DocumentCode
3767645
Title
Using neural networks to predict the future performance of students
Author
Ros?ngela Marques de Albuquerque;Andr? Alves Bezerra;Darielson Araujo de Souza;Lu?s Bruno Pereira do Nascimento;Jarbas Joaci de Mesquita S?;Jos? Cl?udio do Nascimento
Author_Institution
Department of Electrical and Computer Engineering, Campus Sobral - Federal University of Cear?, Estanislau Frota, S/N, CEP: 62010-560, Brazil
fYear
2015
Firstpage
109
Lastpage
113
Abstract
This work studies two models of artificial neural networks (ANN) in order to predict at the beginning of the school year the performance of students in SPAECE (Permanent System of Evaluation of Basic Education of Ceará). The factors considered as relevant to student achievements were the grade, period of study, school score and student score in elementary school, which were used as input variables to an ANN. A model of Perceptron and Multilayer Perceptron (MLP) were developed and trained using data of students of the year 2013 from schools of the 6th CREDE (State Development Regional Coordination). Test data evaluation shows that the ANN model is able to correctly predict 95% of performance of beginning students.
Keywords
"Context modeling","Standards","Logistics","Education","Color","RNA"
Publisher
ieee
Conference_Titel
Computers in Education (SIIE), 2015 International Symposium on
Type
conf
DOI
10.1109/SIIE.2015.7451658
Filename
7451658
Link To Document