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… Show more
“…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].…”
Section: Discussionmentioning
confidence: 65%
“…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.…”
Section: Ai For Education: New Methods Of Analysismentioning
confidence: 99%
“…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].…”
Section: Ai For Education: New Methods Of Analysismentioning
confidence: 99%
“…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.…”
Section: Ai For Education: New Methods Of Analysismentioning
Education plays a critical role in society as it promotes economic development through human capital, reduces crime, and improves general well-being. In any country, especially in the developing ones, its presence on the political agenda is necessary. Despite recent educational advances, those developing countries have increased enrollments, but academic performance has fallen far short of expectations. According to international evaluations, Latin American countries have made little progress in recent years, considering the level of investment in education. Thus, Artificial Intelligence (AI) models, which deal with data differently from traditional analysis methods, can be an option to better understand educational dynamics and detect patterns. Through a literature review using the PRISMA methodology, we investigated how AI has been used to evaluate educational performance in basic education (elementary and high school) in several countries. We searched five platforms, resulting in a total of 19,114 works retrieved, and 70 articles included in the review. Among the main findings of this study, we can mention: (i) low adherence to the use of AI methodology in education for practical actions; (ii) restriction of analyzes to specific datasets; (iii) most studies focus on computational methodology and not on the meaning of the results for education; and (iv) a less trend to use AI methods, especially in Latin America. The COVID-19 pandemic has exacerbated educational challenges, highlighting the need for innovative solutions. Given the gap in the use of AI in education, we propose its methods for global academic evaluation as a means of supporting public policy-making and resource allocation. We estimate that these methods may yield better results more quickly, enabling us to better address the urgent needs of students and educators worldwide.
“…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].…”
Section: Discussionmentioning
confidence: 65%
“…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.…”
Section: Ai For Education: New Methods Of Analysismentioning
confidence: 99%
“…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].…”
Section: Ai For Education: New Methods Of Analysismentioning
confidence: 99%
“…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.…”
Section: Ai For Education: New Methods Of Analysismentioning
Education plays a critical role in society as it promotes economic development through human capital, reduces crime, and improves general well-being. In any country, especially in the developing ones, its presence on the political agenda is necessary. Despite recent educational advances, those developing countries have increased enrollments, but academic performance has fallen far short of expectations. According to international evaluations, Latin American countries have made little progress in recent years, considering the level of investment in education. Thus, Artificial Intelligence (AI) models, which deal with data differently from traditional analysis methods, can be an option to better understand educational dynamics and detect patterns. Through a literature review using the PRISMA methodology, we investigated how AI has been used to evaluate educational performance in basic education (elementary and high school) in several countries. We searched five platforms, resulting in a total of 19,114 works retrieved, and 70 articles included in the review. Among the main findings of this study, we can mention: (i) low adherence to the use of AI methodology in education for practical actions; (ii) restriction of analyzes to specific datasets; (iii) most studies focus on computational methodology and not on the meaning of the results for education; and (iv) a less trend to use AI methods, especially in Latin America. The COVID-19 pandemic has exacerbated educational challenges, highlighting the need for innovative solutions. Given the gap in the use of AI in education, we propose its methods for global academic evaluation as a means of supporting public policy-making and resource allocation. We estimate that these methods may yield better results more quickly, enabling us to better address the urgent needs of students and educators worldwide.
“…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.…”
Studies comparing large-scale assessment data among educational systems have been an important tool for understanding the differences in how education is delivered worldwide. Many of these studies do not go beyond reporting average student scores in a particular educational system. A more unbiased analysis would avoid the simple use of gross performance and consider educational system contexts. A common approach is to estimate effectiveness by the residuals of parametric linear models. These models rely upon strong assumptions regarding the data-generating process, and are limited to handling extensive datasets. To address this issue, our paper provides a new approach based on machine learning models. The new approach is flexible, allows paired comparison, and is model-independent. An analysis conducted in Brazil verifies the suitability of the method to explore differences in effectiveness between Brazilian educational administrative units at the regional and state levels from 2009 to 2019. Our results are consistent with the existing literature, but the methodology produced a number of new findings that were not observed in studies using more traditional approaches.
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