Use of Large Language Models for Extracting Knowledge Components in CS1 Programming Exercises
With the recent surge in enrollment in computer sciences, providing automated individualized feedback in CS1 courses can potentially eliminate the achievement gap in Computing. This study utilizes large language models to extract foundational programming concepts targeted by various programming assignments in a CS1 course. We seek to answer the following research questions: RQ1. How effectively can large language models identify knowledge components in a CS1 course from programming assignments? RQ2. Can large language models be used to extract program-level knowledge components, and how can the information be used to identify students’ misconceptions? Preliminary results demonstrated a high similarity between course-level knowledge components retrieved from a large language model and that of an expert-generated list.