LLM-KCI: Leveraging Large Language Models to Identify Programming Knowledge Components

Identifying Knowledge Components (KCs) in computer science education improves curriculum design and teaching strategies. We introduce a framework using Large Language Models to identify KCs from programming assignments automatically. Our framework helps educators align assignments with course objectives. GPT-4 identifies relevant KCs well, though there's a low match with expert-generated KCs at the course level. At the problem level, performance is lower, but key KCs are reasonably identified.

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Publication Details

Published
2025-02-18
DOI
https://doi.org/10.1145/3641555.3705215
Citations
3
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
9.74
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article

LLM-KCI: Leveraging Large Language Models to Identify Programming Knowledge Components

Bita Akram, Rose Niousha, Vedansh Malhotra, Narges Norouzi et al.
3 citations
Software Engineering Research
9.74
article

LLM-KCI: Leveraging Large Language Models to Identify Programming Knowledge Components

Bita Akram, Rose Niousha, Vedansh Malhotra, Narges Norouzi, Abigail O’Neill, Ethan Chen
article en
3 citations

Abstract

Identifying Knowledge Components (KCs) in computer science education improves curriculum design and teaching strategies. We introduce a framework using Large Language Models to identify KCs from programming assignments automatically. Our framework helps educators align assignments with course objectives. GPT-4 identifies relevant KCs well, though there's a low match with expert-generated KCs at the course level. At the problem level, performance is lower, but key KCs are reasonably identified.

North Carolina State University (US), University of California, Berkeley (US), Stanford University (US)
Quality Education
Openalex Percentile: Top 3%
Software Engineering Research
9.74
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