d25518ae4e635af0197366e056e51c78 ijgeoinf_a2021v10n12p830.pdf 7e0e50c1884e432c93bb0e6a174567a4588ec874 ijgeoinf_a2021v10n12p830.pdf 3bd334ec705c2e118987a63fca2db51e6c55b40ce9b33610eeeb2df730799aff ijgeoinf_a2021v10n12p830.pdf Title: Urban–Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador Subject: Academic performance (AP) is explained by a multitude of factors, principally by those related to socioeconomic, cultural, and educational environments. However, AP is less understood from a spatial perspective. The aim of this study was to investigate a methodology using a machine learning approach to determine which answers from a questionnaire-based survey were relevant for explaining the high AP of secondary school students across urban–rural gradients in Ecuador. We used high school locations to construct individual datasets and stratify them according to the AP scores. Using the Boruta algorithm and backward elimination, we identified the best predictors, classified them using random forest, and mapped the AP classification probabilities. We summarized these results as frequent answers observed for each natural region in Ecuador and used their probability outputs to formulate hypotheses with respect to the urban–rural gradient derived from annual maps of impervious surfaces. Our approach resulted in a cartographic analysis of AP probabilities with overall accuracies around 0.83–0.84% and Kappa values of 0.65–0.67%. High AP was primarily related to answers regarding the academic environment and cognitive skills. These identified answers varied depending on the region, which allowed for different interpretations of the driving factors of AP in Ecuador. A rural-to-urban transition ranging 8–17 years was found to be the timespan correlated with achievement of high AP. Keywords: academic performance; impervious surfaces; urban-rural; Ecuador Author: Fabián Santos-García, Karina Delgado Valdivieso, Andreas Rienow and Joaquín Gairín Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Dec 10 12:39:35 2021 CET ModDate: Fri Dec 10 15:05:03 2021 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 27 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 8093251 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- WRSUDH+VnURWPalladioL Type 1 Custom yes yes no 10 0 NCQCNA+URWPalladioL-Ital Type 1 Custom yes yes no 15 0 SVJLJX+URWPalladioL-Bold Type 1 Custom yes yes no 20 0 QJYWAL+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 SSNIUI+VnURWPalladioL-Bold Type 1 Custom yes yes no 28 0 IRWHZX+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 36 0 EHEDSU+TimesNewRomanPS-BoldItalicMT 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SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Children: Item: Title: The BCH Instrument and Its Preprocessing Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Academic Performance and Stratification Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: Feature Space and Indexing Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Study Area and High School Descriptions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Urban Rural Gradient Derivation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Feature Selection and Prediction Algorithms Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Children: Item: Title: Random Forest (RF) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: The Boruta Approach Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Sampling Strategy Based on Distances to High Schools Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Implementation of Boruta and RF for the BCH Instrument Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: AP Probability Hot Spots and Testing Hypotheses with the Urban Rural Gradient Map Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Children: Item: Title: Model Predictive Performance Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Item: Title: Model Sensitivity and Complexity Assessment Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@3aeaafa6 Item: Title: Frequent Best Predictor Themes Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@76a3e297 Item: Title: Frequent Best Predictor Answers to Survey Questions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Item: Title: AP Probability Hot Spots and Significance Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed9d034 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6121c9d6 Children: Item: Title: Stratification and Urban Rural Gradient Map Derivation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@87f383f Item: Title: Distance-Based Sampling and Boruta Implementation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4eb7f003 Item: Title: Hot Spot-Based Hypothesis and Spatial Organization of AP Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@eafc191 Item: Title: Best Predictive Answers and Implications Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612fc6eb Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1060b431 Item: Title: Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612679d6 Item: Title: Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@11758f2a Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@e720b71 Info: Title: Urban Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador Author: Fabián Santos-García, Karina Delgado Valdivieso, Andreas Rienow and Joaquín Gairín Subject: Academic performance (AP) is explained by a multitude of factors, principally by those related to socioeconomic, cultural, and educational environments. However, AP is less understood from a spatial perspective. The aim of this study was to investigate a methodology using a machine learning approach to determine which answers from a questionnaire-based survey were relevant for explaining the high AP of secondary school students across urban rural gradients in Ecuador. We used high school locations to construct individual datasets and stratify them according to the AP scores. Using the Boruta algorithm and backward elimination, we identified the best predictors, classified them using random forest, and mapped the AP classification probabilities. We summarized these results as frequent answers observed for each natural region in Ecuador and used their probability outputs to formulate hypotheses with respect to the urban rural gradient derived from annual maps of impervious surfaces. Our approach resulted in a cartographic analysis of AP probabilities with overall accuracies around 0.83 0.84% and Kappa values of 0.65 0.67%. High AP was primarily related to answers regarding the academic environment and cognitive skills. These identified answers varied depending on the region, which allowed for different interpretations of the driving factors of AP in Ecuador. A rural-to-urban transition ranging 8 17 years was found to be the timespan correlated with achievement of high AP. 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