Exploring Learning Resource Recommendation Approaches for Secondary Education
Christoph Brandstetter, Fabian Suda, Luca Papariello, Fabian Dopler, Bernhard Göschlberger · 2021
Recommender Systems are a well researched area and there are many approaches and algorithms solving different kind of problems. In TEL recommending appropriate learning resources is a common problem that cannot be generalized well due to different didactic strategies, educational needs and heterogeneous data sources. We therefore argue that a design science process is best suited to apply, combine and improve different established recommendation system approaches to TEL systems. In this paper, we report our architecture to support our design process and the lessons learned from our specific TEL use case. Finally, we conclude discussing the open problems, advantages of our architectural approach and directions for future research.