Knowledge Tracing

Follow this topic Ask about this topic

Knowledge tracing estimates what a learner knows and predicts future responses from sequences of questions, answers, and other learning behaviors. Methods range from Bayesian Knowledge Tracing and item-response theory to recurrent networks, Transformers, knowledge graphs, and language-model approaches that use text or problem-solving steps. These estimates can personalize practice, identify skill gaps, and guide decisions about when a learner is ready to advance. A central challenge is balancing predictive accuracy with reliable, interpretable, and fair mastery estimates, especially when data are sparse or benchmark results hide differences across learners and items.

Latest papers

31 papers3 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

Prediction Is Not Calibration: Item Parameter Recovery and Representation Coupling in Knowledge Tracing
Oct 21, 2026Wenrui Yuan, Stéphanie M. van den Berg, Johannes SteinrückeKnowledge Tracing Parameter Estimation
University of Twente, Enschede, The Netherlands
Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?
Oct 8, 2026Unggi Lee, Haeun ParkKnowledge Tracing LLM Inference
Korea University Sejong Campus · Korea University
An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing
Oct 4, 2026Houru Jiang, Zixi Wang, Tengteng Cheng +6Knowledge Tracing Automated Evaluation
Jinan University, Guangzhou, China · Guangdong Institute of Smart Education, Jinan University, Guangzhou, China · Jilin University, Changchun, China
RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer
Sep 18, 2026Samuel Girard, Juan D. Pinto, Jill-Jênn Vie +1Knowledge Tracing
Inria-Saclay · University of Illinois Urbana-Champaign, Urbana, IL, USA · Telecom-Sud Paris
Stable and Faithful Explanations for Knowledge Tracing
Sep 15, 2026Praveena Padi, Arun Morampudi, Ujval Sai Gopal Irrinki +1Knowledge Tracing Explainability Evaluation
Georgia Institute of Technology, Atlanta, Georgia, USA · Independent Researcher, Atlanta, Georgia, USA · Amazon Web Services, Austin, Texas, USA +1
Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation
Sep 9, 2026Hiroshi Tamano, Daichi MochihashiLatent Dynamics Modeling Knowledge Tracing
the graduate university for advanced studies, sokendai · the institute of statistical mathematics
One Mastery Threshold Does Not Fit All Knowledge Tracing Models
Sep 8, 2026Xianghui Meng, Yujing Zhang, Jionghao LinIntelligent Tutoring Systems Knowledge Tracing
The University of Hong Kong, Hong Kong SAR, China
A Dataset for Modeling Iterative Problem-Solving
Sep 1, 2026Fagun Patel, Sang T. Truong, Duc Q. Nguyen +4Educational Data Mining Knowledge Tracing
Stanford University · National University of Singapore
Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing
Aug 25, 2026Haotian Zhang, Shucun Wang, Jinze Wu +6Knowledge Tracing
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China · iFLYTEK AI Research, Hefei, China · State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics
Aug 4, 2026Carson J. Cook, Ahmed J. Zerouali, Anthony Schmidt +3Latent Variable Models Interpretable ML
Amplify Education, Inc.
Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing
Jul 14, 2026Duantengchuan Li, Yingqian Bi, Jinsong Chen +2Knowledge Tracing
School of the Information Management, Wuhan University, Wuhan 430072, China · School of Computer Science and Artificial Intelligence, Hubei University of Technology, Wuhan 430068, China · Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430074, China
MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment
Jun 27, 2026Xinjin Li, Mengyue Wang, Yuzhen Lin +4Intelligent Tutoring Systems Hierarchical Representation Learning
Columbia University, New York, USA · University of California, Berkeley, Berkeley, USA · School of Information Systems and Management, Carnegie Mellon University, Pittsburgh, PA, USA +4
Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage
Jun 12, 2026Xiaoran Yan, Cheng Tang, Atsushi ShimadaPost-Hoc Calibration Empirical Bayes
Kyushu University, Fukuoka, Japan
Advanced Mathematics Learning Behavior Prediction and Academic Early Warning Model Based on Multimodal Data Analysis
May 31, 2026Liu Qiong, Li ZhengboEducational Assessment AI in Education
Moutai Institute,, Renhuai, 564507, Guizhou, China
BuddyBench: A Privacy-Constrained Multi-Task Benchmark for Pediatric Social-Communication Personalization
May 27, 2026Jeyeon Eo, Joo Young Kim, Ran Ju +2Sequential Recommendation Healthcare
1Independent Researcher, Seoul, Republic of Korea · 2Neudive Inc., Daegu, Republic of Korea · 3Korea University, Sejong, Republic of Korea
Estimating Learners' Skill Acquisition Without Temporal Information
May 22, 2026Ryosuke Nagai, Kyohei Atarashi, Koh Takeuchi +2Knowledge Tracing Adaptive Learning
SODA · Kyoto University, Kyoto, Japan · Inria, France
Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning
May 10, 2026Siyu Wu, Cong Xu, Wei ZhangRepresentation Learning Interpretable ML
1Shanghai Institute of AI Education, East China Normal University, Shanghai, 200241, China · 2Shool of Computer Sicence and Technology, East China Normal University, Shanghai, 200241, China
MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing
May 9, 2026Yuhao Jia, Duantengchuan Li, Jinsong Chen +4Knowledge Tracing
Faculty of Artificial Intelligence in Education, Central China Normal University · School of Information Management, Wuhan University · School of Big Data and Artificial Intelligence, Chizhou University
Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing
May 7, 2026Peilin Zhan, Wei Chen, Weilin Chen +2Knowledge Tracing Doubly Robust Estimation
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
May 1, 2026Shuyan Huang, Alexander Scarlatos, Jaewook Lee +1Intelligent Tutoring Systems Interpretable ML
University of Massachusetts Amherst
RAG-KT: Cross-platform Explainable Knowledge Tracing with Multi-view Fusion Retrieval Generation
Apr 13, 2026Zhiyi Duan, Hongyu Yuan, Rui LiuKnowledge Tracing AI in Education
Inner Mongolia University, Hohhot, China
Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling
Apr 9, 2026Danial Hooshyar, Gustav Šír, Yeongwook Yang +6Knowledge Tracing AI in Education
School of Digital Technologies Tallinn University, Estonia · Faculty of Information Technology University of Jyväskylä Finland · Faculty of Education and Psychology University of Jyväskylä Finland +7
Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing
Apr 9, 2026Jun Seo, Sangwon Ryu, Heejin Do +2Knowledge Tracing Intelligent Tutoring Systems
GSAI, POSTECH · ETH Zurich, ETH AI Center · CSE, POSTECH
ConceptKT: A Benchmark for Concept-Level Deficiency Prediction in Knowledge Tracing
Mar 25, 2026Yu-Chen Kang, Yu-Chien Tang, An-Zi YenKnowledge Tracing Intelligent Tutoring Systems
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan
Circuit Complexity of Hierarchical Knowledge Tracing and Implications for Log-Precision Transformers
Mar 25, 2026Naiming Liu, Richard Baraniuk, Shashank SonkarCircuit Complexity Knowledge Tracing
Rice University · University of Central Florida
PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training
Mar 5, 2026Zirong Chen, Hongchao Zhang, Meiyi MaPersonalized Learning Adaptive Learning
College of Connected Computing, Vanderbilt University, Nashville, Tennessee 37235, USA
Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs
Mar 3, 2026Prarthana Bhattacharyya, Joshua Mitton, Ralph Abboud +1Knowledge Tracing AI in Education
Eedi · Learning Engineering Virtual Institute
MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing
Mar 3, 2026Runze Li, Kedi Chen, Guwei Feng +3Knowledge Tracing Educational Technology
School of Computer Science and Technology, East China Normal University, China · Shanghai Innovation Institute, China · WeChat AI, Tencent, China
Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space
Feb 26, 2026Xingcheng Fu, Shengpeng Wang, Yisen Gao +4Knowledge Tracing Educational Data Mining
Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, Guilin, China · Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, China · Computer Science and Engineering , The Hong Kong University of Science and Technology, Hong Kong, China +1
MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning
Feb 24, 2026Indronil Bhattacharjee, Christabel WayllaceKnowledge Tracing Few-Shot Learning
New Mexico State University, Las Cruces, New Mexico, USA

Oct 21, 2026J/K move · Enter open · S save

Prediction Is Not Calibration: Item Parameter Recovery and Representation Coupling in Knowledge Tracing

Wenrui Yuan, Stéphanie M. van den Berg, Johannes Steinrücke

University of Twente, Enschede, The Netherlands

Knowledge Tracing Parameter Estimation Parameter Identifiability Item Response Theory

Open paper AskPDF arXiv

Knowledge tracing (KT) models can estimate item parameters through an item response decoder trained solely for next-response prediction, yet predictive accuracy does not guarantee parameter recovery. We study 2PL, GPCM, and NRM decoders paired with LSTM, Transformer, and DKVMN encoders. Response-model derivatives show why discrimination and option slope updates depend directly on the scale and spread of estimated ability. Affine logit identities show how item-dependent shifts and rescaling in estimated ability are absorbed into item parameters without changing response probabilities. In simulations, difficulty, mean threshold, and correct-option intercept recovery remain high under shuffled order across representation widths, whereas discrimination and keyed option slope contrast recovery require wider item representations and degrade further under fixed item order. Separating the sequence item representation (width

d_S

) from the item parameter key (width

d_M

), and pairing a wide key with a thin sequence representation (

\mathrm{SK}(64,8)

), reduces fixed-order ability distortions and improves recovery and cross-order consistency without sacrificing test accuracy or increasing model size relative to a shared width-64 baseline (

\mathrm{SH}(64)

). On TIMSS and EdNet, widening

d_M

consistently improves agreement with an offline IRT reference, while the trade-off from narrowing

d_S

depends on the encoder's reliance on sequence width for prediction. These findings show why item parameters, especially discrimination and option slopes, must be evaluated separately from response prediction.