A Model for Predicting Disapproval of Apprentices in Distance Education Using Decision Tree
Authors: 
João Luiz Cavalcante Ferreira, André Filipe Aloise, Vítor Kehl Matter, Jorge Luis Victória Barbosa, Sandro José Rigo, Kleinner S. Farias de OliveiraAuthors Info & Claims
Article No.: 14, Pages 1 - 8
Abstract
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.
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May 2019
623 pages
ISBN:9781450372374
DOI:10.1145/3330204
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© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Published: 20 May 2019
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