Explainable Few-shot Knowledge Tracing](https://mlsanity.com/paper/2405.14391/) (2024)
- [A Survey of Explainable Knowledge Tracing](https://mlsanity.com/paper/2403.07279/) (2024)
- [Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability](https://mlsanity.com/paper/2112.15072/) (2021)
- [Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing](https://mlsanity.com/paper/2304.03945/) (2023)
- [DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation](https://mlsanity.com/paper/2208.12651/) (2022)
- [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)
- [Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating](https://mlsanity.com/paper/2505.19543/) (2025)
- [Cold Start Problem: An Experimental Study of Knowledge Tracing Models with New Students](https://mlsanity.com/paper/2505.21517/) (2025)
- [LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences](https://mlsanity.com/paper/2105.06266/) (2021)
- [A Review of Data Mining in Personalized Education: Current Trends and Future Prospects](https://mlsanity.com/paper/2402.17236/) (2024)
- [DKT2: Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale Data](https://mlsanity.com/paper/2501.14256/) (2025)
- [Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing](https://mlsanity.com/paper/2507.11060/) (2025)
- [Next Token Knowledge Tracing: Exploiting Pretrained LLM Representations to Decode Student Behaviour](https://mlsanity.com/paper/2511.02599/) (2025)
- [Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems](https://mlsanity.com/paper/2112.04368/) (2021)

## Abstract (arXiv)

The world has transitioned into a new phase of online learning in response to the recent Covid19 pandemic. Now more than ever, it has become paramount to push the limits of online learning in every manner to keep flourishing the education system. One crucial component of online learning is Knowledge Tracing (KT). The aim of KT is to model student's knowledge level based on their answers to a sequence of exercises referred as interactions. Students acquire their skills while solving exercises and each such interaction has a distinct impact on student ability to solve a future exercise. This textitimpact is characterized by 1) the relation between exercises involved in the interactions and 2) student forget behavior. Traditional studies on knowledge tracing do not explicitly model both the components jointly to estimate the impact of these interactions. In this paper, we propose a novel Relation-aware self-attention model for Knowledge Tracing (RKT). We introduce a relation-aware self-attention layer that incorporates the contextual information. This contextual information integrates both the exercise relation information through their textual content as well as student performance data and the forget behavior information through modeling an exponentially decaying kernel function. Extensive experiments on three real-world datasets, among which two new collections are released to the public, show that our model outperforms state-of-the-art knowledge tracing methods. Furthermore, the interpretable attention weights help visualize the relation between interactions and temporal patterns in the human learning process.