Assessing the educational performance of different Brazilian school cycles using data science methods

Abstract

Educational indicators are metrics that assist in assessing the quality of the educational system. They are often associated with economic and social factors suggested to contribute to good school performance, however there is no consensus on the impact of these factors. The main objective of this work was to evaluate the factors related to school performance. Using a data set composed by Brazilian schools’ performance (IDEB), socioeconomic and school structure variables, we generated different models. The non-linear model predicted the best performance, measured by the error and determination coefficient metrics. The heterogeneity of the importance of the variable between school cycles and regions of the country was detected, this effect may contribute to the development of public educational policies.

Applications of Artificial Intelligence Models in Educational Analytics and Decision Making: A Systematic Review

“…The number of studies in LA that uses AI resources for educational analysis may be low due to a lack of familiarity with the methods, but AI methodologies are becoming more accessible, whether through MOOC courses or platforms like Google Colab, where complex analyses can be performed remotely. Our searches point to Brazil as a separate case in this scenario, as some studies are promoting a new direction for the results obtained via AI [53,75]. Studies from Colombia go in the same direction [92].…”

“…Traditionally, developing countries already have less technological infrastructure and skilled labor, and will have to make greater efforts not to further accentuate the difference from developed economies. Maia et al [53] mention numerous gaps in the examination of a data set of more than 90% of Brazilian public schools. A comparison of traditional and AI approaches was carried out to measure the educational and socioeconomic aspects that are related to school achievement, with the latter having the best performance-considering error metrics and determination coefficient.…”

“…A comparison of traditional and AI approaches was carried out to measure the educational and socioeconomic aspects that are related to school achievement, with the latter having the best performance-considering error metrics and determination coefficient. The distinctiveness of each school period evaluated and the distinction of the relevance of variables in the school stage became clear, making such specifications vital to incorporate in projects and public policies that decision-makers and other workers come to carry out [53].…”

“…Highly valuable approaches can be utilized for this, as datasets in education can be simply integrated within the scope of AI [54]. Different AI approaches can generate more precise and consistent results when applied to traditional methodologies [53,55]. However, there are still a few projects in education that benefit from AI, Data Science, and Machine Learning resources.…”

Beyond scores: A machine learning approach to comparing educational system effectiveness

“…He built a specific model for each culture and compared the results. Other studies [ 18 , 25 , 26 ] used unique models to analyze differences in the predictive relevance of variables in student outcomes for each of the five Brazilian regions using a Brazilian LSA. Their results showed significant differences among regions, which suggested the need for further intranational analysis to understand why some variables were important for student outcomes in some regions and others were important in other regions.…”