Xiaoqi Che 1,* , Yang Xiao 2 , Long Su 1 , Lei Bao 3
1
Beihang University, Beijing, Beijing 100191, China
2
South China Normal University, Guangzhou, Guangdong 510631, China
3
Department of Physics, The Ohio State University, Columbus, Ohio 43220, United States
*Authors to whom correspondence should be addressed.
Received: 2020-1-15 / Accepted: 2020-4-21 / Published: 2020-5-28
DOI: https://doi.org/10.37906/real.2020.4
Abstract Abstract: A course selection (CS) model is constructed based on the course enrollment database from a Chinese university in this research. By using big-data analysis on the database, we study the factors that may determine the students’ CS and we investigate how to optimize allocation of course resources. Our results reveal that the allocation of course resources (type of courses) and the recent revision of academic programs will significantly influence students’ CS. Courses on natural science and engineering (type-b courses), as well as on literature and art (type-d courses), are becoming most popular in course enrollment, which are followed by practical employment-related courses (type-c courses) such as law and management. The core courses of the major (type-a courses) are least selected. Since the new academic program was implemented, type-b courses have been becoming more popular but type-c and type-d courses are less enrolled than before. Our findings can be referenced by policy makers to improve the allocation of course resources in universities, which will benefit the students’ CS and satisfy the demands from society. Our results will also serve as important information for improving education efficiency.
Research Areas: Educational policy
Keywords: allocation of Course Resources, educational efficiency, course selection, big data analysis, credit system
