Learning what is important: feature selection and rule extraction in a virtual course

Virtual campus environments are becoming a mainstream alternative to traditional distance higher education. The Internet medium they use allows the gathering of information on students’ usage behaviour. The knowledge extracted from this information can be fed back to the e-learning environment to ease teachers’ workload. In this context, two problems are addressed in the current study: finding which usage features are best at predicting online students’ marks, and explaining mark prediction in the form of simple and interpretable rules. To that effect, two methods are used: Fuzzy Inductive Reasoning (FIR) for feature selection and Orthogonal Search-Based Rule Extraction (OSRE). Experiments carried out on the available data indicate that students’ marks can be accurately predicted and that a small subset of variables explains the accuracy of such prediction, which can be described through a set of actionable rules.