Technology and abstraction: complex skills development through video games
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , |
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. |
id |
UFMG-9_9151d8c63fe1c2fafee3abeeca26e3be |
---|---|
oai_identifier_str |
oai:periodicos.ufmg.br:article/33575 |
network_acronym_str |
UFMG-9 |
network_name_str |
Texto livre |
repository_id_str |
|
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 |
_version_ |
1799711143462174720 |