FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation

Detalhes bibliográficos
Autor(a) principal: Maskeliūnas, Rytis
Data de Publicação: 2023
Outros Autores: Damaševičius, Robertas, Blažauskas, Tomas, Swacha, Jakub, Queirós, Ricardo, Paiva, José Carlos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/24433
Resumo: This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners’ ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51).
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spelling FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluationFGPEPareto optimizationGamified programmingPersonalized learningAdaptive learningProgressive Web Applications (PWAs)Mobile learningThis paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners’ ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51).MDPIRepositório Científico do Instituto Politécnico do PortoMaskeliūnas, RytisDamaševičius, RobertasBlažauskas, TomasSwacha, JakubQueirós, RicardoPaiva, José Carlos2024-01-10T12:51:27Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/24433eng2073-431X10.3390/computers12070144info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-17T01:47:52Zoai:recipp.ipp.pt:10400.22/24433Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:44:54.258734Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
title FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
spellingShingle FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
Maskeliūnas, Rytis
FGPE
Pareto optimization
Gamified programming
Personalized learning
Adaptive learning
Progressive Web Applications (PWAs)
Mobile learning
title_short FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
title_full FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
title_fullStr FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
title_full_unstemmed FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
title_sort FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
author Maskeliūnas, Rytis
author_facet Maskeliūnas, Rytis
Damaševičius, Robertas
Blažauskas, Tomas
Swacha, Jakub
Queirós, Ricardo
Paiva, José Carlos
author_role author
author2 Damaševičius, Robertas
Blažauskas, Tomas
Swacha, Jakub
Queirós, Ricardo
Paiva, José Carlos
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Maskeliūnas, Rytis
Damaševičius, Robertas
Blažauskas, Tomas
Swacha, Jakub
Queirós, Ricardo
Paiva, José Carlos
dc.subject.por.fl_str_mv FGPE
Pareto optimization
Gamified programming
Personalized learning
Adaptive learning
Progressive Web Applications (PWAs)
Mobile learning
topic FGPE
Pareto optimization
Gamified programming
Personalized learning
Adaptive learning
Progressive Web Applications (PWAs)
Mobile learning
description This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners’ ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51).
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01T00:00:00Z
2024-01-10T12:51:27Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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url http://hdl.handle.net/10400.22/24433
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2073-431X
10.3390/computers12070144
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dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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