Sparse Neural Attentive Knowledge-based Models for Grade Prediction

Abstract: 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 their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. A student's knowledge state is built by linearly accumulating the learned provided knowledge components of the courses he/she has taken in the past, weighted by his/her grades in them. However, not all the prior courses contribute equally to the target course. In this paper, we propose a novel Neural Attentive Knowledge-based model (NAK) that learns the importance of each historical course in predicting the grade of a target course. Compared to CKRM and other competing approaches, our experiments on a large real-world dataset consisting of $\sim$1.5 grades show the effectiveness of the proposed NAK model in accurately predicting the students' grades. Moreover, the attention weights learned by the model can be helpful in better designing their degree plans.