Barbu, MarianVilanova, RamonVicario, JoséPereira, Maria JoãoAlves, PauloPodpora, MichalKawala-Janik, A.Prada, Miguel AngelDominguez, ManuelSpagnolini, AnnaFontana, L.2019-02-252019-02-252019Barbu, Marian; Vilanova, Ramon; Vicario, Jose; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Kawala-Janik, A.; Prada, Miguel; Dominguez, Manuel; Spagnolini, Anna; Fontana, L. (2019). Data mining tool for academic data exploitation: publication report on engineering students profiles. ERASMUS + KA2 / KA203978-989-20-9286-7http://hdl.handle.net/10198/18957This report summarizes the findings of the project SPEET. It relies on the initial document generated as Intellectual Output #1 and the results obtained by application of the IT tools developed in Intellectual Output #2, and Intellectual Output #3 to the academic data provided by the partner institutions. The main objectives of applying analytic techniques to evaluate the academic data sources can be categorized as follows: Improve Student Results; Create Mass-customized Programs; Improve the Learning Experience in Real-time; Reduce Dropouts and Increase Results.engAcademic analyticsLearning analyticsBig data in educationEducational data miningStudent profileDropout preventionreport