  Detecting abnormal behaviors of students in time and providing personalized
intervention and guidance at the early stage is important in educational
management. Academic performance prediction is an important building block to
enabling this pre-intervention and guidance. Most of the previous studies are
based on questionnaire surveys and self-reports, which suffer from small sample
size and social desirability bias. In this paper, we collect longitudinal
behavioral data from 6,597 students' smart cards and propose three major types
of discriminative behavioral factors, diligence, orderliness, and sleep
patterns. Empirical analysis demonstrates these behavioral factors are strongly
correlated with academic performance. Furthermore, motivated by social
influence theory, we analyze the correlation between each student's academic
performance with his/her behaviorally similar students'. Statistical tests
indicate this correlation is significant. Based on these factors, we further
build a multi-task predictive framework based on a learning-to-rank algorithm
for academic performance prediction. This framework captures inter-semester
correlation, inter-major correlation and integrates student similarity to
predict students' academic performance. The experiments on a large-scale
real-world dataset show the effectiveness of our methods for predicting
academic performance and the effectiveness of proposed behavioral factors.
