  In order to help undergraduate students towards successfully completing their
degrees, developing tools that can assist students during the course selection
process is a significant task in the education domain. The optimal set of
courses for each student should include courses that help him/her graduate in a
timely fashion and for which he/she is well-prepared for so as to get a good
grade in. To this end, we propose two different grade-aware course
recommendation approaches to recommend to each student his/her optimal set of
courses. The first approach ranks the courses by using an objective function
that differentiates between courses that are expected to increase or decrease a
student's GPA. The second approach combines the grades predicted by grade
prediction methods with the rankings produced by course recommendation methods
to improve the final course rankings. To obtain the course rankings in the
first approach, we adapt two widely-used representation learning techniques to
learn the optimal temporal ordering between courses. Our experiments on a large
dataset obtained from the University of Minnesota that includes students from
23 different majors show that the grade-aware course recommendation methods can
do better on recommending more courses in which the students are expected to
perform well and recommending fewer courses in which they are expected not to
perform well in than grade-unaware course recommendation methods.
