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Detecting abnormal behaviors of students in time and providing personalizedintervention and guidance at the early stage is important in educationalmanagement. Academic performance prediction is an important building block toenabling this pre-intervention and guidance. Most of the previous studies arebased on questionnaire surveys and self-reports, which suffer from small samplesize and social desirability bias. In this paper, we collect longitudinalbehavioral data from 6,597 students' smart cards and propose three major typesof discriminative behavioral factors, diligence, orderliness, and sleeppatterns. Empirical analysis demonstrates these behavioral factors are stronglycorrelated with academic performance. Furthermore, motivated by socialinfluence theory, we analyze the correlation between each student's academicperformance with his/her behaviorally similar students'. Statistical testsindicate this correlation is significant. Based on these factors, we furtherbuild a multi-task predictive framework based on a learning-to-rank algorithmfor academic performance prediction. This framework captures inter-semestercorrelation, inter-major correlation and integrates student similarity topredict students' academic performance. The experiments on a large-scalereal-world dataset show the effectiveness of our methods for predictingacademic performance and the effectiveness of proposed behavioral factors.

Predicting Academic Performance for College Students: A Campus Behavior Perspective