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Master's Dissertation
DOI
https://doi.org/10.11606/D.104.2020.tde-23032020-100937
Document
Author
Full name
Fabiana Arca Cruz Tortorelli
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2020
Supervisor
Committee
Cobre, Juliana (President)
Nakano, Eduardo Yoshio
Shimakura, Silvia Emiko
Title in Portuguese
Modelos de Riscos Competitivos no Estudo de Evasão Discente
Keywords in Portuguese
Evasão universitária
Inferênica Bayesiana
Modelo log-log Commplementar de riscos competitivos
Riscos competitivos
Abstract in Portuguese
Um dos problemas enfrentado por universidades públicas e privadas é a evasão discente. Desta forma, a motivação deste trabalho foi investigar as características que levam um aluno matriculado em um curso regular de graduação aos desfechos evadir e graduar. Para o estudo utilizamos modelos de riscos competitivos para dados discretos, e propomos utilizar a transformação log-log complementar na função de risco para estudar o risco da causa específica (evadir, graduar) ao longo de um período de tempo calculado em semestres. As estimativas dos parâmetros e seleção do modelo foram obtidas através da inferência bayesiana. Verificamos que o modelo proposto neste trabalho consegue ser ajustado aos dados teóricos. Na aplicação em dados reais do curso de Matemática Aplicada do Instituto de Ciências Matemáticas e de Computação (ICMC) concluímos que o grau de instrução dos pais e forma de ingresso podem contribuir para evasão discente.
Title in English
Study of University Dropout using Competing Risks Models
Keywords in English
Bayesian Inference
Competing Risks
Competing risks complementary log-log model
University Dropout
Abstract in English
Dropout is a problem faced by public and private universities. Therefore, the motivation of this work is to investigate the characteristics of students enrolled in a regular undergraduate course and verify which one is related with dropout and/or graduate. For the study we consider competitive risk models, and we propose to use the transformation of complementary log-log for the risk function into discrete data to study specific cause risk at a period of time (calculated in semesters). For the parameters estimations we used Bayesian Inference. We verified that the model proposed in this work can be adjusted to the theoretical data. In real data from the Applied Mathematics course to the Institute of Mathematical and Computer Sciences (ICMC), we concluded that parental education and admission type influence in dropout outcome.
 
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Publishing Date
2020-03-23
 
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