A Study of the Reading Behavior of University Students

Reading is part of our daily life. For university students, reading is extremely important for improving their professional knowledge and cultivating their personal hobbies. This work studied the relationship between the university students' academic ranking and their reading behavior as well as reading quantities. By analyzing the data of library loan records and the examination performances of all the students who majored in software engineering in Chang'an University within their four-year-long undergraduate study, we set the reading quantities of professional books and other (non-professional) books as two essential factors, and established a model based on multiple linear regression machine learning algorithm, which successfully predicted the students' academic ranking from their reading quantities. K-means was also used to perform simple clustering on different types of students.