Visualising module dependencies in academic recommendations

Starting 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 everyday life, recommender systems remain underused 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.