Análise de Modelos Mentais e Equívocos no Aprendizado de Funções de Ordem Superior: Um Relato de Experiência com Simulação Visual
Resumo
A demanda por programação por não desenvolvedores, especialmente em tarefas relacionadas a dados, aumentou consideravelmente nos últimos tempos. As Funções de Ordem Superior (FOS) surgiram como ferramentas populares nesse contexto, mas os aprendizes ainda enfrentam desafios na compreensão desses constructos. Este trabalho apresenta um relato de experiência sobre o uso assíncrono de uma ferramenta de simulação visual adaptada para o ensino de FOS. Diante de um cenário de aprendizado autônomo e assíncrono, o estudo adotou uma abordagem mista para mapear como os aprendizes interagem com as visualizações e constroem seus modelos mentais. Embora os testes quantitativos não tenham apontado disparidades significativas de desempenho geral entre os principais grupos analisados, a análise detalhada das respostas permitiu identificar e categorizar equívocos conceituais comuns e modelos mentais conflitantes adotados pelos estudantes. Os resultados revelam padrões específicos de erro atrelados ao tipo de suporte visual utilizado, oferecendo subsídios práticos para o design de materiais pedagógicos e ferramentas voltadas ao ensino de programação funcional na trilha de Computação na Educação.
Palavras-chave:
Funções de Ordem Superior, Modelos Mentais, Visualização de Programas
Referências
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de Koning, B. B., Tabbers, H. K., Rikers, R. M., and Paas, F. (2010). Attention guidance in learning from a complex animation: Seeing is understanding? Learning and Instruction, 20(2):111–122. Eye tracking as a tool to study and enhance multimedia learning.
Duran, R., Rybicki, J.-M., Sorva, J., and Hellas, A. (2019). Exploring the value of student self-evaluation in introductory programming. In Proceedings of the 2019 ACM Conference on International Computing Education Research, ICER '19, page 121–130, New York, NY, USA. Association for Computing Machinery.
Duran, R., Sorva, J., and Leite, S. (2018). Towards an analysis of program complexity from a cognitive perspective. In Proceedings of the 2018 ACM Conference on International Computing Education Research, ICER '18, page 21–30, New York, NY, USA. Association for Computing Machinery.
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Duran, R., Zavgorodniaia, A., and Sorva, J. (2022). Cognitive load theory in computing education research: A review. ACM Trans. Comput. Educ., 22(4).
Eliot, Sutinen, E., Tarhio, J., Lahtinen, S.-p., Tuovinen, A.-P., Rautama, E., and Meisalo, V. (1997). Eliot - an algorithm animation environment.
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Kong, H.-K., Liu, Z., and Karahalios, K. (2017). Internal and external visual cue preferences for visualizations in presentations. Comput. Graph. Forum, 36(3):515–525.
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Levy, R., Ben-Ari, M., and Uronen, P. (2003). The jeliot 2000 program animation system. Computers & Education, 40:1–15.
Martins, M. R. and Duran, R. (2023). Ferramentas de visualização de programas na compreensão de funções de alta-ordem. In Anais Estendidos do III Simpósio Brasileiro de Educação em Computação, pages 18–19. SBC.
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Mutlu-Bayraktar, D., Cosgun, V., and Altan, T. (2019). Cognitive load in multimedia learning environments: A systematic review. Computers & Education, 141:103618.
Naps, T. L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., Korhonen, A., Malmi, L., McNally, M., Rodger, S., and Velázquez-Iturbide, J. A. (2002). Exploring the role of visualization and engagement in computer science education. SIGCSE Bull., 35(2):131–152.
Renkl, A. and Scheiter, K. (2017). Studying visual displays: How to instructionally support learning. Educational Psychology Review, 29.
Rivera, E. and Krishnamurthi, S. (2022). Structural versus pipeline composition of higher-order functions (experience report). Proc. ACM Program. Lang., 6(ICFP).
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Scaffidi, C., Shaw, M., and Myers, B. (2005). An approach for categorizing end user programmers to guide software engineering research. In Proceedings of the First Workshop on End-User Software Engineering, WEUSE I, page 1–5, New York, NY, USA. Association for Computing Machinery.
Schroeder, N. and Cenkci, A. (2018). Spatial contiguity and spatial split-attention effects in multimedia learning environments: a meta-analysis. Educational Psychology Review, 30:1–23.
Sirkiä, T. (2013). A javascript library for visualizing program execution. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research, Koli Calling '13, page 189–190, New York, NY, USA. Association for Computing Machinery.
Sirkiä, T. et al. (2017). Creating, tailoring, and distributing program animations-supporting the production process of interactive learning content.
Sirkiä, T. and Sorva, J. (2015). Tailoring animations of example programs. In Proceedings of the 15th Koli Calling Conference on Computing Education Research, pages 147–151.
Sirkiä, T. (2016). Jsvee & kelmu: Creating and tailoring program animations for computing education. In 2016 IEEE Working Conference on Software Visualization (VISSOFT), pages 36–45.
Soloway, E. (1986). Learning to program= learning to construct mechanisms and explanations. Communications of the ACM, 29(9):850–858.
Sorva, J. et al. (2012). Visual program simulation in introductory programming education. Aalto University.
Sorva, J. and Sirkiä, T. (2010). Uuhistle: a software tool for visual program simulation. In Koli Calling.
Wang, X., Lin, L., Han, M., and Spector, J. M. (2020). Impacts of cues on learning: Using eye-tracking technologies to examine the functions and designs of added cues in short instructional videos. Computers in Human Behavior, 107:106279.
Boulay, B. D. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1):57–73.
Brodley, C. E., Hescott, B. J., Biron, J., Ressing, A., Peiken, M., Maravetz, S., and Mislove, A. (2022). Broadening participation in computing via ubiquitous combined majors (cs+ x). In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1, pages 544–550.
de Koning, B. B., Tabbers, H. K., Rikers, R. M., and Paas, F. (2010). Attention guidance in learning from a complex animation: Seeing is understanding? Learning and Instruction, 20(2):111–122. Eye tracking as a tool to study and enhance multimedia learning.
Duran, R., Rybicki, J.-M., Sorva, J., and Hellas, A. (2019). Exploring the value of student self-evaluation in introductory programming. In Proceedings of the 2019 ACM Conference on International Computing Education Research, ICER '19, page 121–130, New York, NY, USA. Association for Computing Machinery.
Duran, R., Sorva, J., and Leite, S. (2018). Towards an analysis of program complexity from a cognitive perspective. In Proceedings of the 2018 ACM Conference on International Computing Education Research, ICER '18, page 21–30, New York, NY, USA. Association for Computing Machinery.
Duran, R., Sorva, J., and Seppälä, O. (2021). Rules of program behavior. ACM Trans. Comput. Educ., 21(4).
Duran, R., Zavgorodniaia, A., and Sorva, J. (2022). Cognitive load theory in computing education research: A review. ACM Trans. Comput. Educ., 22(4).
Eliot, Sutinen, E., Tarhio, J., Lahtinen, S.-p., Tuovinen, A.-P., Rautama, E., and Meisalo, V. (1997). Eliot - an algorithm animation environment.
Guo, P. J. (2013). Online python tutor: Embeddable web-based program visualization for cs education. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education, SIGCSE '13, page 579–584, New York, NY, USA. Association for Computing Machinery.
Guzdial, M. and Shreiner, T. (2021). Integrating computing through task-specific programming for disciplinary relevance: Considerations and examples. In Computational Thinking in Education, pages 172–190. Routledge.
Kong, H.-K., Liu, Z., and Karahalios, K. (2017). Internal and external visual cue preferences for visualizations in presentations. Comput. Graph. Forum, 36(3):515–525.
Krishnamurthi, S. and Fisler, K. (2020). Data-centricity: a challenge and opportunity for computing education. Communications of the ACM, 63(8):24–26.
Krishnamurthi, S. and Fisler, K. (2021). Developing behavioral concepts of higher-order functions. In Proceedings of the 17th ACM Conference on International Computing Education Research, ICER 2021, page 306–318, New York, NY, USA. Association for Computing Machinery.
Levy, R., Ben-Ari, M., and Uronen, P. (2003). The jeliot 2000 program animation system. Computers & Education, 40:1–15.
Martins, M. R. and Duran, R. (2023). Ferramentas de visualização de programas na compreensão de funções de alta-ordem. In Anais Estendidos do III Simpósio Brasileiro de Educação em Computação, pages 18–19. SBC.
Mayer, R. E. (1999). Multimedia aids to problem-solving transfer. International Journal of Educational Research, 31(7):611–623.
Muldner, K., Jennings, J., and Chiarelli, V. (2022). A review of worked examples in programming activities. ACM Trans. Comput. Educ., 23(1).
Mutlu-Bayraktar, D., Cosgun, V., and Altan, T. (2019). Cognitive load in multimedia learning environments: A systematic review. Computers & Education, 141:103618.
Naps, T. L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., Korhonen, A., Malmi, L., McNally, M., Rodger, S., and Velázquez-Iturbide, J. A. (2002). Exploring the role of visualization and engagement in computer science education. SIGCSE Bull., 35(2):131–152.
Renkl, A. and Scheiter, K. (2017). Studying visual displays: How to instructionally support learning. Educational Psychology Review, 29.
Rivera, E. and Krishnamurthi, S. (2022). Structural versus pipeline composition of higher-order functions (experience report). Proc. ACM Program. Lang., 6(ICFP).
Rivera, E., Krishnamurthi, S., and Goldstone, R. (2022). Plan composition using higher-order functions. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1, ICER '22, page 84–104, New York, NY, USA. Association for Computing Machinery.
Scaffidi, C., Shaw, M., and Myers, B. (2005). An approach for categorizing end user programmers to guide software engineering research. In Proceedings of the First Workshop on End-User Software Engineering, WEUSE I, page 1–5, New York, NY, USA. Association for Computing Machinery.
Schroeder, N. and Cenkci, A. (2018). Spatial contiguity and spatial split-attention effects in multimedia learning environments: a meta-analysis. Educational Psychology Review, 30:1–23.
Sirkiä, T. (2013). A javascript library for visualizing program execution. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research, Koli Calling '13, page 189–190, New York, NY, USA. Association for Computing Machinery.
Sirkiä, T. et al. (2017). Creating, tailoring, and distributing program animations-supporting the production process of interactive learning content.
Sirkiä, T. and Sorva, J. (2015). Tailoring animations of example programs. In Proceedings of the 15th Koli Calling Conference on Computing Education Research, pages 147–151.
Sirkiä, T. (2016). Jsvee & kelmu: Creating and tailoring program animations for computing education. In 2016 IEEE Working Conference on Software Visualization (VISSOFT), pages 36–45.
Soloway, E. (1986). Learning to program= learning to construct mechanisms and explanations. Communications of the ACM, 29(9):850–858.
Sorva, J. et al. (2012). Visual program simulation in introductory programming education. Aalto University.
Sorva, J. and Sirkiä, T. (2010). Uuhistle: a software tool for visual program simulation. In Koli Calling.
Wang, X., Lin, L., Han, M., and Spector, J. M. (2020). Impacts of cues on learning: Using eye-tracking technologies to examine the functions and designs of added cues in short instructional videos. Computers in Human Behavior, 107:106279.
Publicado
05/10/2026
Como Citar
MARTINS, Marcos Rogério; DURAN, Rodrigo Silva.
Análise de Modelos Mentais e Equívocos no Aprendizado de Funções de Ordem Superior: Um Relato de Experiência com Simulação Visual. In: WORKSHOP DE INFORMÁTICA NA ESCOLA (WIE), 32. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 247-259.
DOI: https://doi.org/10.5753/wie.2026.27810.
