By Jucemar Paes Neto and Daniel Christian Henrique
Education in Brazil since the 1990s has instituted some changes in its legislation and education systems in an attempt to offer greater possibilities of access to education to different segments of its population. The main change arising from this era was the enactment of the Law on Guidelines and Bases for National Education (lei número 9394/96). Among some essential points of this law, the possibility of obtaining profit from private institutions was stipulated, and they must submit to the same legislation applied to commercial companies, conferring on them the duty to pay taxes on equity, income and services, like any other commercial company (CUNHA, 2007).
Higher education begins to shift its focus, no longer oriented to a specific segmentation of society, almost totally elitist and traditionalist, bringing together groups that previously had no way of achieving this possibility. Initially, beginning in the 2000s, this growth was driven more by the private sector initiatives of the HEIs, with even a slight retraction from the public sector, induced by the new functions that LGB provided them to assume. As a result, a range of colleges, university centers, universities and higher education technical and technological institutions are spread across the country. Each generating their courses in accordance with the specificities of the region and their demand, further deepening the peculiarities of the courses, differentiated by new themes, specialties, areas in accordance with the new divisions of work (PRATES; BARBOSA, 2015). It is worth mentioning that the main differentiator was the creation of Higher Technology Courses, enabling training that is more applied to the labor market, focused and in less time.
Other points worth mentioning in the LGB, associated with the policy of expanding universities, were the accreditation of higher education institutions to offer distance learning courses. Since the implementation of the first distance education courses in 1999, the number of courses in this modality has grown exponentially year on year, with the private sector still responsible for the largest portion of courses (DOURADO, 2008).
Finally, in an effort to meet the determinations of the National Education Plan that stipulated the need to increase the schooling of young people between 18 and 24 years of age, PROUNI (Programa Universidade Para Todos "University for All Program", our translation) was created by the federal government. In this program, HEIs would be entitled to tax exemption provided they offer free scholarships to students in higher and sequential courses. The initial screen for choosing these students would be proof of having attended high school in public schools (or with a full scholarship in private schools), with low family income, blacks, indigenous people or browns (CUNHA, 2007).
This expansion of access was not restricted to private HEIs, Decree no. 6,096 in 2007 instituted REUNI (Programa de Apoio a Planos de Reestruturação e Expansão das Universidades Federais, "Program to Support Federal University Restructuring and Expansion Plans", our translation), which expanded federal universities to the entire interior of the country, allowing free access to students who previously could not move to other regions of the state or the country to take a higher education course, even if free. Cunha (2007) corroborates by complementing that the program also provides financial support to universities that manage to reduce dropout, fill idle vacancies, increase the number of vacancies for new entrants, especially at night, a period of greater access for those students who need to work for maintain their livelihood or at the university itself.It is common sense in the academic environment that the funding scholarship alone is not enough for many new students benefiting from them, especially in federal and state universities that offer full courses, making it impossible for students to work in the morning or afternoon to obtain additional resources. needed for your needs. Possibilities of support in housing, transportation, educational material or any other support that offers greater possibility of permanence and a dignified study are also necessary. They are also offered in the form of scholarships, generally according to specific selection criteria for each educational institution.
After meeting the basic needs for conducting studies, other means can be obtained by students who additionally improve their learning and market practices, such as research, monitoring, extension and internship scholarships. It is worth mentioning that the accumulation of these grants is not allowed, but obtaining support grants (housing, transportation, etc.) are not an obstacle to competition from these last grants.
Within this whole context, a study was carried out with the aim of verifying whether the increase or decrease in the probabilities of Brazilian higher education students to complete their course or not are related to some of these grants and funding, as well as which are most contributing to this formation.
The data were obtained from the 2018 Higher Education Census (INEP, 2020) using the “Alunos” (students) database formed by 105 variables with more than 10 million rows. Therefore, data analysis procedures in big data were requested to initiate the analyzes. The data were generated by MEC through an individual questionnaire per student (found in Annex II of the folder).
The chosen variable to be explained in the analysis is shown below (INEP, 2020):
It has binary “yes” or “no” responses represented by “1” and “0”, respectively, in the database. Therefore, Logistic Regression procedures were put in place. The explanatory variables are also binary and have been separated into 5 equations to facilitate the analysis, with their descriptions in sequence (INEP, 2020):
Independent variables in Equation 1:
Independent variables in Equation 2:
Independent variable in Equation 3:
Independent variables in Equation 4:
Independent variable in Equation 5:
In view of the immense magnitude of student data from all over Brazil for several variables, it was necessary to use half of the available data to run in R software with specific programs for big data analysis, totaling 5 million rows of the table - generating between 10 and 30 million data in each analysis, much higher than any statistical sample.
The regression of the “Graduates” variable as a result of Equation 1 did not obtain good results in the approval tests of the equation coefficients, using a significance level equal to 5%. Then, a supplementary step was performed using the Stepwise Backward technique to eliminate explanatory variables that could be incurring multicollinearity. The procedure eliminated the variables “Extension Scholarship”, “Internship Scholarship” and “Labor Scholarship”. The model without these variables was approved in Wald's z statistic (see table 1) and obtained good results from McFadden's pseudo-R² and in Nagelkerke's R2 (see table 2).
The next two new logistic regressions to explain the variable “Graduates” by the variables of Equations 1 and 2 of scholarships and support, respectively, obtained all the coefficients approved in Wald's z statistic (see table 1). The values of pseudo-R² were satisfactory for group 2:
Table 1 - Wald z test of the coefficients - Equations 1 and 2
Equations | Variable | Coefficient b | Exp b | p-value of z |
Equation 1 | (Intercept) | -1,34451 | 0,260667 | < 2 E -16 |
IN_BOLSA_MONITORIA | 0,14448 | 1,155439 | 0,002116 | |
IN_BOLSA_PESQUISA | -0,14976 | 0,860915 | 0,000221 | |
Equation 2 | (Intercept) | -2,15673 | 0,115703 | < 2 E -16 |
IN_APOIO_BOLSA_PERMANENCIA | -0,05688 | 0,944707 | 0,00181 | |
IN_APOIO_MATERIAL_DIDATICO | 0,07071 | 1,07327 | 2,73 E -09 | |
IN_APOIO_MORADIA | -0,1801 | 0,835187 | 1,38 E -10 | |
IN_APOIO_TRANSPORTE | 0,24032 | 1,271656 | < 2 E -16 |
Table 2 - Model statistics - Equations 1 and 2
Model Statistics | Equation 1 | Equation 2 |
MCFadden's R2 | 0,991 | 0,837 |
McFadden's Adj R2 | 0,991 | 0,837 |
ML (Cox-Snell) R2 | 0,495 | 0,438 |
Cragg-Uhler(Nagelkerke) R2 | 0,994 | 0,88 |
The first conclusion that can be seen from the results is that the grants related more directly to teaching and research activities were more relevant to the number of graduating students, given the exclusion of the other variables, but in opposite directions. The positive coefficient of the monitoring grant in the developed equation together with its exp (b) value denote the chances of the student with this aid to complete his / her course is 1.15 times higher than the student who does not have this scholarship. Whereas for research scholarship holders, with a negative coefficient, their chances of forming equivalent to 0.86 times compared to those who do not have it. More clearly, research fellows reduce their chances of graduating by 14% compared to those who do not have the same type of scholarship. Only qualitative research could address the reasons for this dropout. The monitoring, in turn, is characterized by activities to be developed in accordance with the performance of the student in a discipline already taken and with a good degree of approval for the development of actions of pedagogical support to other students and aid to the preparation activity of teaching material to the teacher, it may turn out to be an activity with less risk of failure over time, since it has already been successful in the discipline it will support. The research grant, in turn, requires the involvement of several variables for the success of its conclusion, not previously absorbed. Here, there is a stimulus for some research to find these motivations and discouragements.
With a focus on Equation 2 of analysis, the greatest positive highlight was support for transportation, which became the support grant with the greatest expression in helping to complete courses, increasing the chances of graduating 1.27 times compared to non-holders of this scholarship. With a negative correlation, support for housing stands out, reducing the chances of completing the course. This scholarship is also characteristic of federal and state universities for offering full courses and the need for almost exclusive dedication to studies and the consequent inability to work during the day for students to financially support themselves in the city, requiring support for a residence - also seen that rent is one of the most costly expenses that students have in university life.
Entering the analysis of Equation 3, despite the low values obtained in the pseudo-R² tests regarding the contribution of student financing, its observation is still relevant, as this variable includes all types of financing that are also found divided into several other variables in the Higher Education Census database:
Table 3 - Wald z test of the coefficients - Equations 3, 4 and 5
Equações | Variable | Coef. bj | Exp(bj) | p-value of z |
Equation 3 | (Intercept) | -2,204555 | 0,1103 | < 2 E -16 |
IN_FINANCIAMENTO_ESTUDANTIL | 0,241519 | 1,273182 | < 2 E -16 | |
Equation 4 | (Intercept) | -2,010181 | 0,133964 | < 2 E-16 |
IN_FIN_NAOREEMB_PROUNI_PARCIAL | 0,291971 | 1,339064 | < 2 E-16 | |
IN_FIN_NAOREEMB_PROUNI_INTEGR | 0,228895 | 1,25721 | < 2 E-16 | |
Equation 5 | (Intercept) | -2,172494 | 0,113893 | < 2 E-16 |
IN_FIN_REEMB_FIES | 0,895802 | 2,449299 | < 2 E-16 |
Table 4 - Model statistics - Equations 3, 4 and 5
Model Statistics | Equation 3 | Equation 4 | Equation 5 |
MCFadden's R2 | 0,153 | 0,639 | 0,646 |
McFadden's Adj R2 | 0,153 | 0,639 | 0,646 |
ML (Cox-Snell) R2 | 0,1 | 0,356 | 0,359 |
Cragg-Uhler(Nagelkerke) R2 | 0,201 | 0,715 | 0,722 |
Unlike academic and support scholarships, which are for a fixed period, access to finance may be the key factor in completing or not the course or not. In the absence of scholarships or work to support tuition fees, only the release of financing offers the necessary resources to maintain the studies, which may be reimbursable or not. Among the various modalities seen above, the most prominent are the FIES refundable financing programs, originating from the federal government, and the PROUNI non-refundable programs.
The logistic regression shows that, despite the different types of financing, obtaining any of them increases the chances of graduating by only 1.27 times, according to the value of exp (b) of its coefficient. The problem is in the immense variety of types of financing immersed in the variable. Noting the problem, two other logistical regressions were run in group 4, now focused only on investigating the isolated contribution of the PROUNI and FIES scholarships.
In the partial or full financing of PROUNI generated by Equation 4, little has changed. Partial financing establishes a 1.34 times increase in the chance of graduating compared to non-scholarship students, while full-funding increases 1.26 times their chances, but now with a good prediction equation, indicated by a 71.5% Nagelkerke R2.
These not so high percentages can be explained in the words of Aprile (2008) when expressing that despite the program's contribution to the country's scientific and technological development and income distribution, its insertion was established in a precarious basic education scenario in nationwide, reflecting on the quality of high school graduates.
Carvalho (2005 apud Aprile, 2008) also complements that only full or partial gratuity to studies for low-income people is not enough to keep them in the private sector academy. Conditions similar to those offered by public universities would also be necessary, such as transportation, student housing, subsidized food, research grants, extension, etc. Catani, Gilioli and Hey (2006) also conclude that the student's permanence in the course is essential for the democratization of teaching, so it is not enough to offer benefits, but also rights. Finally, it deserves attention some results from Saraiva and Nunes (2010) that those who managed to enter and finish their student journey in higher education benefited by PROUNI felt included in society, realizing a previously unreachable dream and changing their lives.
And FIES only? They were analyzed in Equation 5. The change was significant. A student with this funding increase in approximately two and a half times his chances of becoming a graduate of the course, attested by a predictive equation with Nagelkerke and MacFadden's R2 equivalent to 72.2% and 64.6% respectively. Therefore, two federal programs, two realities. How can FIES reimbursable financing extend course conclusions so dramatically than a non-reimbursable one like PROUNI? Some points highlighted by Oliveira and Carnielli (2010) guide possible explanations for this greater conclusion of the students: (1) employed students also need financing to be able to pay the tuition fees, often not compatible with their salaries; (2) the need for a guarantor increases the student's responsibility (although many consider it difficult to access finance); (3) the possibility of planning your life, which will enable you to enter the job market in the future or to undertake with the knowledge obtained. As negative points, the research highlights: (1) the payment term offered by CEF could be more flexible, in accordance with the student's situation after graduation; (2) some students nearing completion of the course say they are sorry about the future debt they had to handle.
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