Cumulative Knowledge-based Regression Models for Next-term Grade Prediction

Sara Morsy and George Karypis
SIAM Data Mining Conference (SDM), 2017
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Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on the students' performance. In this paper, we present a cumulative knowledge-based regression model with different course-knowledge spaces for the task of next-term grade prediction. This method utilizes historical student-course grades as well as the information available about the courses to capture the relationships between courses in terms of the knowledge components provided by them. Our experiments on a large dataset obtained from 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.
Best Application Paper Award.
Research topics: Collaborative filtering | Data mining | Learning analytics