The potentials of recommender systems challenges for student learning

Increasingly, educators make use of learning-by-doing approaches to teach students of STEM programmes the skills that they need to become successful in careers in research and development. However, we argue that the technical challenges addressed in these programmes are often too limited and therefore do not support the students in gaining the more advanced skill sets required to thrive in our technology-oriented economy. We therefore suggest to incorporate realistic and complex challenges that model real-world problems faced in industrial settings. Focusing on the domain of recommender systems, we see potentials in embedding recommender systems challenges to enhance student learning to teach students the skills required by modern data scientists.