You Are How You Behave – Spatiotemporal Representation Learning for College Student Academic Achievement

Scholarships are a reflection of academic achievement for college students. The traditional scholarship assignment is strictly based on final grades and cannot recognize students whose performance trend improves or declines during the semester. This paper develops the Trajectory Mining on Clustering for Scholarship Assignment and Academic Warning (TMS) approach to identify the factors that affect the academic achievement of college students and to provide decision support to help low-performing students attain better performance. Specifically, we first conduct feature engineering to generate a set of features to characterize the lifestyles patterns, learning patterns, and Internet usage patterns of students. We then apply the objective and subjective combined weighted k-means (Wosk-means) algorithm to perform clustering analysis to identify the characteristics of different student groups. Considering the difficulty in obtaining the real global positioning system (GPS) records of students, we apply manually generated spatiotemporal trajectories data to quantify the direction of trajectory deviation with the assistance of the PrefixSpan algorithm to identify low-performing students. The experimental results show that the silhouette coefficient and Calinski-Harabasz index of the Wosk-means algorithm are both approximately 1.5 times to that of the best baseline algorithm, and the sum of the squared error of the Wosk-means algorithm is only the half of the best baseline algorithm.