Recommender systems research and theory in higher education: A systematic literature review
Recommender systems provide the ability to personalize and adapt environments for student learning. To customize learning experiences, applications of recommender systems research in education have resulted in evidence of various recommender system approaches such as content-based filtering, collaborative filtering, and knowledge-based. This research focuses on those applications in higher education when given a non-MOOC classroom setting and examines the theoretical basis for the approaches. Learning and information systems theories are considered in this systematic review of the literature published from 2017 to early 2022. Findings indicate varying adaptive learning design recommender approaches and the potential to build the theoretical base of both learning and information systems theories.
McNett, Alicia, and Cherie Noteboom. "Recommender systems research and theory in higher education: A systematic literature review." Issues in Information Systems 23, no. 3 (2022).