Aplicação e Avaliação de um Sistema Adaptativo Baseado em Lógica Fuzzy para o Ensino de Robótica com Arduino
Resumo
Este artigo apresenta a aplicação e a avaliação de um sistema adaptativo baseado em lógica fuzzy para apoiar o ensino de robótica com Arduino. O recurso educacional utiliza três dimensões da aprendizagem — cognitiva, metacognitiva e afetiva — como entradas do mecanismo de inferência, permitindo direcionar automaticamente os estudantes para trilhas de reforço, regular ou desafio. A aplicação do curso permitiu coletar dados de desempenho e progressão da aprendizagem, os quais foram analisados por meio de abordagens exploratórias e quantitativas. Os resultados preliminares indicam que a adaptação contínua contribui para apoiar estudantes com perfis diversos e favorecer trajetórias de aprendizagem mais equilibradas em educação em computação.Referências
Al Duhayyim, M. and Newbury, P. (2018). Concept-based and fuzzy adaptive e-learning. In Proceedings of the 3rd International Conference on Information and Education Innovations, ICIEI ’18, page 49–56, New York, NY, USA. Association for Computing Machinery.
Ananias, E. and Gaspar, P. D. (2022). A low-cost collaborative robot for science and education purposes to foster the industry 4.0 implementation. Applied System Innovation, 5(4).
Anderson, L. W. and Krathwohl, D. R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Addison Wesley Longman.
Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., and Hatzilygeroudis, I. (2023). Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in greece. Education and Information Technologies, 28(6):6453–6483.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, 2 edition.
Derrick, B., Toher, D., and White, P. (2016). Why welch’s test is type i error robust. The Quantitative Methods for Psychology, 12(1):30–38.
Eryılmaz, M. and Adabashi, A. (2020). Development of an intelligent tutoring system using bayesian networks and fuzzy logic for a higher student academic performance. Applied Sciences, 10(19).
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American psychologist, 34(10):906.
García-Tudela, P. A. and Marín-Marín, J.-A. (2023). Use of arduino in primary education: A systematic review. Education Sciences, 13(2).
Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and Tensor-Flow. O’Reilly Media, 3 edition.
Hursen, C. and Bicen, H. (2025). Global scientific landscape of ai and fuzzy logic in education: A bibliometric and thematic evolution analysis. In CEUR Workshop Proceedings: Workshops at the 8th International Conference on Applied Informatics (ICAIW 2025), pages 80–89, Ben Guerir, Morocco. CEUR-WS.org.
Hwang, G.-J., Sung, H.-Y., Chang, S.-C., and Huang, X.-C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1:100003.
Levene, H. (1960). Robust tests for equality of variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, pages 278–292. Stanford University Press.
Maryono, D., Sajidan, Akhyar, M., Sarwanto, Wicaksono, B. T., and Prakisya, N. P. T. (2025). Ngodingseru. com: an adaptive e-learning system with gamification to enhance programming problem-solving skills for vocational high school students. Discover Education, 4(1):157.
Mayer, R. E. (2002). Multimedia learning. In Psychology of Learning and Motivation, volume 41, pages 85–139. Elsevier.
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8:422.
Prihar, E., Haim, A., Sales, A., and Heffernan, N. (2022). Automatic interpretable personalized learning. In Proceedings of the Ninth ACM Conference on Learning @ Scale, L@S ’22, page 1–11, New York, NY, USA. Association for Computing Machinery.
Razali, N. M. and Wah, Y. B. (2011). Power comparisons of shapiro–wilk, kolmogorov–smirnov, lilliefors and anderson–darling tests. Journal of Statistical Modeling and Analytics, 2(1):21–33.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.
Singh, N., Gunjan, V. K., and Nasralla, M. M. (2022). A parametrized comparative analysis of performance between proposed adaptive and personalized tutoring system “seis tutor” with existing online tutoring system. IEEE Access, 10:39376–39386.
Slavin, R. E. (1995). Cooperative Learning: Theory, Research, and Practice. Allyn and Bacon.
Szczepański, M. and Marciniak, J. (2023). Application of a fuzzy controller in adaptive e-learning content used to evaluate student activity. Procedia Comput. Sci., 225(C):2526–2535.
Tan, P.-N., Steinbach, M., and Kumar, V. (2016). Introduction to data mining. Pearson Education India, 1 edition.
Troussas, C., Krouska, A., Mylonas, P., and Sgouropoulou, C. (2023). Personalized learner assistance through dynamic adaptation of chatbot using fuzzy logic knowledge modeling. 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023), pages 1–5.
Vygotsky, L. S. and Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press.
Ananias, E. and Gaspar, P. D. (2022). A low-cost collaborative robot for science and education purposes to foster the industry 4.0 implementation. Applied System Innovation, 5(4).
Anderson, L. W. and Krathwohl, D. R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Addison Wesley Longman.
Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., and Hatzilygeroudis, I. (2023). Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in greece. Education and Information Technologies, 28(6):6453–6483.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, 2 edition.
Derrick, B., Toher, D., and White, P. (2016). Why welch’s test is type i error robust. The Quantitative Methods for Psychology, 12(1):30–38.
Eryılmaz, M. and Adabashi, A. (2020). Development of an intelligent tutoring system using bayesian networks and fuzzy logic for a higher student academic performance. Applied Sciences, 10(19).
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American psychologist, 34(10):906.
García-Tudela, P. A. and Marín-Marín, J.-A. (2023). Use of arduino in primary education: A systematic review. Education Sciences, 13(2).
Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and Tensor-Flow. O’Reilly Media, 3 edition.
Hursen, C. and Bicen, H. (2025). Global scientific landscape of ai and fuzzy logic in education: A bibliometric and thematic evolution analysis. In CEUR Workshop Proceedings: Workshops at the 8th International Conference on Applied Informatics (ICAIW 2025), pages 80–89, Ben Guerir, Morocco. CEUR-WS.org.
Hwang, G.-J., Sung, H.-Y., Chang, S.-C., and Huang, X.-C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1:100003.
Levene, H. (1960). Robust tests for equality of variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, pages 278–292. Stanford University Press.
Maryono, D., Sajidan, Akhyar, M., Sarwanto, Wicaksono, B. T., and Prakisya, N. P. T. (2025). Ngodingseru. com: an adaptive e-learning system with gamification to enhance programming problem-solving skills for vocational high school students. Discover Education, 4(1):157.
Mayer, R. E. (2002). Multimedia learning. In Psychology of Learning and Motivation, volume 41, pages 85–139. Elsevier.
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8:422.
Prihar, E., Haim, A., Sales, A., and Heffernan, N. (2022). Automatic interpretable personalized learning. In Proceedings of the Ninth ACM Conference on Learning @ Scale, L@S ’22, page 1–11, New York, NY, USA. Association for Computing Machinery.
Razali, N. M. and Wah, Y. B. (2011). Power comparisons of shapiro–wilk, kolmogorov–smirnov, lilliefors and anderson–darling tests. Journal of Statistical Modeling and Analytics, 2(1):21–33.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.
Singh, N., Gunjan, V. K., and Nasralla, M. M. (2022). A parametrized comparative analysis of performance between proposed adaptive and personalized tutoring system “seis tutor” with existing online tutoring system. IEEE Access, 10:39376–39386.
Slavin, R. E. (1995). Cooperative Learning: Theory, Research, and Practice. Allyn and Bacon.
Szczepański, M. and Marciniak, J. (2023). Application of a fuzzy controller in adaptive e-learning content used to evaluate student activity. Procedia Comput. Sci., 225(C):2526–2535.
Tan, P.-N., Steinbach, M., and Kumar, V. (2016). Introduction to data mining. Pearson Education India, 1 edition.
Troussas, C., Krouska, A., Mylonas, P., and Sgouropoulou, C. (2023). Personalized learner assistance through dynamic adaptation of chatbot using fuzzy logic knowledge modeling. 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023), pages 1–5.
Vygotsky, L. S. and Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press.
Publicado
04/05/2026
Como Citar
GRION, Renan; MORAES, Laura O..
Aplicação e Avaliação de um Sistema Adaptativo Baseado em Lógica Fuzzy para o Ensino de Robótica com Arduino. In: SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 6. , 2026, Campo Grande/MS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 770-783.
ISSN 3086-0733.
DOI: https://doi.org/10.5753/educomp.2026.18658.
