Next-term student grade prediction

An enduring issue in higher education is student retention to successful graduation. To further this goal, we develop a system for the task of predicting students´ course grades for the next enrollment term in a traditional university setting. Each term, students enroll in a limited number of courses and earn grades in the range A-F for each course. Given historical grade data, our task is to predict the grades for each student in the courses they will enroll in during the next term. With this problem formulation, the next-term student grade prediction problem becomes quite similar to a rating prediction or next-basket recommendation problem. The factorization machine (FM), a general-purpose matrix factorization (MF) algorithm suitable for this task, is leveraged as the state-of-the-art method and compared to a variety of other methods. Our experiments show that FMs achieve the lowest prediction error. Results for both cold-start and non-cold-start prediction demonstrate that FMs can be used to accurately predict in both settings. Finally, we identify limitations observed in FMs and the other models tested and discuss directions for future work. To our knowledge, this is the first study that applies state-of-the-art collaborative filtering algorithms to solve the next-term student grade prediction problem.