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.
Authors
- Bita Akram (ORCID: https://orcid.org/0000-0001-5195-5841)
- Rose Niousha (ORCID: https://orcid.org/0000-0003-2587-6079)
- Vedansh Malhotra (ORCID: https://orcid.org/0009-0000-6197-5125)
- Narges Norouzi (ORCID: https://orcid.org/0000-0001-9861-7540)
- Abigail O’Neill (ORCID: https://orcid.org/0009-0005-1700-3130)
- Ethan Chen
Institutions
- North Carolina State University (US)
- University of California, Berkeley (US)
- Stanford University (US)
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