Summary of An Automatic Question Usability Evaluation Toolkit, by Steven Moore et al.
An Automatic Question Usability Evaluation Toolkit
by Steven Moore, Eamon Costello, Huy A. Nguyen, 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 Scalable Automatic Question Usability Evaluation Toolkit (SAQUET) is an open-source tool that leverages large language models, word embeddings, and Transformers to evaluate the usability of multiple-choice questions (MCQs). SAQUET uses the Item-Writing Flaws (IWF) rubric for comprehensive quality evaluation. The tool effectively pinpoints and assesses various flaws in MCQs, providing analysis beyond traditional metrics. In a diverse dataset across five domains, SAQUET achieved an accuracy rate of over 94% in detecting flaws identified by human evaluators. This highlights the limitations of existing methods and showcases potential for improving educational assessment quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAQUET is a tool that helps evaluate multiple-choice questions. Usually, people either do this job by hand or use automated methods that focus on how easy the questions are to read. But these methods often miss important problems with the questions themselves. SAQUET uses computers to find and fix these problems. It looks at many things, like how complex the words in the question are, and how well the question is written. This helps us understand which questions are good or bad. The tool was tested on a big group of questions from different subjects, and it did a great job of finding the problems that people would identify. |
