Technology and abstraction: complex skills development through video games

Detalhes bibliográficos
Autor(a) principal: Figueroa Vargas, Andrea del Carmen
Data de Publicação: 2021
Outros Autores: Aravena Gaete, Margarita Ercilia, Campos Soto, María Natalia, Ruete Zuñiga , David
Tipo de documento: Artigo
Idioma: spa
Título da fonte: Texto livre
Texto Completo: https://periodicos.ufmg.br/index.php/textolivre/article/view/33575
Resumo: This study aims to propose supervised machine learning models to predict the abstraction ability in students as an early warning mechanism through both the technology use and the video game use. The methodology used was mixed with a prescriptive and predictive design; 118 tests were carried out by Chilean pedagogy students. For analysis, several variables were correlated: age, modality, academic semester and six predictive models. The results show three relevant findings. First, regarding the relation abstraction/ age, in the Satisfactory and No abstraction, the distribution is homogeneous in every age. Second, the relationship abstraction/modality shows a 50% pattern for all the abstraction categories. Third, the relation abstraction/academic semester shows that most students from the seventh semester have no capacity for abstraction. The study concludes the abstraction level is low, showing that 61,1% of the students do not have a higher cognitive level. Two out of six of the supervised machine learning models are suggested to predict early warning, decision tree and random forest since they have a 100% accuracy. Therefore, through the use of technology and the video game it is possible to ensure higher cognitive level development, by the use of several planned strategies in covid-19 times concentrated on the first two tears of pedagogy training.
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spelling Technology and abstraction: complex skills development through video gamesTecnología y Abstracción: desarrollo de habilidades complejas a través de video juegosTecnologia e abstração: desenvolvimento de habilidades complexas por meio de videogamesPensamento superiorHabilidades cognitivasAbstraçãoVideogamesModelos projetivosTecnologiaPensamiento superiorHabilidades cognitivasAbstracciónVideo juegosModelos proyectivosTecnologíaHigher thinkingCognitive skillAbstractionPlay gamesProjective modelsTechnologyThis study aims to propose supervised machine learning models to predict the abstraction ability in students as an early warning mechanism through both the technology use and the video game use. The methodology used was mixed with a prescriptive and predictive design; 118 tests were carried out by Chilean pedagogy students. For analysis, several variables were correlated: age, modality, academic semester and six predictive models. The results show three relevant findings. First, regarding the relation abstraction/ age, in the Satisfactory and No abstraction, the distribution is homogeneous in every age. Second, the relationship abstraction/modality shows a 50% pattern for all the abstraction categories. Third, the relation abstraction/academic semester shows that most students from the seventh semester have no capacity for abstraction. The study concludes the abstraction level is low, showing that 61,1% of the students do not have a higher cognitive level. Two out of six of the supervised machine learning models are suggested to predict early warning, decision tree and random forest since they have a 100% accuracy. Therefore, through the use of technology and the video game it is possible to ensure higher cognitive level development, by the use of several planned strategies in covid-19 times concentrated on the first two tears of pedagogy training.El objetivo de este estudio es proponer modelos de aprendizaje de máquina supervisada para predecir la capacidad de abstracción en los estudiantes, como mecanismo de alerta temprana, a través de la utilización de la tecnología y el uso del video juego. La metodología utilizada es mixta, con un diseño descriptivo y predictivo, se analizaron 118 tests aplicados a estudiantes chilenos de formación docente. Para ello, se correlacionaron las variables: edad, modalidad y semestre académico y se examinaron seis modelos predictivos. Los resultados evidencian tres hallazgos relevantes. En primer lugar, en la relación abstracción/edad, en las categorías Satisfactoria y No Abstracción, la distribución es homogénea en todas las edades. En segundo lugar, la relación abstracción/modalidad se evidencia que sigue un patrón del 50% para todas las categorías de abstracción. En tercer lugar, la relación abstracción/semestre académico, se observa que en el séptimo semestre se concentra la mayor cantidad de estudiantes sin capacidad de abstracción. Se concluye que el nivel de abstracción es bajo, evidenciándose que el 61,1% de los estudiantes no tiene un nivel cognitivo superior. De los 6 modelos de aprendizaje de máquina supervisada, dos se sugieren para predecir una alerta temprana, árbol de decisión y bosque aleatorio que tienen una exactitud (Accuracy) del 100%. Por lo tanto, mediante el uso de la tecnología y el video juego es posible afianzar el desarrollo de habilidades cognitivas superiores, por medio de una serie de estrategias planificadas en tiempos de covid-19, concentradas en el primer y segundo semestre de formación.O objetivo deste estudo é propor modelos de aprendizado de máquina supervisionado para predizer a capacidade de abstração em alunos, como mecanismo de alerta precoce, por meio do uso de tecnologia e de videogames. A metodologia utilizada é mista, com desenho descritivo e preditivo, analisando 118 testes aplicados a alunos chilenos de formação de professores. Para isso, as variáveis: idade, modalidade e semestre letivo foram correlacionadas e seis modelos preditivos foram examinados. Os resultados mostram três achados relevantes. Primeiro, na relação abstração/idade, nas categorias Satisfatório e Não Abstração, a distribuição é homogênea em todas as idades. Em segundo lugar, a relação abstração/modalidade evidencia que segue um padrão de 50% para todas as categorias de abstração. Terceiro, na relação abstração/semestre letivo, observa-se que no sétimo semestre concentra-se o maior número de alunos sem capacidade de abstração. Conclui-se que o nível de abstração é baixo, mostrando que 61,1% dos alunos não possuem um nível cognitivo superior. Dos 6 modelos de aprendizado de máquina supervisionados, dois são sugeridos para prever o aviso prévio, a árvore de decisão e a floresta aleatória com 100% de precisão (Precisão). Portanto, por meio do uso de tecnologia e videogames, é possível fortalecer o desenvolvimento de habilidades cognitivas superiores, por meio de uma série de estratégias planejadas em tempos de COVID-19, concentradas no primeiro e no segundo semestres de treinamento.Universidade Federal de Minas Gerais2021-05-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufmg.br/index.php/textolivre/article/view/3357510.35699/1983-3652.2021.33575Texto Livre; Vol. 14 No. 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e33575Texto Livre; Vol. 14 Núm. 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e33575Texto Livre; Vol. 14 No 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e33575Texto Livre; v. 14 n. 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e335751983-3652reponame:Texto livreinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGspahttps://periodicos.ufmg.br/index.php/textolivre/article/view/33575/27048Copyright (c) 2021 Texto Livre: Linguagem e Tecnologiahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessFigueroa Vargas, Andrea del Carmen Aravena Gaete, Margarita Ercilia Campos Soto, María Natalia Ruete Zuñiga , David 2022-03-24T10:28:59Zoai:periodicos.ufmg.br:article/33575Revistahttp://www.periodicos.letras.ufmg.br/index.php/textolivrePUBhttps://periodicos.ufmg.br/index.php/textolivre/oairevistatextolivre@letras.ufmg.br1983-36521983-3652opendoar:2022-03-24T10:28:59Texto livre - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Technology and abstraction: complex skills development through video games
Tecnología y Abstracción: desarrollo de habilidades complejas a través de video juegos
Tecnologia e abstração: desenvolvimento de habilidades complexas por meio de videogames
title Technology and abstraction: complex skills development through video games
spellingShingle Technology and abstraction: complex skills development through video games
Figueroa Vargas, Andrea del Carmen
Pensamento superior
Habilidades cognitivas
Abstração
Videogames
Modelos projetivos
Tecnologia
Pensamiento superior
Habilidades cognitivas
Abstracción
Video juegos
Modelos proyectivos
Tecnología
Higher thinking
Cognitive skill
Abstraction
Play games
Projective models
Technology
title_short Technology and abstraction: complex skills development through video games
title_full Technology and abstraction: complex skills development through video games
title_fullStr Technology and abstraction: complex skills development through video games
title_full_unstemmed Technology and abstraction: complex skills development through video games
title_sort Technology and abstraction: complex skills development through video games
author Figueroa Vargas, Andrea del Carmen
author_facet Figueroa Vargas, Andrea del Carmen
Aravena Gaete, Margarita Ercilia
Campos Soto, María Natalia
Ruete Zuñiga , David
author_role author
author2 Aravena Gaete, Margarita Ercilia
Campos Soto, María Natalia
Ruete Zuñiga , David
author2_role author
author
author
dc.contributor.author.fl_str_mv Figueroa Vargas, Andrea del Carmen
Aravena Gaete, Margarita Ercilia
Campos Soto, María Natalia
Ruete Zuñiga , David
dc.subject.por.fl_str_mv Pensamento superior
Habilidades cognitivas
Abstração
Videogames
Modelos projetivos
Tecnologia
Pensamiento superior
Habilidades cognitivas
Abstracción
Video juegos
Modelos proyectivos
Tecnología
Higher thinking
Cognitive skill
Abstraction
Play games
Projective models
Technology
topic Pensamento superior
Habilidades cognitivas
Abstração
Videogames
Modelos projetivos
Tecnologia
Pensamiento superior
Habilidades cognitivas
Abstracción
Video juegos
Modelos proyectivos
Tecnología
Higher thinking
Cognitive skill
Abstraction
Play games
Projective models
Technology
description This study aims to propose supervised machine learning models to predict the abstraction ability in students as an early warning mechanism through both the technology use and the video game use. The methodology used was mixed with a prescriptive and predictive design; 118 tests were carried out by Chilean pedagogy students. For analysis, several variables were correlated: age, modality, academic semester and six predictive models. The results show three relevant findings. First, regarding the relation abstraction/ age, in the Satisfactory and No abstraction, the distribution is homogeneous in every age. Second, the relationship abstraction/modality shows a 50% pattern for all the abstraction categories. Third, the relation abstraction/academic semester shows that most students from the seventh semester have no capacity for abstraction. The study concludes the abstraction level is low, showing that 61,1% of the students do not have a higher cognitive level. Two out of six of the supervised machine learning models are suggested to predict early warning, decision tree and random forest since they have a 100% accuracy. Therefore, through the use of technology and the video game it is possible to ensure higher cognitive level development, by the use of several planned strategies in covid-19 times concentrated on the first two tears of pedagogy training.
publishDate 2021
dc.date.none.fl_str_mv 2021-05-18
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufmg.br/index.php/textolivre/article/view/33575
10.35699/1983-3652.2021.33575
url https://periodicos.ufmg.br/index.php/textolivre/article/view/33575
identifier_str_mv 10.35699/1983-3652.2021.33575
dc.language.iso.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://periodicos.ufmg.br/index.php/textolivre/article/view/33575/27048
dc.rights.driver.fl_str_mv Copyright (c) 2021 Texto Livre: Linguagem e Tecnologia
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Texto Livre: Linguagem e Tecnologia
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv Texto Livre; Vol. 14 No. 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e33575
Texto Livre; Vol. 14 Núm. 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e33575
Texto Livre; Vol. 14 No 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e33575
Texto Livre; v. 14 n. 2 (2021): Tecnología educativa para la agenda 2030: Objetivos de Desarrollo Sostenible (ODS) ante la pandemia; e33575
1983-3652
reponame:Texto livre
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Texto livre
collection Texto livre
repository.name.fl_str_mv Texto livre - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv revistatextolivre@letras.ufmg.br
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