Determining students' academic failure profile founded on data mining methods

Exams failure among university students has long fed a large number of debates, many education experts seeking to comprehend and explicate it, and many statisticians have tried to predict it. Understanding, predicting and preventing the academic failure are complex and continuous processes anchored in past and present information collected from scholastic situations and students' surveys, but also on scientific research based on data mining technologies. In the current article the authors illustrate their experiments in the educational area, based on classification learning and data clustering techniques, made in order to draw up the students' profile for exam failure/success.