Banca de QUALIFICAÇÃO: EVERSON MIZAEL CORTEZ SILVA

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
DISCENTE : EVERSON MIZAEL CORTEZ SILVA
DATA : 07/01/2019
HORA: 09:00
LOCAL: sala B321 - Instituto Metrópole Digital
TÍTULO:

A look at school dropout in technical and higher education courses at IFRN

PALAVRAS-CHAVES:

School dropout; Data mining; SUAP; IFRN.

PÁGINAS: 45
GRANDE ÁREA: Ciências Humanas
ÁREA: Educação
SUBÁREA: Ensino-Aprendizagem
ESPECIALIDADE: Tecnologia Educacional
RESUMO:

The Federal Network of Professional, Scientific and Technological Education (RFEPCT) has been experiencing an expansion process since 2005. Until 2018, more than 500 units were created throughout Brazil. Thus, offer and registration have increased. Despite the importance of this expansion, it is not enough just to give access to school. It is necessary to keep the students studying, as observed by LDB. School dropout is a complex issue that has reached educational institutions in different modalities and levels of education. This term is associated to different situations, the most common is an evasion characterized by registrations that were finalized without success. The Campus São Gonçalo do Amarante (SGA) of IFRN, presented in its active and regular courses in the period from 2015 to 2017 an average dropout rates of 13.4% for technical level courses and 31.5% for higher ones. As a Strategic Stay and Success Plan of IFRN establishes a maximum evasion goals in 2023 in 5% for technician and 10% for superior courses, while the PNE 2014-2024 stipulates in its goals the increase of the completion rate for technical level and higher of Federal Network in 90%. That is, even without considering other indicators such as retention rate, this campus is already out of the goals above. This paper aims to characterize the profile of students who evaded the courses at the SGA campus of IFRN. This information will be extracted from the academic system of the institution, SUAP, data such as bimonthly notes, frequencies, failures, lockups, entry form, entry note at institution. In addition, socioeconomic data such as address, among other factors will be extracted. Data mining techniques oriented by CRISP-DM methodology will discover implicit patterns in these data and possible correlations. Similar work that focused on understanding or proposing solutions to the problem of school dropout using data mining techniques was analyzed, in which some of them were able to predict an evasion of students with positive rates above 85%. As a result of this work, it is expected to propose a model with the main characteristics of a student profile who evades courses in IFRN/SGA, as  disciplines, if any, that contribute to the increase of evasion rate if the distance from residence to campus is a relevant factor, if the form or entry note in IFRN influences in some way, among other variables.

MEMBROS DA BANCA:
Presidente - 3229319 - APUENA VIEIRA GOMES
Interno - 2245086 - ISABEL DILLMANN NUNES
Interno - 1804830 - JOSE GUILHERME DA SILVA SANTA ROSA