Analysing Computer Science Courses over Time

In this paper we consider courses of a Computer Science degree in an Italian university from the year 2011 up to 2020. For each course, know number exams taken by students during given calendar and corresponding average grade; also normalized value result obtained entrance test distribution according gender. By using classification clustering techniques, analyze different data sets pre-processing original with information about their exams, highlight which show significant deviation typical progression same teaching year, as time changes. Finally, give heat maps showing order were graduated students. The shows reproducible methodology that can be applied any course similar organization, identify present critical issues over time. A strength work is variables interest, instead more frequently used personal academic concerning