Currently, there is a growing focus on student cognitive modeling, and we have curated articles that cover three essential tasks within this field: cognitive diagnosis, knowledge tracing, and computerized adaptive testing. The purpose of this compilation is to facilitate a rapid comprehension of both historical and future trends in these specific areas.
The cognitive diagnostic model is an approach that assesses students' abilities based on factors such as their interaction records and additional information related to exercises. We categorized the papers based on the data and methodology utilized by the Cognitive Diagnosis (CD) model. You can find detailed information from respective subheadings.
Knowledge tracing(KT) involves modeling a student's knowledge over time, enabling us to accurately predict the student's mastery of specific concepts and anticipate their performance in future assessments. KT papers are categorized based on the implemented methods, and specific details can be obtained from the titles of each section below.
- [SIGIR 2023, MoocRader] MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
- [CIKM 2021, MoocCubeX] MOOCCubeX: A Large Knowledge-centered Repository for Adaptive Learning in MOOCs
- [ICCE 2021, SLP] SLP: A Multi-Dimensional and Consecutive Dataset from K-12 Education
- [NeurIPS 2023, PTADisc] PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
- [NeurIPS 2023, XES3G5M] XES3G5M: A Knowledge Tracing Benchmark Dataset with Auxiliary Information
- [EDM 2024, SingPAD] SingPAD: A Knowledge Tracing Dataset Based on Music Performance Assessment