Predicting student performance in higher education using multi-regression models

Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction.
By implementing data mining model in IIS, this feature could precisely predict the student� grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, Learning Management System (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%.