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Orientador(es)
Resumo(s)
This 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.
Descrição
Palavras-chave
Academic analytics Learning analytics Big data in education Educational data mining Student profile Dropout prevention
Contexto Educativo
Citação
Barbu, 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 / KA203