Generating Teacher Adapted Suggestions for Improving Distance Educational Systems with SIgMa

In this paper we present SIgMa, an adaptable feedback generation tool for teachers which gives information about course development in an educational Web environment with diagnostic capabilities. First student results are analysed using statistical calculations and data mining techniques. Afterwards, the feedback provision is carried out by a rule-based system that recognizes anomalous behaviour patterns among the analysis results and provides some suggestions for improvement which are adapted to teacher strategies and preferences by means of a teacher model. We present the main proposal and the implementation and a test of a restricted prototype.