A Holistic Recommendation System for Higher Education Academic Activities

Several recommender systems have been proposed to suggest courses to students based on their transcripts. In this paper, we evaluate whether these systems can be generalized to other academic activities, such as research projects and extracurricular activities. We follow previous grade-aware and content-based approaches, where course descriptions are represented by embedding vectors to predict students' grades. We extend this method by representing other academic activities as embedding vectors in the same space as the course descriptions using Jina, a recent BERT-based embedding model. This shared embedding space allows us to predict students' potential "grades" for other academic activities, different from courses, which we interpret as their level of interest. We first evaluate the Jina embeddings as features for predicting course grades compared to TF-IDF and Word2Vec. The results show that Jina embeddings outperform both for predicting course grades. Using the Jina embeddings as shared space for academic activities representation, we conducted a user study to evaluate our assumption that predicted grades are good proxies for student interests. We asked students to rate recommendations for courses, undergraduate research projects, and community activities with respect to novelty, diversity, personalization, accuracy, and satisfaction. The results indicate that students prefer our recommender system over a collaborative filtering baseline in all metrics.