A data mining approach to study gender differences in scientific degrees courses

In this paper we present an analysis of the productivity of students attending scientific degree courses using data mining techniques and focusing the study on gender. Particular attention is given to the degree course in Computer Science in which the gender gap is extremely high in order to see if there are different behaviors compared to other courses in the same area. This study proves in an analytic way the existence of three categories of students with similar characteristics in terms of test results and productivity, transversal to gender.