教师如何使用大型语言模型和Bloom分类法创建教育测验
How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes
AI总结：
本文提出基于大型语言模型和Bloom分类法生成教育测验问题的方法，实验表明教师偏好使用自动生成的问题且测验质量不降反升，展现了QG在课堂大规模应用的潜力。
摘要：
问题生成（QG）是一项自然语言处理任务，在教育领域具有丰富的潜在益处和用例。为了实现这一潜力，QG系统的设计和验证必须考虑教学需求。然而，很少有研究在真实教师或学生输入的基础上评估或设计QG方法。本文应用了一种基于大型语言模型的QG方法，其中问题是利用源自Bloom分类法的学习目标生成的。自动生成的问题被用于多项旨在评估教师如何在实践中使用它们的实验。结果表明，教师更喜欢用自动生成的问题来编写测验，且此类测验与手写版本相比没有质量损失。此外，多项指标表明自动生成的问题甚至能提高所创建测验的质量，展现了在课堂环境中大规模使用QG的前景。
英文摘要：
Question generation (QG) is a natural language processing task with an abundance of potential benefits and use cases in the educational domain. In order for this potential to be realized, QG systems must be designed and validated with pedagogical needs in mind. However, little research has assessed or designed QG approaches with the input from real teachers or students. This paper applies a large language model-based QG approach where questions are generated with learning goals derived from Bloom's taxonomy. The automatically generated questions are used in multiple experiments designed to assess how teachers use them in practice. The results demonstrate that teachers prefer to write quizzes with automatically generated questions, and that such quizzes have no loss in quality compared to handwritten versions. Further, several metrics indicate that automatically generated questions can even improve the quality of the quizzes created, showing the promise for large scale use of QG in the classroom setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
