Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | https://tede.unioeste.br/handle/tede/6833 |
Resumo: | The technical education of professionals in the field of computer science, especially with regard to teaching algorithms and programming, faces significant challenges, such as students’ lack of motivation, their unfamiliarity with the relevant content, their inability to understand abstractions, the use of inappropriate materials, and more. To face these challenges, emphasis was placed on a theoretically based teaching sequence with the application of specific methods and techniques and the implementation of the resulting analyses. A didactic sequence, called Module I, was elaborated based on the Meaningful Learning Theory (MLT), learning based on digital games, considering Bloom’s Taxonomy and the references developed by the Brazilian Computer Society (SBC) for computer education, in accordance with the National Curriculum Guidelines (DCN). Module I included initial concepts such as variables, data types, data input and output, logical and relational operations, selection and repetition structures. Among the didactic materials developed and used, the most important is a Learning Environment Online based on digital games called Gaya - In Search of Redemption. Module I was applied in the context of a case study conducted with computer science students enrolled in Algorithms (n = 17) at a public university in 2020, the majority of whom (n = 14) had previously failed in this subject. Quantitative data were collected in the form of tests, assignments, and the performance of stundents on Gaya games, as well as qualitative data obtained through questionnaires, semi-structured interviews, and observations of classroom activities. Data analysis showed that Gaya generally exerted a positive influence, which respondents attributed to its interactivity, content rehearsal, ease of viewing, and greater fun factor. These results were confirmed by data collected in a semi-structured interview with the professor of the subject and with two professors who have already taught algorithms. Regarding the learning potential of Gaya, students scored 81 on the first assessment and 71 on the last assessment (0 to 10 scale), indicating a high learning potential. The Cronbach’s alpha of the survey instruments was 0.79 and 0.77, respectively, indicating good internal consistency. A high correlation was found between the Module I grade point average and the final subject average, whose linear Pearson correlation was 0.88; a correlation coefficient of 0.81 was found between the test scores and the final subject averages, and a correlation coefficient of 0.89 was found between the scores of Tests 1 and 2, leading to the conclusion that student performance remained very similar. It was possible to show some evidence of Meaningful Learning and relate it to aspects of Bloom’s Taxonomy from the summative assessment. Students’ responses indicated that they acquired knowledge whether it was covered in class, on the homework, or through Gaya. However, Gaya was found to address the three main features of MLT, i.e., it takes into account students’ prior knowledge, presents potentially significant material, and stimulates learning, marking it as a relevant pedagogical tool in the context of teaching. and initial learning of algorithms. The observations of the professor of the discipline and the qualitative and quantitative analysis of the activities performed by the students, as well as the statistical similarity of the grades of these students in the following discipline (Data Structure) compared to the rest of the class, are also evidence of Meaningful Learning. However, a reverse effect was noticeable in the use of the Online Environment with some students with more knowledge in Algorithms, indicating necessary improvements in the mechanism that automatically makes new phases available. |
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Rizzi, Claudia Brandelerohttp://lattes.cnpq.br/2203704515345173Santa Catarina, Adairhttp://lattes.cnpq.br/7041836941307184Berssanette, João Henriquehttp://lattes.cnpq.br/4957636385989608Rizzi, Rogério Luishttp://lattes.cnpq.br/658292405336429http://lattes.cnpq.br/3657386675052708Karling, Daniel Antonio2023-09-26T15:09:07Z2022-09-28KARLING, Daniel Antonio. Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos. 2022. 212 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Estadual do Oeste do Paraná, Cascavel.https://tede.unioeste.br/handle/tede/6833The technical education of professionals in the field of computer science, especially with regard to teaching algorithms and programming, faces significant challenges, such as students’ lack of motivation, their unfamiliarity with the relevant content, their inability to understand abstractions, the use of inappropriate materials, and more. To face these challenges, emphasis was placed on a theoretically based teaching sequence with the application of specific methods and techniques and the implementation of the resulting analyses. A didactic sequence, called Module I, was elaborated based on the Meaningful Learning Theory (MLT), learning based on digital games, considering Bloom’s Taxonomy and the references developed by the Brazilian Computer Society (SBC) for computer education, in accordance with the National Curriculum Guidelines (DCN). Module I included initial concepts such as variables, data types, data input and output, logical and relational operations, selection and repetition structures. Among the didactic materials developed and used, the most important is a Learning Environment Online based on digital games called Gaya - In Search of Redemption. Module I was applied in the context of a case study conducted with computer science students enrolled in Algorithms (n = 17) at a public university in 2020, the majority of whom (n = 14) had previously failed in this subject. Quantitative data were collected in the form of tests, assignments, and the performance of stundents on Gaya games, as well as qualitative data obtained through questionnaires, semi-structured interviews, and observations of classroom activities. Data analysis showed that Gaya generally exerted a positive influence, which respondents attributed to its interactivity, content rehearsal, ease of viewing, and greater fun factor. These results were confirmed by data collected in a semi-structured interview with the professor of the subject and with two professors who have already taught algorithms. Regarding the learning potential of Gaya, students scored 81 on the first assessment and 71 on the last assessment (0 to 10 scale), indicating a high learning potential. The Cronbach’s alpha of the survey instruments was 0.79 and 0.77, respectively, indicating good internal consistency. A high correlation was found between the Module I grade point average and the final subject average, whose linear Pearson correlation was 0.88; a correlation coefficient of 0.81 was found between the test scores and the final subject averages, and a correlation coefficient of 0.89 was found between the scores of Tests 1 and 2, leading to the conclusion that student performance remained very similar. It was possible to show some evidence of Meaningful Learning and relate it to aspects of Bloom’s Taxonomy from the summative assessment. Students’ responses indicated that they acquired knowledge whether it was covered in class, on the homework, or through Gaya. However, Gaya was found to address the three main features of MLT, i.e., it takes into account students’ prior knowledge, presents potentially significant material, and stimulates learning, marking it as a relevant pedagogical tool in the context of teaching. and initial learning of algorithms. The observations of the professor of the discipline and the qualitative and quantitative analysis of the activities performed by the students, as well as the statistical similarity of the grades of these students in the following discipline (Data Structure) compared to the rest of the class, are also evidence of Meaningful Learning. However, a reverse effect was noticeable in the use of the Online Environment with some students with more knowledge in Algorithms, indicating necessary improvements in the mechanism that automatically makes new phases available.A formação técnica de profissionais na área da Computação, principalmente no que tange ao ensino de Algoritmos e Programação, enfrenta desafios importantes, como a falta de motivação dos estudantes, sua pouca familiaridade com conteúdos relacionados, inabilidade com abstrações, uso de materiais inapropriados, entre outros. O enfrentamento a esses de safios tem focado na ação docente fundamentada teórica e metodologicamente na aplicação de métodos e técnicas específicas bem como na realização de análises decorrentes, per curso realizado durante o desenvolvimento da presente pesquisa. Uma Sequência Didática, denominada Módulo I, foi elaborada com base na Teoria da Aprendizagem Significativa (TAS), na Aprendizagem Baseada em Jogos Digitais considerando a Taxionomia de Bloom, e nos Referenciais de Formação em Computação desenvolvidos pela Sociedade Brasileira de Computação (SBC) em consonância com as Diretrizes Curriculares Nacionais (DCN). O Módulo I abrangeu conceitos iniciais incluindo variáveis, tipos de dados, entrada e saída de dados, operações lógicas e relacionais, estruturas de seleção e de repetição. Dentre o conjunto de materiais didáticos elaborados e utilizados, o principal deles constitui um Ambiente Online de aprendizagem baseado em jogos digitais, nomeado Gaya - Em Busca da Redenção, que foi especificado, desenvolvido e testado. A aplicação do Módulo I se deu por meio de um estudo de caso realizado com estudantes de graduação em Ciência da Computação matriculados em 2020 na disciplina de Algoritmos (n = 17) em uma universidade pública, sendo que a maioria (n = 14) deles já havia reprovado anteriormente nessa disciplina. Dados quantitativos referentes a provas, trabalhos, execução dos jogos de Gaya foram coletados, assim como dados qualitativos obtidos por meio de questio nários, entrevistas semiestruturadas e observações de atividades realizadas em aulas. A análise dos dados apontou que, em geral, Gaya exerceu influência positiva, justificada pelos respondentes pela interatividade, prática dos conteúdos, fácil visualização e por proporcionar maior diversão. Esses resultados foram corroborados por dados coletados em entrevista semiestruturada com a docente da disciplina e com dois professores que já ministraram Algoritmos. Com relação ao potencial de Gaya em propiciar aprendizagem, na opinião dos estudantes, obtiveram-se as notas 81 na avaliação inicial e 71 na avaliação final (escala de 0 a 10), indicando alto potencial de aprendizagem, sendo que o Alfa de Cronbach dos instrumentos de coleta foram 0,79 e 0,77, respectivamente, indicando boa consistência interna. Foi observada alta correlação de Pearson entre a média das notas do Módulo I e a média final da disciplina:0,88; obtiveram-se os coeficientes de correlação de 0,81 entre as notas da prova e as médias finais da disciplina e de 0,89 entre as notas das provas 1 e 2, o que leva a concluir que o desempenho dos estudantes manteve-se bastante similar – e elevado – no decorrer da disciplina de Algoritmos. Foi possível apontar algumas evidências de Aprendizagem Significativa e relacioná-las a aspectos da Taxionomia de Bloom a partir da avaliação somativa. As respostas e justificativas dadas pelos estudantes indicaram que eles se apropriaram dos conhecimentos, quer tenham sido abordados em sala, trabalhos ou por Gaya. Identificou-se que Gaya contempla as três grandes características da TAS, ou seja, considera conhecimentos prévios dos estudantes, constitui um material potencialmente significativo e estimula a disposição a aprender, o que o caracteriza como um instrumento pedagógico relevante no escopo do ensino e da aprendizagem inicial de Algoritmos. Evidências de Aprendizagem Significativa também foram encontradas nas observações da docente da disciplina e nas análises quali e quantitativa das atividades realizadas pelos estudantes, além da semelhança estatística das notas desses alunos na disciplina seguinte (Estrutura de Dados) ao compará-las com as do restante da turma. Entretanto, foi perceptível um efeito reverso no uso do Ambiente Online com alguns estudantes com mais conhecimentos em Algoritmos, indicando melhorias necessárias no mecanismo que disponibiliza novas fases automaticamente.Submitted by Rosangela Silva (rosangela.silva3@unioeste.br) on 2023-09-26T15:09:07Z No. of bitstreams: 2 Daniel Antonio Karling.pdf: 7815689 bytes, checksum: 581c6e1d0eb295eab902bc660b886d65 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2023-09-26T15:09:07Z (GMT). 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dc.title.por.fl_str_mv |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos |
dc.title.alternative.eng.fl_str_mv |
Development and Evaluation of a Online Environment Based on Digital Games for Meaningful Learning of Algorithms. |
title |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos |
spellingShingle |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos Karling, Daniel Antonio Ensino Aprendizagem Algoritmos Aprendizagem Significativa Jogos digitais Teaching Learning Algorithms Digital games Meaningful Learning CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos |
title_full |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos |
title_fullStr |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos |
title_full_unstemmed |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos |
title_sort |
Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos |
author |
Karling, Daniel Antonio |
author_facet |
Karling, Daniel Antonio |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Rizzi, Claudia Brandelero |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2203704515345173 |
dc.contributor.referee1.fl_str_mv |
Santa Catarina, Adair |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/7041836941307184 |
dc.contributor.referee2.fl_str_mv |
Berssanette, João Henrique |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/4957636385989608 |
dc.contributor.referee3.fl_str_mv |
Rizzi, Rogério Luis |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/658292405336429 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3657386675052708 |
dc.contributor.author.fl_str_mv |
Karling, Daniel Antonio |
contributor_str_mv |
Rizzi, Claudia Brandelero Santa Catarina, Adair Berssanette, João Henrique Rizzi, Rogério Luis |
dc.subject.por.fl_str_mv |
Ensino Aprendizagem Algoritmos Aprendizagem Significativa Jogos digitais |
topic |
Ensino Aprendizagem Algoritmos Aprendizagem Significativa Jogos digitais Teaching Learning Algorithms Digital games Meaningful Learning CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Teaching Learning Algorithms Digital games Meaningful Learning |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
The technical education of professionals in the field of computer science, especially with regard to teaching algorithms and programming, faces significant challenges, such as students’ lack of motivation, their unfamiliarity with the relevant content, their inability to understand abstractions, the use of inappropriate materials, and more. To face these challenges, emphasis was placed on a theoretically based teaching sequence with the application of specific methods and techniques and the implementation of the resulting analyses. A didactic sequence, called Module I, was elaborated based on the Meaningful Learning Theory (MLT), learning based on digital games, considering Bloom’s Taxonomy and the references developed by the Brazilian Computer Society (SBC) for computer education, in accordance with the National Curriculum Guidelines (DCN). Module I included initial concepts such as variables, data types, data input and output, logical and relational operations, selection and repetition structures. Among the didactic materials developed and used, the most important is a Learning Environment Online based on digital games called Gaya - In Search of Redemption. Module I was applied in the context of a case study conducted with computer science students enrolled in Algorithms (n = 17) at a public university in 2020, the majority of whom (n = 14) had previously failed in this subject. Quantitative data were collected in the form of tests, assignments, and the performance of stundents on Gaya games, as well as qualitative data obtained through questionnaires, semi-structured interviews, and observations of classroom activities. Data analysis showed that Gaya generally exerted a positive influence, which respondents attributed to its interactivity, content rehearsal, ease of viewing, and greater fun factor. These results were confirmed by data collected in a semi-structured interview with the professor of the subject and with two professors who have already taught algorithms. Regarding the learning potential of Gaya, students scored 81 on the first assessment and 71 on the last assessment (0 to 10 scale), indicating a high learning potential. The Cronbach’s alpha of the survey instruments was 0.79 and 0.77, respectively, indicating good internal consistency. A high correlation was found between the Module I grade point average and the final subject average, whose linear Pearson correlation was 0.88; a correlation coefficient of 0.81 was found between the test scores and the final subject averages, and a correlation coefficient of 0.89 was found between the scores of Tests 1 and 2, leading to the conclusion that student performance remained very similar. It was possible to show some evidence of Meaningful Learning and relate it to aspects of Bloom’s Taxonomy from the summative assessment. Students’ responses indicated that they acquired knowledge whether it was covered in class, on the homework, or through Gaya. However, Gaya was found to address the three main features of MLT, i.e., it takes into account students’ prior knowledge, presents potentially significant material, and stimulates learning, marking it as a relevant pedagogical tool in the context of teaching. and initial learning of algorithms. The observations of the professor of the discipline and the qualitative and quantitative analysis of the activities performed by the students, as well as the statistical similarity of the grades of these students in the following discipline (Data Structure) compared to the rest of the class, are also evidence of Meaningful Learning. However, a reverse effect was noticeable in the use of the Online Environment with some students with more knowledge in Algorithms, indicating necessary improvements in the mechanism that automatically makes new phases available. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022-09-28 |
dc.date.accessioned.fl_str_mv |
2023-09-26T15:09:07Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
KARLING, Daniel Antonio. Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos. 2022. 212 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Estadual do Oeste do Paraná, Cascavel. |
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https://tede.unioeste.br/handle/tede/6833 |
identifier_str_mv |
KARLING, Daniel Antonio. Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos. 2022. 212 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Estadual do Oeste do Paraná, Cascavel. |
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https://tede.unioeste.br/handle/tede/6833 |
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1974996533081274470 |
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600 600 600 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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application/pdf |
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Universidade Estadual do Oeste do Paraná Cascavel |
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Programa de Pós-Graduação em Ciência da Computação |
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UNIOESTE |
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Brasil |
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Centro de Ciências Exatas e Tecnológicas |
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Universidade Estadual do Oeste do Paraná Cascavel |
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