Cumulative Knowledge-based Regression Models for Next-term Grade Prediction
Grade prediction for courses not yet taken by students is important so as to guide them while registering for next-term courses. Moreover, it can help their advisers for designing personalized degree plans and modifying them based on the students' performance. In this paper, we present cumulative knowledge-based regression models with different course-knowledge spaces for the task of next-term grade prediction. These models utilize historical student-course grade data as well as the information available about the courses that capture the relationships between courses in terms of the knowledge components provided by them. Our experiments on a large dataset obtained from the College of Science and Engineering at University of Minnesota show that our proposed methods achieve better performance than competing methods and that these performance gains are statistically significant.