Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions

This paper uses GPT-4 to automatically generate and tag Knowledge Components (KCs) for multiple-choice questions in higher-education Chemistry and E-Learning courses, comparing LLM-generated KCs against human-created ones and developing clustering algorithms to group similar questions. The work focuses on automating the KC tagging process to support adaptive learning systems and learning analytics.

Knowledge Components (KCs) linked to assessments enhance the measurement of student learning, enrich analytics, and facilitate adaptivity. However, generating and linking KCs to assessment items requires significant effort and domain-specific knowledge. To streamline this process for higher-education courses, we employed GPT-4 to generate KCs for multiple-choice questions (MCQs) in Chemistry and E-Learning. We analyzed discrepancies between the KCs generated by the Large Language Model (LLM) and

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