Is college students' trajectory associated with academic performance?. (March 2022) - Lim, Hyoungjoon; Kim, Soohyun; Chung, Kyong-Mee; Lee, Kangjae; Kim, Taewhan; Heo, Joon
Abstract: Many higher-education institutions have endeavored to understand students' characteristics in order to improve the quality of education. To this end, demographic information and questionnaire surveys have been used, and more recently, digital information from learning management systems and other sources has emerged for students' profiling. This study adopted a novel approach using semantic trajectory data created from smart card logs of campus buildings and class attendance records to investigate the relationship between students' trajectory patterns and academic performance. More than 4000 freshmen were observed per semester at the Songdo International Campus, Yonsei University, in South Korea during four semesters in 2016 and 2017. Dynamic time warping was newly adopted to calculate the similarities among student trajectories, and the similarities of students' trajectories were grouped by hierarchical clustering. Average grade point averages (GPAs) of the groups were evaluated and compared by major and gender. The results showed that the average GPAs were statistically different from each other in general, which confirmed the hypothesis that a student's trajectory differentiates a student's GPA. Furthermore, GPA was positively associated with students' degree of activeness in movement — the more accesses to campus facilities, the better the GPA. Besides, the differences in the average GPAs of the male groups were clearer than was the case for females, and the trajectory of the second semester better characterized an individual student. The study shows that a semantic trajectory pattern generated from location logs is a new and influential factor that can be utilized to understand students' characteristics in higher education and to predict their academic performances. Highlights: The authors proposed students' semantic trajectory for student profiling. Large datasets were collected from over 4000 students for two school years. Dynamic time warping, hierarchical clustering, and ANOVA tests were conducted. Students' semantic trajectory was proved to be a new and influential factor associated with academic performance.