Learning what is important: feature selection and rule extraction in a virtual course
Terence A. Etchells, Àngela Nebot, Alfredo Vellido, Paulo Lisböa, Francisco Mugica · 2006
Abstract. 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. 1