Home // ICCGI 2013, The Eighth International Multi-Conference on Computing in the Global Information Technology // View article
Authors:
Miguel Angel Gil Rios
Norma Griselda Reyes Ávila
María Dolores Juárez Ramírez
Emmanuel Espitia Rea
Julio César Mosqueda Gómez
Myriam Soria García
Keywords: artificial neural networks; students drop out; early prediction; university drop out
Abstract:
This research is focused on the use of an Artificial Neural Network-based prototype in order to measure and predict the probability that students drop out of university. This probability is calculated in an early stage when the students get enrolled in some of the university study programs. Once we obtain the results they are analyzed and compared in order to know how the factors affect to the model behavior and the predicted result. Finally, we describe how this research can assist student advisors by using a support tool that helps them to identify in a fast way those students with a high risk of dropping out of university, and help them before they quit school.
Pages: 289 to 294
Copyright: Copyright (c) IARIA, 2013
Publication date: July 21, 2013
Published in: conference
ISSN: 2308-4529
ISBN: 978-1-61208-283-7
Location: Nice, France
Dates: from July 21, 2013 to July 26, 2013