Detecting Students' Perception Style by using Games
- Autores
- Amandi, Analia Adriana; Feldman, Juan; Monteserin, Ariel José
- Año de publicación
- 2014
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Knowing students´ learning styles allows us to improve their experience in an educational environment. Particularly, the perception style is one of the most important dimensions of the learning styles since it describes the way students perceive the world as well as the kind of learning content they prefer. Several approaches to detect students´ perception style according to Felder´s model have been proposed. However, these approaches exhibit several limitations that make their implementation difficult. Thus, we propose a novel approach to detect the perception style of a student by analyzing his/her interaction with games, namely puzzle games. To carry out this detection, we track how students play a puzzle game and extract information about this interaction. Then, we train a Naive Bayes Classifier to infer the students´ perception style by using the information extracted. We have evaluated our proposed approach with 47 Computer Engineering students. Experimental results showed that the perception style was successfully predicted through the use of games, with an accuracy of 85%. Finally, we conclude that games are a promising environment where the students´ perception style can be detected.
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Feldman, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina - Materia
-
Digital Games
Learning Styles
Naive Bayes Classifier
Perception Style - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/6773
Ver los metadatos del registro completo
id |
CONICETDig_52dc5747a05f784ff971ece8fba06ee6 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/6773 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
spelling |
Detecting Students' Perception Style by using GamesAmandi, Analia AdrianaFeldman, JuanMonteserin, Ariel JoséDigital GamesLearning StylesNaive Bayes ClassifierPerception Stylehttps://purl.org/becyt/ford/5.3https://purl.org/becyt/ford/5https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Knowing students´ learning styles allows us to improve their experience in an educational environment. Particularly, the perception style is one of the most important dimensions of the learning styles since it describes the way students perceive the world as well as the kind of learning content they prefer. Several approaches to detect students´ perception style according to Felder´s model have been proposed. However, these approaches exhibit several limitations that make their implementation difficult. Thus, we propose a novel approach to detect the perception style of a student by analyzing his/her interaction with games, namely puzzle games. To carry out this detection, we track how students play a puzzle game and extract information about this interaction. Then, we train a Naive Bayes Classifier to infer the students´ perception style by using the information extracted. We have evaluated our proposed approach with 47 Computer Engineering students. Experimental results showed that the perception style was successfully predicted through the use of games, with an accuracy of 85%. Finally, we conclude that games are a promising environment where the students´ perception style can be detected.Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Feldman, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaElsevier2014-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6773Amandi, Analia Adriana; Feldman, Juan; Monteserin, Ariel José; Detecting Students' Perception Style by using Games; Elsevier; Computers and Education; 71; 2-2014; 14-220360-1315enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0360131513002625info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compedu.2013.09.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:08:06Zoai:ri.conicet.gov.ar:11336/6773instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:08:06.701CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Detecting Students' Perception Style by using Games |
title |
Detecting Students' Perception Style by using Games |
spellingShingle |
Detecting Students' Perception Style by using Games Amandi, Analia Adriana Digital Games Learning Styles Naive Bayes Classifier Perception Style |
title_short |
Detecting Students' Perception Style by using Games |
title_full |
Detecting Students' Perception Style by using Games |
title_fullStr |
Detecting Students' Perception Style by using Games |
title_full_unstemmed |
Detecting Students' Perception Style by using Games |
title_sort |
Detecting Students' Perception Style by using Games |
dc.creator.none.fl_str_mv |
Amandi, Analia Adriana Feldman, Juan Monteserin, Ariel José |
author |
Amandi, Analia Adriana |
author_facet |
Amandi, Analia Adriana Feldman, Juan Monteserin, Ariel José |
author_role |
author |
author2 |
Feldman, Juan Monteserin, Ariel José |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Digital Games Learning Styles Naive Bayes Classifier Perception Style |
topic |
Digital Games Learning Styles Naive Bayes Classifier Perception Style |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/5.3 https://purl.org/becyt/ford/5 https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Knowing students´ learning styles allows us to improve their experience in an educational environment. Particularly, the perception style is one of the most important dimensions of the learning styles since it describes the way students perceive the world as well as the kind of learning content they prefer. Several approaches to detect students´ perception style according to Felder´s model have been proposed. However, these approaches exhibit several limitations that make their implementation difficult. Thus, we propose a novel approach to detect the perception style of a student by analyzing his/her interaction with games, namely puzzle games. To carry out this detection, we track how students play a puzzle game and extract information about this interaction. Then, we train a Naive Bayes Classifier to infer the students´ perception style by using the information extracted. We have evaluated our proposed approach with 47 Computer Engineering students. Experimental results showed that the perception style was successfully predicted through the use of games, with an accuracy of 85%. Finally, we conclude that games are a promising environment where the students´ perception style can be detected. Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina Fil: Feldman, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina |
description |
Knowing students´ learning styles allows us to improve their experience in an educational environment. Particularly, the perception style is one of the most important dimensions of the learning styles since it describes the way students perceive the world as well as the kind of learning content they prefer. Several approaches to detect students´ perception style according to Felder´s model have been proposed. However, these approaches exhibit several limitations that make their implementation difficult. Thus, we propose a novel approach to detect the perception style of a student by analyzing his/her interaction with games, namely puzzle games. To carry out this detection, we track how students play a puzzle game and extract information about this interaction. Then, we train a Naive Bayes Classifier to infer the students´ perception style by using the information extracted. We have evaluated our proposed approach with 47 Computer Engineering students. Experimental results showed that the perception style was successfully predicted through the use of games, with an accuracy of 85%. Finally, we conclude that games are a promising environment where the students´ perception style can be detected. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-02 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/6773 Amandi, Analia Adriana; Feldman, Juan; Monteserin, Ariel José; Detecting Students' Perception Style by using Games; Elsevier; Computers and Education; 71; 2-2014; 14-22 0360-1315 |
url |
http://hdl.handle.net/11336/6773 |
identifier_str_mv |
Amandi, Analia Adriana; Feldman, Juan; Monteserin, Ariel José; Detecting Students' Perception Style by using Games; Elsevier; Computers and Education; 71; 2-2014; 14-22 0360-1315 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0360131513002625 info:eu-repo/semantics/altIdentifier/doi/ info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compedu.2013.09.007 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
_version_ |
1842270031581282304 |
score |
13.13397 |