# simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing

arXiv: https://arxiv.org/abs/2302.06881 · HTML: https://mlsanity.com/paper/2302.06881/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing](https://mlsanity.com/paper/2002.07033/) (2020)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (2020)
- [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)
- [Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization](https://mlsanity.com/paper/1806.02180/) (2018)
- [pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models](https://mlsanity.com/paper/2206.11460/) (2022)
- [Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory](https://mlsanity.com/paper/1904.11738/) (2019)
- [GIKT: A Graph-based Interaction Model for Knowledge Tracing](https://mlsanity.com/paper/2009.05991/) (2020)
- [Improving Knowledge Tracing via Pre-training Question Embeddings](https://mlsanity.com/paper/2012.05031/) (2020)
- [Enhancing Knowledge Tracing via Adversarial Training](https://mlsanity.com/paper/2108.04430/) (2021)
- [DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills](https://mlsanity.com/paper/1905.06873/) (2019)
- [Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing](https://mlsanity.com/paper/1809.08713/) (2018)
- [Knowledge Query Network: How Knowledge Interacts with Skills](https://mlsanity.com/paper/1908.02146/) (2019)
- [HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing](https://mlsanity.com/paper/2006.16915/) (2020)
- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [Deep Knowledge Tracing with Side Information](https://mlsanity.com/paper/1909.00372/) (2019)
- [Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability](https://mlsanity.com/paper/2112.15072/) (2021)

## Built on by (most cited first, 30 of 47)

- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [Towards Robust Knowledge Tracing Models via k-Sparse Attention](https://mlsanity.com/paper/2407.17097/) (2024)
- [Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents](https://mlsanity.com/paper/2505.19997/) (2025)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [Predictive, scalable and interpretable knowledge tracing on structured domains](https://mlsanity.com/paper/2403.13179/) (2024)
- [Disentangled Knowledge Tracing for Alleviating Cognitive Bias](https://mlsanity.com/paper/2503.02539/) (2025)
- [Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing](https://mlsanity.com/paper/2410.01727/) (2024)
- [SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model](https://mlsanity.com/paper/2407.01245/) (2024)
- [Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer](https://mlsanity.com/paper/2505.13489/) (2025)
- [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)
- [RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes](https://mlsanity.com/paper/2406.12465/) (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)
- [NR4DER: Neural Re-ranking for Diversified Exercise Recommendation](https://mlsanity.com/paper/2506.06341/) (2025)
- [Fusion or Confusion? Multimodal Complexity Is Not All You Need](https://mlsanity.com/paper/2512.22991/) (2025)
- [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)
- [Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning](https://mlsanity.com/paper/2403.06725/) (2024)
- [Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention](https://mlsanity.com/paper/2407.02547/) (2024)
- [A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments](https://mlsanity.com/paper/2410.15054/) (2024)
- [Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing](https://mlsanity.com/paper/2604.08260/) (2026)
- [Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues](https://mlsanity.com/paper/2605.01097/) (2026)
- [Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis](https://mlsanity.com/paper/2501.13943/) (2025)
- [LLM-driven Effective Knowledge Tracing by Integrating Dual-channel Difficulty](https://mlsanity.com/paper/2502.19915/) (2025)
- [A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings](https://mlsanity.com/paper/2506.09393/) (2025)
- [Language Bottleneck Models for Qualitative Knowledge State Modeling](https://mlsanity.com/paper/2506.16982/) (2025)
- [MemoryKT: An Integrative Memory-and-Forgetting Method for Knowledge Tracing](https://mlsanity.com/paper/2508.08122/) (2025)
- [LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation](https://mlsanity.com/paper/2602.17036/) (2026)
- [KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data](https://mlsanity.com/paper/2606.10358/) (2026)
- [Personalized Knowledge Tracing through Student Representation Reconstruction and Class Imbalance Mitigation](https://mlsanity.com/paper/2409.06745/) (2024)
- [AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing](https://mlsanity.com/paper/2504.04706/) (2025)

## Abstract (arXiv)

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augmentation and etc., which make the overall algorithm and system become more and more complex. Furthermore, due to the lack of standardized evaluation protocol citepliu2022pykt, there is no widely agreed KT baselines and published experimental comparisons become inconsistent and self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009 range from 0.721 to 0.821 citepminn2018deep,yeung2018addressing. Therefore, in this paper, we provide a strong but simple baseline method to deal with the KT task named textscsimpleKT. Inspired by the Rasch model in psychometrics, we explicitly model question-specific variations to capture the individual differences among questions covering the same set of knowledge components that are a generalization of terms of concepts or skills needed for learners to accomplish steps in a task or a problem. Furthermore, instead of using sophisticated representations to capture student forgetting behaviors, we use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions. Extensive experiments show that such a simple baseline is able to always rank top 3 in terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT baseline methods on 7 public datasets of different domains. We believe this work serves as a strong baseline for future KT research. Code is available at urlhttps://github.com/pykt-team/pykt-toolkitfootnoteWe merged our model to the textscpyKT benchmark at urlhttps://pykt.org/..