Tesis
Estudo dos fatores de risco associados à evasão de alunos de graduação da Universidade Federal de Santa Maria
Fecha
2018-01-12Autor
Savian, Mônica Cristina Bogoni
Institución
Resumen
Currently, Higher Education Institutions (HEIs), especially public ones, express great
concern regarding the adequate qualification of their students and assurance of
adequate results in terms of numbers of graduates who are authorized every year to
exercise their professions. Thus, the present study aims to identify and estimate the
risk factors associated with the evasion of undergraduate students from the Federal
University of Santa Maria (UFSM) in the period between 2009 and 2015 through
logistic regression models. The present study was quantitative, descriptive,
retrospective, and applied. The centers that presented the highest and the lowest
percentage of evasion were the CCNE (52.0%) and the CCS (11.6%), respectively.
About the profile of the student, it was observed that their mean age was between 20
and 28 years-old. The gender varies according to the center of education analyzed.
They are from white ethnicity, unmarried, and admitted to the university in the first
term by broad competition. On average, 65.0% of students do not live in the city
where the university is located. Through logistic regression models adapted to the
data, it was observed that in most cases the higher the student's age, the greater the
risk of evasion. With regard to quotas, it was observed that admission to university
under public school quota is generally a protection factor in relation to the wide
competition, while admission under other types of quota represents a risk. The first
semesters were the ones that presented the highest risk of evasion in all courses in
which this variable presented statistical significance in the model when compared to
the last semesters. It was observed that the variables appeared as a protection factor
for the student as for the number of modules succeeded, being a scholarship holder
or participating in projects. The risk of evasion increases at each module failed by
excessive absence or enrollment cancellation. By means of the adjusted models, it
was possible to verify the main factors associated with evasion as well as the
variables that contribute the most and that present a greater risk for such event.
Thus, it improves knowledge for the managers to implement actions that can
minimize the evasion rates at UFSM.