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.

翻译：由于日益依赖电子教科书和大规模开放在线课程(MOOC),学生学习数据的数量有所增加。通过仔细分析这些数据,教育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,对OpenDSA eTextbook学期使用的日志数据进行了分析,以确定最困难的数据结构课程练习和评估课程练习的质量。我们的研究以分析学生对课程练习的反应为基础。我们应用了项目反应理论(IRT)分析和潜在特质模式(LTM)来确定最困难的练习。我们运用了项目反应理论(IRT)来评估我们应用光学理论进行的课程练习的质量。我们的研究结果表明,与算法分析专题有关的练习代表了最困难的练习,而现有的六项练习被归类为可改进或需要关注的贫弱练习。