# Knowledge Query Network: How Knowledge Interacts with Skills

arXiv: https://arxiv.org/abs/1908.02146 · HTML: https://mlsanity.com/paper/1908.02146/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2019.

## Built on by (most cited first, 23 of 23)

- [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)
- [Forgetting-aware Linear Bias for Attentive Knowledge Tracing](https://mlsanity.com/paper/2309.14796/) (2023)
- [Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing](https://mlsanity.com/paper/2410.01727/) (2024)
- [A Survey of Explainable Knowledge Tracing](https://mlsanity.com/paper/2403.07279/) (2024)
- [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)
- [Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing](https://mlsanity.com/paper/2507.11060/) (2025)
- [Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning](https://mlsanity.com/paper/2403.06725/) (2024)
- [Addressing Label Leakage in Knowledge Tracing Models](https://mlsanity.com/paper/2403.15304/) (2024)
- [Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing](https://mlsanity.com/paper/2410.12872/) (2024)
- [Uncertainty-preserving deep knowledge tracing with state-space models](https://mlsanity.com/paper/2407.17427/) (2024)
- [AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling](https://mlsanity.com/paper/2502.11817/) (2025)
- [MemoryKT: An Integrative Memory-and-Forgetting Method for Knowledge Tracing](https://mlsanity.com/paper/2508.08122/) (2025)
- [Personalized Knowledge Tracing through Student Representation Reconstruction and Class Imbalance Mitigation](https://mlsanity.com/paper/2409.06745/) (2024)
- [Deep Knowledge Tracing for Personalized Adaptive Learning at Historically Black Colleges and Universities](https://mlsanity.com/paper/2410.13876/) (2024)
- [Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing](https://mlsanity.com/paper/2509.21514/) (2025)
- [CleverBirds: A Multiple-Choice Benchmark for Fine-grained Human Knowledge Tracing](https://mlsanity.com/paper/2511.08512/) (2025)
- [KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing](https://mlsanity.com/paper/2512.18709/) (2025)
- [An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing](https://mlsanity.com/paper/2610.06988/) (2026)

## Abstract (arXiv)

Knowledge Tracing (KT) is to trace the knowledge of students as they solve a sequence of problems represented by their related skills. This involves abstract concepts of students' states of knowledge and the interactions between those states and skills. Therefore, a KT model is designed to predict whether students will give correct answers and to describe such abstract concepts. However, existing methods either give relatively low prediction accuracy or fail to explain those concepts intuitively. In this paper, we propose a new model called Knowledge Query Network (KQN) to solve these problems. KQN uses neural networks to encode student learning activities into knowledge state and skill vectors, and models the interactions between the two types of vectors with the dot product. Through this, we introduce a novel concept called textitprobabilistic skill similarity that relates the pairwise cosine and Euclidean distances between skill vectors to the odds ratios of the corresponding skills, which makes KQN interpretable and intuitive. On four public datasets, we have carried out experiments to show the following: 1. KQN outperforms all the existing KT models based on prediction accuracy. 2. The interaction between the knowledge state and skills can be visualized for interpretation. 3. Based on probabilistic skill similarity, a skill domain can be analyzed with clustering using the distances between the skill vectors of KQN. 4. For different values of the vector space dimensionality, KQN consistently exhibits high prediction accuracy and a strong positive correlation between the distance matrices of the skill vectors.