Sequence based course recommender for personalized curriculum planning

Abstract

Students in higher education need to select appropriate courses to meet graduation requirements for their degree. Selection approaches range from manual guides, on-line systems to personalized assistance from academic advisers. An automated course recommender is one approach to scale advice for large cohorts. However, existing recommenders need to be adapted to include sequence, concurrency, constraints and concept drift. In this paper, we propose the use of recent deep learning techniques such as Long Short-Term Memory (LSTM) Recurrent Neural Networks to resolve these issues in this domain.