Abstract
Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a labor-intensive process that requires educators to formulate precise stems and plausible distractors. Recent advances in large language models (LLMs) have sparked interest in automating MCQ creation, but challenges persist in ensuring mathematical accuracy and addressing student errors. This paper introduces a prototype tool designed to facilitate collaboration between LLMs and educators for streamlining the math MCQ generation process. We conduct a pilot study involving math educators to investigate how the tool can help them simplify the process of crafting high-quality math MCQs. We found that while LLMs can generate well-formulated question stems, their ability to generate distractors that capture common student errors and misconceptions is limited. Nevertheless, a human-AI collaboration has the potential to enhance the efficiency and effectiveness of MCQ generation.
Abstract (translated)
多选题问题(MCQs)是一种评价学生知识的有效方法，由于其在管理和评分方面的效率而受到欢迎。打造高质量的数学MCQ是一个需要教育者精确制定题目和吸引人的干扰物的过程。近年来，大型语言模型（LLMs）的进步引发了对自动生成MCQ的兴趣，但确保数学准确性和解决学生错误仍然具有挑战性。本文介绍了一个原型工具，旨在促进LLMs和教育者之间的合作，简化数学MCQ的生成过程。我们进行了一项涉及数学教育工作者的试点研究，以调查该工具如何帮助教育者简化制作高质量数学MCQ的过程。我们发现，虽然LLMs可以生成良好的问题陈述，但它们生成干扰物以捕捉常见的学生错误和误解的能力有限。然而，人机合作有可能增强MCQ生成过程的效率和效果。
URL
https://arxiv.org/abs/2405.00864
