根据自动题目生成器的作业难度来预测其考试难度

Binglin Chen, Matthew West, C. Zilles
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引用次数: 2

摘要

为了设计好的评估,在考试前对新试题的难度进行估计是很有用的。在本文中,我们研究了几百个自动题项生成器(生成各种独特题项实例的简短计算机程序)的集合,并表明它们的考试难度可以从学生在考试前练习中在同一生成器上的表现大致预测出来。具体来说,我们展示了学生在考试中正确回答生成器的比率平均在上次练习中正确比率的5%以内。本研究采用本科计算机科学与机械工程导论课程的数据进行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the difficulty of automatic item generators on exams from their difficulty on homeworks
To design good assessments, it is useful to have an estimate of the difficulty of a novel exam question before running an exam. In this paper, we study a collection of a few hundred automatic item generators (short computer programs that generate a variety of unique item instances) and show that their exam difficulty can be roughly predicted from student performance on the same generator during pre-exam practice. Specifically, we show that the rate that students correctly respond to a generator on an exam is on average within 5% of the correct rate for those students on their last practice attempt. This study is conducted with data from introductory undergraduate Computer Science and Mechanical Engineering courses.
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