TY - GEN
T1 - Emerging Trends in Knowledge Tracing Models
T2 - 21st International Conference on Advanced Data Mining and Applications, ADMA 2025
AU - Lee, Liu Cheng
AU - Arakawa, Yutaka
AU - Mine, Tsunenori
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - This study presents a systematic review and empirical analysis of Knowledge Tracing (KT) models from 2022 to 2025. Based on architectural and design principles, recent KT models are categorized into six directions: dynamic memory architectures, graph-structured modeling, attention mechanisms, explainability-oriented approaches, multi-relational reasoning, and automated personalization via representation learning. Multiple representative models were re-implemented in a unified environment with behavioral feature stratification (item difficulty, learner ability, response speed, hint usage) for cross-context testing. Results show attention-based models maintain the highest stability under multi-feature interference, while multi-relational memory models excel in structured, high-pressure contexts. Future work includes expanding evaluation to more diverse datasets, developing architectures with dynamic feature detection and adaptation, and broadening behavioral analysis to additional real-world learning signals.
AB - This study presents a systematic review and empirical analysis of Knowledge Tracing (KT) models from 2022 to 2025. Based on architectural and design principles, recent KT models are categorized into six directions: dynamic memory architectures, graph-structured modeling, attention mechanisms, explainability-oriented approaches, multi-relational reasoning, and automated personalization via representation learning. Multiple representative models were re-implemented in a unified environment with behavioral feature stratification (item difficulty, learner ability, response speed, hint usage) for cross-context testing. Results show attention-based models maintain the highest stability under multi-feature interference, while multi-relational memory models excel in structured, high-pressure contexts. Future work includes expanding evaluation to more diverse datasets, developing architectures with dynamic feature detection and adaptation, and broadening behavioral analysis to additional real-world learning signals.
KW - Deep Learning
KW - Educational Data Mining
KW - Interpretability
KW - Knowledge Tracing
KW - Representation Learning
UR - https://www.scopus.com/pages/publications/105020669536
UR - https://www.scopus.com/pages/publications/105020669536#tab=citedBy
U2 - 10.1007/978-981-95-3459-3_17
DO - 10.1007/978-981-95-3459-3_17
M3 - Conference contribution
AN - SCOPUS:105020669536
SN - 9789819534586
T3 - Lecture Notes in Computer Science
SP - 217
EP - 224
BT - Advanced Data Mining and Applications - 21st International Conference, ADMA 2025, Proceedings
A2 - Yoshikawa, Masatoshi
A2 - Meng, Xiaofeng
A2 - Cao, Yang
A2 - Xiao, Chuan
A2 - Chen, Weitong
A2 - Wang, Yanda
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 October 2025 through 24 October 2025
ER -