The predictive modeling for learning student results based on sequential rules

Nowadays, learning activities at universities in Vietnam are mostly in the form of credit-based mode. That is, to graduate students have to complete the subjects specified in the curriculum including the compulsory and optional ones. Therefore, to achieve their best performance, students would need guidelines on study direction in the compulsory subjects and choose the optional courses appropriate to their interests and abilities. Based on these practical requirements, the paper proposes a tool to assist students in predicting their own academic performance in order to improve their academic ability and be more scientifically grounded. In addition, the tool also helps students choose the subjects for the next semester in a reasonable manner. This tool is based on a set of sequential rules derived from the learning result of the students. To evaluate the performance of the proposed model, this tool was tested from real students records in the Faculty of Information Technology in Ho Chi Minh City University of Industry.