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Hagemann, Nina
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Hagemann, Nina
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Hagemann, Nina
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- PublicationVisualising Module Dependencies in Academic RecommendationsStarting their academic career can be overwhelming for many young people. Students are often presented with a variety of options within their programmes of study and making appropriate and informed decisions can be a challenge. Compared to many other areas in our every day life, recommender systems remain under used in the academic setting. In this part of our research we use non-negative matrix factorisation to identify dependencies between modules, visualise sequential recommendations, and bring structure and clarity into the academic module space.
Scopus© Citations 6 565