Machine learning as a tool for identifying opportunities for intervention in a university module

For a university student, the main measure of success is whether or not they pass the modules for which they are enrolled. It is not always easy for a lecturer to determine whether or not a student will be successful as there may be various factors on which the final result of a student depends. Applying rules or common insights might aid a lecturer in identifying students who are struggling in a specific module. This may afford the lecturer the opportunity to devise an intervention; so as to enhance the chances of the student passing the module. To facilitate the gathering of information on which to base possible student academic interventions, this study makes use of an artificial neural network and decision tree to derive a series of insights and rules which a lecturer may use to identify struggling students. The artificial neural network and decision tree are both trained using supervised learning techniques. Individually, both the neural network and decision tree have an accuracy in excess of 77% and a sensitivity in excess of 83% on a test data set. Both models show a moderate level of agreement with a pre-labelled dataset.