  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.
However, prior courses taken by a student can have \black{different
contributions when estimating a student's knowledge state and towards each
target course, which} cannot be captured by linear models. Moreover, CKRM and
other grade prediction methods ignore the effect of concurrently-taken courses
on a student's performance in a target course. In this paper, we propose
context-aware non-linear and neural attentive models that can potentially
better estimate a student's knowledge state from his/her prior course
information, as well as model the interactions between a target course and
concurrent courses. Compared to the competing methods, our experiments on a
large real-world dataset consisting of more than $1.5$M grades show the
effectiveness of the proposed models in accurately predicting students' grades.
Moreover, the attention weights learned by the neural attentive model can be
helpful in better designing their degree plans.
