Knowledge Tracing in Programming Education Integrating Students' Questions
Doyoun Kim, Suin Kim, Yojan Jo

TL;DR
This paper introduces SQKT, a novel knowledge tracing model that incorporates students' questions and automatically extracted skill information to improve performance prediction in programming education, demonstrating significant accuracy improvements.
Contribution
The paper presents SQKT, a new model that leverages students' questions and semantic embeddings to enhance knowledge tracing in programming courses, addressing data scarcity and generalization.
Findings
33.1% absolute improvement in AUC over baselines
Effective in cross-domain and data-scarce settings
Enhances adaptive learning system design
Abstract
Knowledge tracing (KT) in programming education presents unique challenges due to the complexity of coding tasks and the diverse methods students use to solve problems. Although students' questions often contain valuable signals about their understanding and misconceptions, traditional KT models often neglect to incorporate these questions as inputs to address these challenges. This paper introduces SQKT (Students' Question-based Knowledge Tracing), a knowledge tracing model that leverages students' questions and automatically extracted skill information to enhance the accuracy of predicting students' performance on subsequent problems in programming education. Our method creates semantically rich embeddings that capture not only the surface-level content of the questions but also the student's mastery level and conceptual understanding. Experimental results demonstrate SQKT's superior…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Educational Technology and Assessment
