论文标题
使用项目响应理论和记录的数据分析在Etextbook中识别难度的练习
Identifying Difficult exercises in an eTextbook Using Item Response Theory and Logged Data Analysis
论文作者
论文摘要
对eTextbook和大规模开放在线课程（MOOC）的依赖日益增长的依赖性导致学生学习数据的数量增加。通过仔细分析这些数据，教育工作者可以确定困难的练习，并在教授特定主题时评估练习的质量。在这项研究中，提供了对Opendsa Etextbook学期使用的日志数据的分析，以确定最困难的数据结构课程练习并评估课程练习的质量。我们的研究基于分析学生对课程练习的反应。我们应用项目响应理论（IRT）分析和潜在特征模式（LTM）来确定最困难的练习。评估我们应用IRT理论的课程练习质量。我们的发现表明，与算法分析主题相关的练习代表了最困难的练习，并且现有的六项练习被归类为可以改进或需要注意的练习不佳。
The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students' learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students' responses to the course exercises. We applied item response theory (IRT) analysis and a latent trait mode (LTM) to identify the most difficult exercises .To evaluate the quality of the course exercises we applied IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.
