Mineração de dados aplicada a educação: Um estudo comparativo acerca das características que influenciam a evasão escolar

Calixto, Kennet Emerson Avelino; Segundo, Caetano Vieira Neto; Gusmão, Renê Pereira de

doi:10.36229/978-65-86127-64-5.cap.22

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“…The number of studies that seek to predict dropout has intensified in recent years. Researchers such as [16][17][18][19][20][21] carried out surveys that sought to predict which students were likely to drop out of courses in the face-to-face modality.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [16,17] used school data from the census between the years 2011 to 2016 in the states of Pernambuco, Sergipe, and Ceará, in Brazil; using predictive characteristics such as age, gender, and demographic region presented significant results within the studied context. Authors in [16] applied the Decision Tree classifier and obtained 69% accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [16] applied the Decision Tree classifier and obtained 69% accuracy. Authors in [17] used data from Ceará and Sergipe between the years 2014 and 2016 and reached 87% on the same metric with logistic regression.…”

Section: Related Workmentioning

confidence: 99%

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IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data

Freitas¹,

Vasconcelos²,

Peixoto

et al. 2020

Electronics

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data

Freitas¹,

Vasconcelos²,

Peixoto

et al. 2020

Electronics

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show abstract

Mineração de dados aplicada a educação: Um estudo comparativo acerca das características que influenciam a evasão escolar

Abstract: Modo de acesso: World Wide Web Inclui bibliografia 1. Educação 2. Tecnologias 3. Prática Pedagógica I. Título CDD-370 O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos seus respectivos autores www.poisson.com.br

Cited by 1 publication

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IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data

IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data

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