A Block-Based Learning Model for Teaching Swarm Intelligence and Bio-Inspired Optimization Algorithms in Elementary and Secondary Education Through an Artificial Fish Swarm Optimization–Inspired Computational Environment
Keywords:
Swarm Intelligence, Bio-Inspired Algorithms, Block-Based Programming, Computational ThinkingAbstract
This study proposes a structured block-based learning model for introducing Swarm Intelligence (SI) and bio-inspired optimization algorithms to elementary and secondary (K–12) learners through an Artificial Fish Swarm Optimization (AFSO)-inspired computational environment. The increasing relevance of computational thinking in early education necessitates pedagogical frameworks that translate complex optimization and artificial intelligence (AI) concepts into developmentally appropriate learning experiences. Grounded in constructivist learning theory and the BSCS 5E Instructional Model, the proposed framework integrates visual programming environments such as Scratch to simulate swarm-based behaviors in an accessible and interactive manner.
The research synthesizes foundational theories from swarm intelligence, genetic programming, particle swarm optimization, and fish swarm algorithms to construct a pedagogical bridge between advanced computational intelligence and K–12 education. The model emphasizes experiential learning, iterative exploration, and simulation-based understanding of collective behavior systems. By leveraging simplified AFSO-inspired agent interactions, learners can visualize emergent optimization processes and develop intuition for decentralized problem-solving systems.
Findings from the conceptual framework indicate that block-based simulation significantly enhances conceptual comprehension, algorithmic thinking, and engagement in STEM education contexts. However, challenges remain in cognitive load management, abstraction limitations, and scalability across age groups. The study contributes a pedagogically grounded computational model that integrates AI education with bio-inspired algorithmic thinking, offering a foundation for future empirical validation in classroom environments.
Downloads
References
1. Bybee R, Taylor JA, Gardner A, Van Scotter P, Carlson Pow-ell J, Westbrook A,et al. The BSCS 5E Instructional Model:Origins and Effectiveness. Colorado Springs: BSCS; 2006. Avail-able from:https://www.bates.edu/research/files/2018/07/BSCS_5E_Executive_Summary.pdf.
2. Holland JH.Adaptation in Natural and Artificial Systems.Univer-sity of Michigan Press; 1975. Available from:https://mitpress.mit.edu/9780262581110/adaptation-in-natural-and-artificial-systems/.
3. Kennedy J, Eberhart R. Flocks, herds and schools: a distributed behav-ioral model.ACM SIGGRAPH Comput Graph. 1987;21(4):25–34.Available from:https://dl.acm.org/doi/pdf/10.1145/37401.37406.
4. Kennedy J, Eberhart RC, Shi Y. Swarm intelligence.InMorgan Kaufmann Series in Artificial Intelligence.Morgan Kaufmann, 2001. ISBN: 9781558605954. Available from:https://doi.org/10.1016/B978155860595-4/50000-0.
5. Koza JR.Genetic Programming: On the Programming of Com-puters by Means of Natural Selection. The MIT Press; 1992.Available from:https://mitpress.mit.edu/9780262527910/genetic-programming/.
6. Li XL, Shao ZJ, Qian JX. Optimizing method based onautonomous animats: fish-swarm algorithm.Syst Eng TheoryPract. 2002;22(11):32–8 (in Chinese). Available from:https://sysengi.cjoe.ac.cn/EN/10.12011/1000-6788(2002)11-32.
7. Liang JJ, Qin AK, Suganthan PN, Baskar S. Comprehensivelearning particle swarm optimizer for global optimization of multi-modal functions.IEEE Trans Evol Comput. 2006;10(3):281–95. Available from:https://ieeexplore.ieee.org/document/1637688.
8. Liu M, Li X, Zhang M, Li C. Research on artificial fish swarmalgorithm with cultural evolution for subcarrier allocation.Int J Hybrid Inf Technol. 2015;8(6):301–10. Available from:https://gvpress.com/journals/IJHIT/vol8_no6/27.pdf.
9. Molina D, Poyatos J, Ser JD, García S, Hussain A, HerreraF. Comprehensive taxonomies of nature-and bio-inspiredoptimization: inspiration versus algorithmic behavior, criticalanalysis recommendations.Cogn Comput. 2020;12(5):897–939. Available from:https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/2847_10.1007@s12559-020-09730-8.pdf.
10. Oyekunle Oyewumi Taiwo D, Preston D. Swarm intelligence anddecision-making: the role of swarm intelligence in decision. 2024.Available from:https://ssrn.com/abstract=4849581.Vol 3 | Issue 3 | August 202416
11. Papert S.Mindstorms: Children, Computers, and Powerful Ideas.New York: Basic Books; 1980. Available from:https://dl.acm.org/doi/pdf/10.5555/1095592.
12. Reynolds CW. Flocks, herds and schools: a distributed behav-ioral model.ACM SIGGRAPH Comput Graph. 1987;21(4):25–34.Available from:https://dl.acm.org/doi/pdf/10.1145/37401.37406.
13. Resnick M. Sowing the seeds for a more creative society.Learn LeadTechnol. 2007;35(4):18–22. Available from:https://web.media.mit.edu/~mres/papers/Learning-Leading.pdf.
14. SalpasaranisIntroducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Salpasaranis K, Stylianakis V, Kotsopoulos ST. Comprehensive taxonomies of nature-and bio-inspiredoptimization: inspiration versus algorithmic behavior, criticalanalysis recommendations.Cogn Comput. 2020;12(5):897–939. Available from:https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/2847_10.1007@s12559-020-09730-8.pdf.
15. Scratch.mit.edu About scratch [Internet]. 2024. Available from:https://scratch.mit.edu/.
16. Scratch.mit.edu, Salpasaranis K. Simplified artificial fish swarmalgorithm simulation for elementary and junior secondary educa-tion [Internet]. 2024 [updated 2024 February 26; cited 2024 July 26].Available from:https://scratch.mit.edu/projects/967330607.
17. Scratch.mit.edu, Salpasaranis K. Simplified artificial fish swarmalgorithm simulation for senior secondary education [Internet].2024 [updated 2024 February 26; cited 2024 July 26]. Available from:https://scratch.mit.edu/projects/967331536.Vol 3 | Issue 3 | August 202417
18. Vesterstrom J, Thomsen R. A comparative study of differen-tial evolution, particle swarm optimization, and evolutionaryalgorithms on numerical benchmark problems.Proceedings ofthe 2004 Congress on Evolutionary Computation (IEEE Cat.No.04TH8753), vol. 2, pp. 1980–7, Portland, OR, USA, 2004. Available from:https://ieeexplore.ieee.org/document/1331139.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dr. Ryohei Matsuda, Dr. Misaki Kato (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published by The Parthenon Frontiers and its associated journals are distributed under the terms of the Creative Commons Attribution (CC BY 4.0) International License unless otherwise stated.
Authors retain full copyright of their published work. By submitting their manuscript, authors agree to grant The Parthenon Frontiers a non-exclusive license to publish, archive, and distribute the article worldwide. Authors are free to:
-
Share their article on personal websites, institutional repositories, or social media platforms.
-
Reuse their content in future works, presentations, or educational materials, provided proper citation of the original publication.
