  With cross-disciplinary academic interests increasing and academic advising
resources over capacity, the importance of exploring data-assisted methods to
support student decision making has never been higher. We build on the findings
and methodologies of a quickly developing literature around prediction and
recommendation in higher education and develop a novel recurrent neural
network-based recommendation system for suggesting courses to help students
prepare for target courses of interest, personalized to their estimated prior
knowledge background and zone of proximal development. We validate the model
using tests of grade prediction and the ability to recover prerequisite
relationships articulated by the university. In the third validation, we run
the fully personalized recommendation for students the semester before taking a
historically difficult course and observe differential overlap with our
would-be suggestions. While not proof of causal effectiveness, these three
evaluation perspectives on the performance of the goal-based model build
confidence and bring us one step closer to deployment of this personalized
course preparation affordance in the wild.
