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Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments
Since its inception, the Actiotope Model of Giftedness (AMG) has provided researchers with a useful model to explain the development of exceptionality. Rather than a focus on the individual, the model postulates that exceptionality is the outcome of a system that includes complex interactions betwee...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381310/ https://www.ncbi.nlm.nih.gov/pubmed/37504771 http://dx.doi.org/10.3390/jintelligence11070128 |
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author | Phillipson, Shane N. Han, Cindy Di Lee, Vincent C. S. |
author_facet | Phillipson, Shane N. Han, Cindy Di Lee, Vincent C. S. |
author_sort | Phillipson, Shane N. |
collection | PubMed |
description | Since its inception, the Actiotope Model of Giftedness (AMG) has provided researchers with a useful model to explain the development of exceptionality. Rather than a focus on the individual, the model postulates that exceptionality is the outcome of a system that includes complex interactions between an individual’s current level of talent and their internal and external environment. To date, however, the statistical techniques that have been used to investigate the model, including linear regression and structural equation modeling, are unable to fully operationalize the systemic nature of these interactions. In order to fully realize the predictive potential and application of the AMG, we outline the use of artificial neural networks (ANNs) to model the complex interactions and suggest that such networks can provide additional insights into the development of exceptionality. In addition to supporting continued research into the AMG, the use of ANNs has the potential to provide educators with evidence-based strategies to support student learning at both an individual and whole-school level. |
format | Online Article Text |
id | pubmed-10381310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103813102023-07-29 Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments Phillipson, Shane N. Han, Cindy Di Lee, Vincent C. S. J Intell Concept Paper Since its inception, the Actiotope Model of Giftedness (AMG) has provided researchers with a useful model to explain the development of exceptionality. Rather than a focus on the individual, the model postulates that exceptionality is the outcome of a system that includes complex interactions between an individual’s current level of talent and their internal and external environment. To date, however, the statistical techniques that have been used to investigate the model, including linear regression and structural equation modeling, are unable to fully operationalize the systemic nature of these interactions. In order to fully realize the predictive potential and application of the AMG, we outline the use of artificial neural networks (ANNs) to model the complex interactions and suggest that such networks can provide additional insights into the development of exceptionality. In addition to supporting continued research into the AMG, the use of ANNs has the potential to provide educators with evidence-based strategies to support student learning at both an individual and whole-school level. MDPI 2023-06-25 /pmc/articles/PMC10381310/ /pubmed/37504771 http://dx.doi.org/10.3390/jintelligence11070128 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Concept Paper Phillipson, Shane N. Han, Cindy Di Lee, Vincent C. S. Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments |
title | Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments |
title_full | Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments |
title_fullStr | Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments |
title_full_unstemmed | Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments |
title_short | Artificial Neural Networks and the Actiotope Model of Giftedness—Clever Solutions from Complex Environments |
title_sort | artificial neural networks and the actiotope model of giftedness—clever solutions from complex environments |
topic | Concept Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381310/ https://www.ncbi.nlm.nih.gov/pubmed/37504771 http://dx.doi.org/10.3390/jintelligence11070128 |
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