Knowledge Tracing

Follow this topic Ask about this topic

Momentum

1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 14

All topics

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
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
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
Tracing Mathematical Proficiency Through Problem-Solving Processes
Nov 29, 2025Jungyang Park, Suho Kang, Jaewoo Park +4Intelligent Tutoring Systems Knowledge Tracing
1Yonsei University · 2Mathpresso · 3Seoul National University

Sep 9, 2026, stat.MEJ/K move · Enter open · S save

Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation

Hiroshi Tamano, Daichi Mochihashi

the graduate university for advanced studies, sokendai · the institute of statistical mathematics

Latent Dynamics Modeling Knowledge Tracing Item Response Theory

Open paper AskPDF arXiv

Multidimensional item response theory (MIRT) is a statistical test theory that precisely estimates multiple latent skills of learners from the responses in a test. Both compensatory and non-compensatory models have been proposed for MIRT: the former assumes that each skill can complement other skills, whereas the latter assumes they cannot. This non-compensatory assumption is convincing in many tests that measure multiple skills; therefore, applying non-compensatory models to such data is crucial for achieving unbiased and accurate estimation. In contrast to tests, latent skills will change over time in daily learning. To monitor the growth of skills, dynamical extensions of MIRT models have been investigated. However, most of them assumed compensatory models, and a model that can reproduce continuous latent states of skills under the non-compensatory assumption has not been proposed thus far. To enable accurate skill tracing under the non-compensatory assumption, we propose a dynamical extension of non-compensatory MIRT models by combining a linear dynamical system and a non-compensatory model. This results in a complicated posterior of skills, which we approximate with a Gaussian distribution by minimizing the Kullback-Leibler divergence between the approximated posterior and the true posterior. The learning algorithm for the model parameters is derived through Monte Carlo expectation maximization. Simulation studies verify that the proposed method is able to reproduce latent skills accurately, whereas the dynamical compensatory model suffers from significant underestimation errors. Furthermore, experiments on an actual data set demonstrate that our dynamical non-compensatory model can infer practical skill tracing and clarify differences in skill tracing between non-compensatory and compensatory models.

Momentum

Related topics

Latest papers 14