# Goal-based Course Recommendation

## TL;DR

This paper introduces a novel recurrent neural network-based system for personalized course recommendations, aiming to assist students in preparing for target courses by considering their background and learning zone.

## Contribution

It develops a new goal-based recommendation model that personalizes course suggestions using student background and validates it through multiple predictive tests.

## Key findings

- Effective grade prediction accuracy
- Successful recovery of prerequisite relationships
- Differential overlap in recommendations before difficult courses

## Abstract

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.