Interpretation of the results by Argentine Region of the Educational Quality Assessment Operation, using machine learning models
Keywords:
selectkbest, gradient boosting classifier, dataframe, educational performanceAbstract
This work is based on the use of the Machine Learning model: Gradient Boosting Classifier from the Sklearn Library, in the Standardized Assessment Tests "Aprender" that was developed in Argentina, to measure Language Performance and Mathematics Performance. It is proposed to carry out this approach with the data from the Sixth Grade Evaluation of Primary School, from the 2018 Edition of this Educational Quality Assessment. In the research stage, only Language Performance was analyzed and the results are presented in this document by regions of Argentina. A preselection of variables was made, using the Sklearn library: SelectKBest and then a single Machine Learning model was adopted: the aforementioned Gradient. Subsequently, the calculations were executed by country and its regions and comparisons were made.
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Copyright (c) 2025 Andrés Francisco Farías, Germán Antonio Montejano, Ana Gabriela Garis, Andrés Alejandro Farías, Sebastián Javier Farías
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