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Navigating Academia – Recommender Systems for Module Exploration

2022, Hagemann, Nina, 0000-0001-7705-2368

Personalised recommendations feature prominently in many aspects of our lives, from the movies we watch to the news we read and even the people we date. However, one area that is still relatively underdeveloped is the educational sector, where recommender systems have the potential to help students in a variety of ways, support- ing their decision making when choosing a suitable university programme, finding the right study material, and making informed choices about their learning pathways. This work focuses on recommender systems for academic advising, helping students find the most suitable modules. Today’s students enjoy various options regarding the availability of courses and modules, encouraging students to broaden their horizons, explore their interests and strengths, and develop new skills. One such opportunity offered in many universities is the possibility to freely choose elective modules from outside a student’s primary area of study. Taking such elective modules is often a requirement and can significantly impact students’ academic experience and overall performance. In this thesis, we explore how recommender systems, and content-based approaches, in particular, can be used to support students in finding suitable modules, shape their academic and career paths, as well as gain knowledge and make more informed decisions. Our approach is based on the textual descriptors that are freely available on universities module catalogues to match students with modules based on their learned interests and preferences. In contrast to the majority of related work in the field, our approaches work independently of students’ demographic, personal, and performance data. We show how the module descriptors can be used to extract module similarities and latent topics that allow for rich visualisation options and personalised module recommender systems. We evaluate our approach using offline and online studies. In a live user study, we show that our approach can improve student knowledge about their subject and elective module options. Furthermore, the results show that the participating students largely enjoy interacting with the system and show a high likeliness of reusing the system again in the future.