AI RESEARCH
Using LLMs for Knowledge Component-level Correctness Labeling in Open-ended Coding Problems
arXiv CS.CL
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ArXi:2602.17542v2 Announce Type: replace Fine-grained skill representations, commonly referred to as knowledge components (KCs), are fundamental to many approaches in student modeling and learning analytics. However, KC-level correctness labels are rarely available in real-world datasets, especially for open-ended programming tasks where solutions typically involve multiple KCs simultaneously. Simply propagating problem-level correctness to all associated KCs obscures partial mastery and often leads to poorly fitted learning curves.