Desenvolvimento e Avaliação de Ambiente Online Baseado em Jogos Digitais para Aprendizagem Significativa de Algoritmos

• Ano de defesa: 2022
• Autor(a) principal: Karling, Daniel Antonio
• Orientador(a): Rizzi, Claudia Brandelero
• Banca de defesa: Santa Catarina, Adair , Berssanette, João Henrique , Rizzi, Rogério Luis
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Paraná; Cascavel
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação
• Departamento: Centro de Ciências Exatas e Tecnológicas
• País: Brasil
• Palavras-chave em Português: Ensino Aprendizagem; Algoritmos; Aprendizagem Significativa; Jogos digitais
• Palavras-chave em Inglês: Teaching Learning; Algorithms; Digital games; Meaningful Learning
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://tede.unioeste.br/handle/tede/6833

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.