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Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach
The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-tri...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873351/ https://www.ncbi.nlm.nih.gov/pubmed/31803116 http://dx.doi.org/10.3389/fpsyg.2019.02583 |
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author | Martínez-Abad, Fernando |
author_facet | Martínez-Abad, Fernando |
author_sort | Martínez-Abad, Fernando |
collection | PubMed |
description | The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-trivial information from massive databases. This work, which falls within this context, proposes an innovative approach for the identification and characterization of educational and organizational factors associated with high school effectiveness. Under a perspective of basic research, our aim is to study the suitability of decision trees, techniques inherent to data mining, to establish predictive models for school effectiveness. Based on the available Spanish sample of the PISA 2015 assessment, an indicator of the school effectiveness was obtained from the application of multilevel models with predictor variables of a contextual nature. After selecting high- and low-effectiveness schools in this first phase, the second phase of the study was carried out and consisted of the application of decision trees to identify school, teacher, and student factors associated with high and low effectiveness. The C4.5 algorithm was calculated and, as a result, we obtained 120 different decision trees based on five determining factors (database used; stratification in the initial selection of schools; significance of the predictor variables of the models; use of items and/or scales; and use of the training or validated samples). The results show that the use of this kind of technique could be appropriate if mainly used with correctly pre-processed data that include the combined information available from all educational agents. This study represents a major breakthrough in the study of the factors associated with school effectiveness from a quantitative approach, since it proposes and provides a simple and appropriate procedure for modeling and establishing patterns. In doing so, it contributes to the development of knowledge in the field of school effectiveness that can help in educational decision-making. |
format | Online Article Text |
id | pubmed-6873351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68733512019-12-04 Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach Martínez-Abad, Fernando Front Psychol Psychology The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-trivial information from massive databases. This work, which falls within this context, proposes an innovative approach for the identification and characterization of educational and organizational factors associated with high school effectiveness. Under a perspective of basic research, our aim is to study the suitability of decision trees, techniques inherent to data mining, to establish predictive models for school effectiveness. Based on the available Spanish sample of the PISA 2015 assessment, an indicator of the school effectiveness was obtained from the application of multilevel models with predictor variables of a contextual nature. After selecting high- and low-effectiveness schools in this first phase, the second phase of the study was carried out and consisted of the application of decision trees to identify school, teacher, and student factors associated with high and low effectiveness. The C4.5 algorithm was calculated and, as a result, we obtained 120 different decision trees based on five determining factors (database used; stratification in the initial selection of schools; significance of the predictor variables of the models; use of items and/or scales; and use of the training or validated samples). The results show that the use of this kind of technique could be appropriate if mainly used with correctly pre-processed data that include the combined information available from all educational agents. This study represents a major breakthrough in the study of the factors associated with school effectiveness from a quantitative approach, since it proposes and provides a simple and appropriate procedure for modeling and establishing patterns. In doing so, it contributes to the development of knowledge in the field of school effectiveness that can help in educational decision-making. Frontiers Media S.A. 2019-11-15 /pmc/articles/PMC6873351/ /pubmed/31803116 http://dx.doi.org/10.3389/fpsyg.2019.02583 Text en Copyright © 2019 Martínez-Abad. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Martínez-Abad, Fernando Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach |
title | Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach |
title_full | Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach |
title_fullStr | Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach |
title_full_unstemmed | Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach |
title_short | Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach |
title_sort | identification of factors associated with school effectiveness with data mining techniques: testing a new approach |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873351/ https://www.ncbi.nlm.nih.gov/pubmed/31803116 http://dx.doi.org/10.3389/fpsyg.2019.02583 |
work_keys_str_mv | AT martinezabadfernando identificationoffactorsassociatedwithschooleffectivenesswithdataminingtechniquestestinganewapproach |