A SURVEY ON NOVEL APPROACH FOR STUDENT ACADEMIC PERFORMANCE PREDICTION AND MONITORING USING MACHINE LEARNING TECHNIQUE

Digital data trails from disparate sources covering different aspects of student life are stored daily in most modern university campuses. However, it remains challenging to combine these data to obtain a holistic view of a student, use these data to accurately predict academic performance, and use such predictions to promote positive student engagement with the university. In our study, first, an experiment is conducted based on a real-world campus dataset of college students that aggregates multisource behavioural data covering not only online and offline learning but also behaviours inside and outside of the classroom. Specially, to gain in-depth inside into the features leading to excellent or poor performance, matrix measuring the linear and nonlinear behavioural changes (e.g. regularity and stability) of campus lifestyles are estimated; furthermore, features representing dynamic changes in temporal lifestyle patterns are extracted by the means of long short-term memory (LSTM). Second, machine learning based classification algorithms are developed to predict academic performance. Finally visualized feedback enabling students (especially at risk-students) to potentially optimize their interaction with the university and achieve a study-life balanced is designed.