  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.
