# qDKT: Question-centric Deep Knowledge Tracing

arXiv: https://arxiv.org/abs/2005.12442 · HTML: https://mlsanity.com/paper/2005.12442/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 24 of 24)

- [CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles](https://mlsanity.com/paper/2305.13272/) (2023)
- [Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations](https://mlsanity.com/paper/2302.06885/) (2023)
- [Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://mlsanity.com/paper/2302.07942/) (2023)
- [Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing](https://mlsanity.com/paper/2410.01727/) (2024)
- [Code Soliloquies for Accurate Calculations in Large Language Models](https://mlsanity.com/paper/2309.12161/) (2023)
- [Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology](https://mlsanity.com/paper/2411.03495/) (2024)
- [Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning](https://mlsanity.com/paper/2404.15156/) (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)
- [FoundationalASSIST: An Educational Dataset for Foundational Knowledge Tracing and Pedagogical Grounding of LLMs](https://mlsanity.com/paper/2602.00070/) (2026)
- [Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning](https://mlsanity.com/paper/2403.06725/) (2024)
- [DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing](https://mlsanity.com/paper/2502.10396/) (2025)
- [Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models](https://mlsanity.com/paper/2305.14507/) (2023)
- [Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing](https://mlsanity.com/paper/2604.08260/) (2026)
- [Consistency and Monotonicity Regularization for Neural Knowledge Tracing](https://mlsanity.com/paper/2105.00607/) (2021)
- [A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing](https://mlsanity.com/paper/2305.16165/) (2023)
- [AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling](https://mlsanity.com/paper/2502.11817/) (2025)
- [A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings](https://mlsanity.com/paper/2506.09393/) (2025)
- [Future-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education](https://mlsanity.com/paper/2509.23996/) (2025)
- [MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics](https://mlsanity.com/paper/2601.03217/) (2026)
- [Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Exercise Recommendation](https://mlsanity.com/paper/2507.00032/) (2025)
- [Circuit Complexity of Hierarchical Knowledge Tracing and Implications for Log-Precision Transformers](https://mlsanity.com/paper/2603.23823/) (2026)
- [Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators](https://mlsanity.com/paper/2605.12748/) (2026)
- [Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?](https://mlsanity.com/paper/2610.11135/) (2026)

## Abstract (arXiv)

Knowledge tracing (KT) models, e.g., the deep knowledge tracing (DKT) model, track an individual learner's acquisition of skills over time by examining the learner's performance on questions related to those skills. A practical limitation in most existing KT models is that all questions nested under a particular skill are treated as equivalent observations of a learner's ability, which is an inaccurate assumption in real-world educational scenarios. To overcome this limitation we introduce qDKT, a variant of DKT that models every learner's success probability on individual questions over time. First, qDKT incorporates graph Laplacian regularization to smooth predictions under each skill, which is particularly useful when the number of questions in the dataset is big. Second, qDKT uses an initialization scheme inspired by the fastText algorithm, which has found success in a variety of language modeling tasks. Our experiments on several real-world datasets show that qDKT achieves state-of-art performance on predicting learner outcomes. Because of this, qDKT can serve as a simple, yet tough-to-beat, baseline for new question-centric KT models.