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How do machine-generated questions compare to human-generated questions?

机译:机器生成的问题与人工生成的问题相比如何?

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Abstract Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply biology instructors with questions for college students in introductory biology classes, two algorithms were developed. One generates questions from a formal representation of photosynthesis knowledge. The other collects biology questions from the web. The questions generated by these two methods were compared to questions from biology textbooks. Human students rated questions for their relevance, fluency, ambiguity, pedagogy, and depth. Questions were also rated by the authors according to the topic of the questions. Although the exact pattern of results depends on analytic assumptions, it appears that there is little difference in the pedagogical benefits of each class, but the questions generated from the knowledge base may be shallower than questions written by professionals. This suggests that all three types of questions may work equally well for helping students to learn.
机译:摘要理科教师需要在考试,家庭作业,课堂讨论,复习和其他教学活动中使用的问题。教科书永远不会有足够的问题,因此讲师必须从其他来源找到它们或提出自己的问题。为了向生物学指导者提供生物学入门课程中的大学生问题,开发了两种算法。人们从光合作用知识的形式表示中产生问题。另一个从网上收集生物学问题。将这两种方法产生的问题与生物学教科书中的问题进行了比较。人类学生根据他们的相关性,流利性,歧义性,教学法和深度对问题进行评分。作者还根据问题的主题对问题进行了评分。尽管结果的确切模式取决于分析假设,但似乎每个班级的教学效益差异不大,但是从知识库中产生的问题可能比专业人员撰写的问题浅。这表明,这三种类型的问题在帮助学生学习方面都可以很好地发挥作用。

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