Estudo dos fatores de risco associados à evasão de alunos de graduação da Universidade Federal de Santa Maria - Savian, Mônica Cristina Bogoni (2018)
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.