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Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students

INTRODUCTION: There is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to an...

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Autores principales: de Vasconcelos, Angelina Nunes, Freires, Leogildo Alves, Loureto, Gleidson Diego Lopes, Fortes, Gabriel, da Costa, Júlio Cezar Albuquerque, Torres, Luan Filipy Freire, Bittencourt, Ig Ibert, Cordeiro, Thiago Damasceno, Isotani, Seiji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425558/
https://www.ncbi.nlm.nih.gov/pubmed/37588241
http://dx.doi.org/10.3389/fpsyg.2023.1189283
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author de Vasconcelos, Angelina Nunes
Freires, Leogildo Alves
Loureto, Gleidson Diego Lopes
Fortes, Gabriel
da Costa, Júlio Cezar Albuquerque
Torres, Luan Filipy Freire
Bittencourt, Ig Ibert
Cordeiro, Thiago Damasceno
Isotani, Seiji
author_facet de Vasconcelos, Angelina Nunes
Freires, Leogildo Alves
Loureto, Gleidson Diego Lopes
Fortes, Gabriel
da Costa, Júlio Cezar Albuquerque
Torres, Luan Filipy Freire
Bittencourt, Ig Ibert
Cordeiro, Thiago Damasceno
Isotani, Seiji
author_sort de Vasconcelos, Angelina Nunes
collection PubMed
description INTRODUCTION: There is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context. METHODS: We performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924). RESULTS: After the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student–Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12). DISCUSSION: We discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon.
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spelling pubmed-104255582023-08-16 Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students de Vasconcelos, Angelina Nunes Freires, Leogildo Alves Loureto, Gleidson Diego Lopes Fortes, Gabriel da Costa, Júlio Cezar Albuquerque Torres, Luan Filipy Freire Bittencourt, Ig Ibert Cordeiro, Thiago Damasceno Isotani, Seiji Front Psychol Psychology INTRODUCTION: There is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context. METHODS: We performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924). RESULTS: After the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student–Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12). DISCUSSION: We discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon. Frontiers Media S.A. 2023-07-31 /pmc/articles/PMC10425558/ /pubmed/37588241 http://dx.doi.org/10.3389/fpsyg.2023.1189283 Text en Copyright © 2023 Vasconcelos, Freires, Loureto, Fortes, Costa, Torres, Bittencourt, Cordeiro and Isotani. https://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
de Vasconcelos, Angelina Nunes
Freires, Leogildo Alves
Loureto, Gleidson Diego Lopes
Fortes, Gabriel
da Costa, Júlio Cezar Albuquerque
Torres, Luan Filipy Freire
Bittencourt, Ig Ibert
Cordeiro, Thiago Damasceno
Isotani, Seiji
Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students
title Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students
title_full Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students
title_fullStr Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students
title_full_unstemmed Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students
title_short Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students
title_sort advancing school dropout early warning systems: the iafree relational model for identifying at-risk students
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425558/
https://www.ncbi.nlm.nih.gov/pubmed/37588241
http://dx.doi.org/10.3389/fpsyg.2023.1189283
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