SPEET is an ERASMUS+ project aimed to determine and categorize the different profiles for engineering students across Europe. The main rationale behind this proposal is the observation that students performance can be classified according to their behavior while conducting their studies. After years of teaching and sharing thoughts among colleagues from different EU institutions it seems students could obey to some classification according to the way they face their studies. Therefore, if it would be possible to know what kind of student one student is, this may be of valuable help for tutors.

The project goal emerges from the potential synergy among:
 
a) the huge amount of academic data actually existing at the academic departments of faculties and schools, and
b) the maturity of data science in order to provide algorithms and tools to analyse and extract information from what is more commonly referred to Big Data.  

A rich picture can be extracted from this data if conveniently processed. The purpose of this project is to apply data mining algorithms to process this data in order to extract information about and to identify student profiles. An idea of the student profile we are referring to within the project scope is, for example: Students that finish degree on time, Students that are blocked on a certain set of subjects, Students that leave degree earlier, etc.

With such classification that, of course, devise a more precise definition and categorisation that will be established from the very beginning of the project the more usual student patterns will be depicted. Comparison among the different partner institutions will be done in order to establish correlations and get a more complete European-level picture.

The main question that will be asked once these student profiles are determined is regarding once a new student gets enrolled, could we know as in advance as possible which profile this student obeys to? This would definitively help tutor this student providing data founded recommendations in order to avoid early leaving, increase motivation and better pass blocking subjects, etc.

An IT tool is intended to be produced in order to help disseminate the study and allow other faculties and schools to conduct similar study. It is worth to stress that as far as the scope of this project is concerned, the study will concentrate on engineering students. This will help to delimit and better define / analyse the results.

DEIB is involved together with MAT Department (resp. prof. Anna Maria Paganoni) in characterizing the statistical behavior of the ensemble of the population of students to highlight the recurring pattern and minimize the student drop-off at early stage (undergraduate level). Methods are based on statistical analyses of the large ensemble of data, commonly known as big data analytic tailored for student careers. Data are from multiple engineering Faculties all over Europe and from Politecnico di Milano the project includes the cooperation with the School of Information and Industrial Engineering (resp. dr. Aldo Torrebruno).