Summary of Automated Generation and Tagging Of Knowledge Components From Multiple-choice Questions, by Steven Moore et al.
Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions
by Steven Moore, Robin Schmucker, Tom Mitchell, John Stamper
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper leverages GPT-4 to generate Knowledge Components (KCs) linked to multiple-choice questions (MCQs) in Chemistry and E-Learning. The goal is to streamline the process of generating and linking KCs to assessment items, which typically requires significant effort and domain-specific knowledge. To evaluate the effectiveness of the Large Language Model (LLM), human evaluators compared LLM-generated KCs with those created by humans for each subject area. The results show that the LLM accurately matched KCs for 56% of Chemistry and 35% of E-Learning MCQs, with a preference for LLM-generated KCs over human-assigned ones in approximately two-thirds of cases. Furthermore, the paper presents an ontology induction algorithm to cluster questions assessing similar KCs based on content, which successfully grouped questions without requiring explicit labels or contextual information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research uses artificial intelligence to help create better quizzes and tests for students. It teaches a computer program called GPT-4 to generate “knowledge components” that are connected to specific questions. This makes it easier to create quizzes that test what students have learned. The study found that the computer-generated knowledge components were almost as good as those created by humans, and in many cases, people preferred the computer-generated ones. The research also developed a way to group similar quiz questions together without needing extra information, which could make creating quizzes even easier. |
Keywords
» Artificial intelligence » Gpt » Large language model
