Please use this identifier to cite or link to this item: http://repositorio.ufc.br/handle/riufc/73385
Type: Artigo de Periódico
Title: IoT system for school dropout prediction using machine learning techniques based on socioeconomic data
Authors: Freitas, Francisco Aislan da Silva
Vasconcelos, Francisco Fábio Ximenes
Peixoto, Solon Alves
Hassan, Mohammad Mehedi
Dewan, Muhammad Ali akber
Oliveira, Victor Hugo Costa de
Rebouças Filho, Pedro Pedrosa
Keywords: IoT;School dropout;Machine learning;Abandono escolar;Aprendizado de máquina
Issue Date: 2020
Publisher: Electronics
Citation: FREITAS, Francisco Aislan da Silva; VASCONCELOS, Francisco Fábio Ximenes; PEIXOTO, Solon Alves; HASSAN, Mohammad Mehedi; DEWAN, Muhammad Ali akber; OLIVEIRA, Victor Hugo Costa de; REBOUÇAS FILHO, Pedro Pedrosa. IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electronics, [s.l.], v. 9, n. 10, p. 1613, 2020.
Abstract: School dropout permeates various teaching modalities and has generated social, economic, political, and academic damage to those involved in the educational process. Evasion data in higher education courses show the pessimistic scenario of fragility that configures education, mainly in underdeveloped countries. In this context, this paper presents an Internet of Things (IoT) framework for predicting dropout using machine learning methods such as Decision Tree, Logistic Regression, Support Vector Machine, K-nearest neighbors, Multilayer perceptron, and Deep Learning based on socioeconomic data. With the use of socioeconomic data, it is possible to identify in the act of pre-registration who are the students likely to evade, since this information is filled in the pre-registration form. This paper proposes the automation of the prediction process by a method capable of obtaining information that would be difficult and time consuming for humans to obtain, contributing to a more accurate prediction. With the advent of IoT, it is possible to create a highly efficient and flexible tool for improving management and service-related issues, which can provide a prediction of dropout of new students entering higher-level courses, allowing personalized follow-up to students to reverse a possible dropout. The approach was validated by analyzing the accuracy, F1 score, recall, and precision parameters. The results showed that the developed system obtained 99.34% accuracy, 99.34% F1 score, 100% recall, and 98.69% precision using Decision Tree. Thus, the developed system presents itself as a viable option for use in universities to predict students likely to leave university
URI: http://www.repositorio.ufc.br/handle/riufc/73385
ISSN: 2079-9292
Access Rights: Acesso Aberto
Appears in Collections:DEEL - Artigos publicados em revista científica

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