  Course enrollment recommendation is a relevant task that helps university
students decide what is the best combination of courses to enroll in the next
term. In particular, recommender system techniques like matrix factorization
and collaborative filtering have been developed to try to solve this problem.
As these techniques fail to represent the time-dependent nature of academic
performance datasets we propose a deep learning approach using recurrent neural
networks that aims to better represent how chronological order of course grades
affects the probability of success. We have shown that it is possible to obtain
a performance of 81.10% on AUC metric using only grade information and that it
is possible to develop a recommender system with academic student performance
prediction. This is shown to be meaningful across different student GPA levels
and course difficulties
