An Interactive Educational Platform for Introducing Genetic Algorithm Concepts
Resumo
Introduction: Teaching Artificial Intelligence (AI) concepts is challenging due to their abstract and interdisciplinary nature. Objective: This work presents an interactive educational platform to support the learning of Genetic Algorithms (GAs), making their processes tangible through simulations and visualizations. Methodology: The platform was developed using web technologies and evaluated with undergraduate students through the MEEGA+ model [Petri et al. 2019]. Expected Results: Results indicate that 94.4% of participants considered the tool intuitive and suitable for educational use, highlighting its strong pedagogical potential.
Palavras-chave:
Human-Computer Interaction, Genetic Algorithms, Educational Technology, Interactive Visualization, Simulation-Based Learning
Referências
Chiao, A. (2021). Genetic algorithm visualization (tsp solver). [link]. Accessed: 2025-05-19.
Clematis, A., Forcheri, P., e Quarati, A. (2006). Interacting with Learning Objects in a Distributed Environment . In 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008), pages 322–329, Los Alamitos, CA, USA. IEEE Computer Society
Coppin, B. (2013). Artificial Intelligence (in Portuguese). LTC.
Crearie, L. (2013). Human computer interaction (hci) factors in technology enhanced learning. In Proceedings of the International Conference on Information Communication Technologies in Education, pages 99–108.
Eck, D. (2020). Genetic algorithms demo. [link]. Accessed: 2025-05-19.
Forrest, S. (1996). Genetic algorithms. ACM computing surveys (CSUR), 28(1):77–80.
Hayashi, E. C. e Baranauskas, M. C. C. (2015). Designing for affectibility: principles and guidelines. In HCI International 2015-Posters’ Extended Abstracts: International Conference, HCI International 2015, Los Angeles, CA, USA, August 2–7, 2015. Proceedings, Part I, pages 25–31. Springer.
on Daniel Shiffman’s Smart Rockets, B. (2022). Smart rockets genetic algorithm demo. [link]. Accessed: 2025-0519.
Petri, G., von Wangenheim, C., e Borgatto, A. (2019). Meega+: a model for the evaluation of educational games for computing education. Brazilian Journal of Computers in Education (Revista Brasileira de Informática na Educação-RBIE), 27(3):52–81.
Wagner, M., Kohlros, E., Quantmeyer, G., e Kötzing, T. (2023). Elea – build your own evolutionary algorithm in your browser.
Wilensky, U. (1997). Simple genetic algorithm (netlogo model). [link]. Center for Connected Learning and Computer-Based Modeling, Accessed: 2025-0519.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3):33–35.
Zeman, T. e Holeˇcek, J. (2022). Effective creation of interactive educational objects for on-line teaching. In 2022 31st Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE), pages 1–5.
Clematis, A., Forcheri, P., e Quarati, A. (2006). Interacting with Learning Objects in a Distributed Environment . In 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008), pages 322–329, Los Alamitos, CA, USA. IEEE Computer Society
Coppin, B. (2013). Artificial Intelligence (in Portuguese). LTC.
Crearie, L. (2013). Human computer interaction (hci) factors in technology enhanced learning. In Proceedings of the International Conference on Information Communication Technologies in Education, pages 99–108.
Eck, D. (2020). Genetic algorithms demo. [link]. Accessed: 2025-05-19.
Forrest, S. (1996). Genetic algorithms. ACM computing surveys (CSUR), 28(1):77–80.
Hayashi, E. C. e Baranauskas, M. C. C. (2015). Designing for affectibility: principles and guidelines. In HCI International 2015-Posters’ Extended Abstracts: International Conference, HCI International 2015, Los Angeles, CA, USA, August 2–7, 2015. Proceedings, Part I, pages 25–31. Springer.
on Daniel Shiffman’s Smart Rockets, B. (2022). Smart rockets genetic algorithm demo. [link]. Accessed: 2025-0519.
Petri, G., von Wangenheim, C., e Borgatto, A. (2019). Meega+: a model for the evaluation of educational games for computing education. Brazilian Journal of Computers in Education (Revista Brasileira de Informática na Educação-RBIE), 27(3):52–81.
Wagner, M., Kohlros, E., Quantmeyer, G., e Kötzing, T. (2023). Elea – build your own evolutionary algorithm in your browser.
Wilensky, U. (1997). Simple genetic algorithm (netlogo model). [link]. Center for Connected Learning and Computer-Based Modeling, Accessed: 2025-0519.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3):33–35.
Zeman, T. e Holeˇcek, J. (2022). Effective creation of interactive educational objects for on-line teaching. In 2022 31st Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE), pages 1–5.
Publicado
08/09/2025
Como Citar
MOREIRA, Gessyca; DE JESUS, Ângelo Magno; DE PAULA, Suelen Mapa.
An Interactive Educational Platform for Introducing Genetic Algorithm Concepts. In: PÔSTERES E DEMONSTRAÇÕES - SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 24. , 2025, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 196-200.
DOI: https://doi.org/10.5753/ihc_estendido.2025.13272.
