Um Modelo para Predição de Reprovação de Aprendizes na Educação a Distância usando Árvore de Decisão

• João Luiz Cavalcante Ferreira Instituto Federal de Educação, Ciência e Tecnologia do Amazonas (IFAM)
• André Filipe Aloise Instituto Federal de Educação, Ciência e Tecnologia do Amazonas (IFAM)
• Vítor Kehl Matter Universidade do Vale do Rio dos Sinos (UNISINOS)
• Jorge Luis Victória Barbosa Universidade do Vale do Rio dos Sinos (UNISINOS)
• Sandro José Rigo Universidade do Vale do Rio dos Sinos (UNISINOS)
• Kleinner S. Farias de Oliveira Universidade do Vale do Rio dos Sinos (UNISINOS)

Resumo

This paper proposes the MD-PREAD, a model that uses the decision tree technique for predicting apprentices with risk of failure. The capability of choosing the decision tree as a way to generate a greater set for educators is the highlight of this project. After the data was collected and processed, it was possible to generate a list of students that had the greatest chance to fail, this data would give the opportunity to help the students to recover their grades before the end of the course. Finally, to evaluate the model, the indexes of the classifiers were compared and the J48 algorithm stood out with an accuracy predominance of 84.5%, precision of 85.52%. It was concluded that the MD-PREAD model can assist in the prognosis of groups at risk of failure.

Palavras-chave: Prediction, Decision Tree, Educational Data Mining