Enriching Course-Specific Regression Models with Content Features for Grade Prediction

Qian Hu, Agoritsa Polyzou, George Karypis, and Huzefa Rangwala
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017
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An enduring issue in higher education is student retention and timely graduation. Early-warning and degree planning systems have been identified as a key approach to tackle this problem. Accurately predicting a student’s performance can help recommend degree pathways for students and identify students at-risk of dropping from their program of study. Various approaches have been developed for predicting students’ next-term grades. Recently, course-specific approaches based on linear regression and matrix factorization have been proposed. To predict a student’s grade, course-specific approaches utilize the student’s grades from courses taken prior to that course. However, there are a lot of factors other than student’s historical grades that influence his/her performance, such as the difficulty of the courses, the quality and pedagogy of the instructor, the academic level of the students when taking the courses and so on. In this paper, we propose a course-specific regression model enriched with features about students, courses and instructors. Our proposed models were evaluated on datasets from two large public universities for academic programs with varying flexibility. The experimental results showed that incorporating content features can boost the performance of the course-specific model. For some degree programs with high flexibility, our experiments showed that predicting the grades with informative content features demonstrated better prediction accuracy.
Research topics: Data mining | Learning analytics