The data from the Brazilian Basic Education Assessment System are the result of a relevant but
costly public policy, while at the same time they are little used by school management as a
learning diagnostic tool. However, if properly treated, they allow extracting important
information so that management can intervene in a focused way in the application of resources
and, more broadly, guide public policies to improve the teaching-learning process. This research
sought to identify the factors that influenced the Basic Education Development Index (Ideb)
score in the assessment applied in 2017, for the 9th grade/8th grade of elementary school among
schools in the state network of Tocantins. These factors were identified from the analysis of the
Ideb grade, associated with the microdata from the Basic Education Assessment System (Saeb)
questionnaires. For this, a comparative analysis of the performance in Ideb 2017 was carried
out. This analysis had a quantitative bias based on data from the principal, teacher, and school
questionnaires, with some questions being selected from these questionnaires, grouping them
into some spheres that make up school management, such as community participation, school
management, external interference, support, the socioeconomic factor, the performance, and
qualifications of the teaching staff. Thus, from an exploratory multivariate factor analysis, it
was possible to identify, among these factors, which ones are related to a greater or lower
performance in the Ideb evaluation. Multivariate factor analysis was based both on factor
extraction using Pearson's principal components method and on the one obtained by tetrachoric
correlation. From the factor analysis, it was possible to highlight that the highest performance
rates were correlated with variables related to infrastructure, such as: classrooms in good
condition, good quality internet for students and teachers, and computer and science
laboratories; schools that received regular financial support, both from the State Government
and the Federated Government, and schools located in urban areas, were also positively
correlated with schools with higher scores. In addition, it was found that the medium to high
socioeconomic index correlated with the schools that presented the highest performance index
in the Ideb. Teacher training was in the intersection region, not being a preponderant factor for
grade improvement, not being correlated exclusively with one or another group, and most of
the variables referring to teacher training did not present a significant factor loading. Although,
by analyzing the correlation matrices, it was found that teacher training contributed to solving
the problems inherent to the high rate of teacher shortages, the high turnover of the teaching
staff and the absence of teachers for specific subjects. It was concluded that the application of
the method will help the manager to understand the indexes raised about his school, correlating
it with the others and tracing a perspective with specific points where it must be improved,
remembering that the data must be worked and applied considering the regional particularities
and the school specifically.
|
