Uso de Séries Temporais e Seleção de Atributos em Mineração de Dados Educacionais para Previsão de Desempenho Acadêmico

Rodrigo Santos, Cristiano Pitangui, Luciana Assis, Alessandro Vivas


The academic performance prediction can be very useful for Educational Institutions in order to help them to take pedagogical decisions that can help students. In this work, we present experiments using Moodle data, Time Series and the Feature Selection Wrapper approach, since, to best of our knowledge, this method to reduce the number of features have not been used in this kind of data. Results showed an improvement in the performance of classifiers, some obtaining the remarkable mark of 84.7% in accuracy results.

Texto completo: