METHODS OF THE FRACTAL APPROACH IN SCIENCE EDUCATION: INNOVATIVE TECHNOLOGY AND CONCEPTS OF COMPUTER MODELING
DOI:
https://doi.org/10.31110/2413-1571-2023-038-3-010Keywords:
computer modeling, fractal approach, self-organizing processes, synergetics, physical and mathematical education, аrtificial іntelligenceAbstract
Formulation of the problem. At the present stage of the development of science education and information technology, their integration, complementarity, and implementation are essential. Therefore, the search for methods of teaching natural sciences, based on the principles of self-organization and computer modeling, corresponds to the immediate tasks of the present.
Materials and methods. Methods of comparative analysis, computer modeling, and generalization strategy are used. The study is based on the physics course content and the use of the programming language.
Results. An innovative fractal approach to the teaching of physical and mathematical disciplines is proposed as a method of improving independent and creative computer modeling of natural phenomena. The fundamental principles of object-oriented programming (encapsulation, inheritance, polymorphism) have proven to be influential in shaping the physical and mathematical aspects of the information architecture of the perception of educational disciplines. The possibility of using this approach in other sections of physics is demonstrated. The developed iterations of the fractal structure are presented in the example of the study of the "Geometric Optics" and "Wave Optics" sections of physics. It is shown that each iteration is characterized by synergy: the addition of a new iteration provides a high-quality and in-depth perception of new information.
Conclusions. The formation of the specified integrated fractal structure conditions the integrity of information perception and its formation happens intuitively. The analysis of the conducted studies confirmed the innovativeness and effectiveness of the fractal approach. This approach can be used to develop systems for the processing and transmission of information, intelligent information materials, and artificial intelligence.
Downloads
References
Frame, M.L., & Mandelbrot, B.B. (2002). Fractals, Graphics, and Mathematics Education. New York: Wiley.
Hodson, D. (2014). Learning Science, Learning about Science, Doing Science: Different goals demand different learning methods. -International Journal of Science Education, 36(15), 2534-2553. https://doi.org/10.1080/09500693.2014.899722
Kafyulilo, A.C., Fisser, P., & Voogt, J. (2015). Supporting Teachers Learning Through the Collaborative Design of Technology-Enhanced Science Lessons. Journal of Science Teacher Education, 26(8), 673-694. https://www.learntechlib.org/p/194868/
Kuo, E., Hull, M.M., Gupta, A., & Elby, A. (2013). How Students Blend Conceptual and Formal Mathematical Reasoning in Solving Physics Problems. Science Education, 97(1), 32–57. http://dx.doi.org/10.1002/sce.21043
Lotter, C., Harwood, W. S., & Bonner, J. J. (2007). The influence of core teaching conceptions on teachers’ use of inquiry teaching practices. - Journal of Research in Science Teaching, 44(9), 1318 – 1347. https://doi.org/10.1002/tea.20191
Luft, J. A. (2001). Changing inquiry practices and beliefs: The impact of an inquiry-based professional development programme on beginning and experienced secondary science teachers. International Journal of Science Education, 23(5), 517–534. http://dx.doi.org/10.1080/09500690121307
Mar’yan, M., Seben, V., & Yurkovych, N. (2020). Synergetics, Fractality and Information. Application to the Self-Organized Sructures and Intelligent Materials. Presov: University of Presov in Presov Publishing.
Özcan, Ö. (2015). Investigating students’ mental models about the nature of light in different contexts. Eur. J. Phys., 36(6), 1-16. http://dx.doi.org/10.1088/0143-0807/36/6/065042
Sherin, B. (2006). Common sense clarified: The role of intuitive knowledge in physics problem solving. Journal of Research in Science Teaching, 43(6), 535 – 555. https://doi.org/10.1002/tea.20136
Slаdek, P., Pawera, L., & Vаlek, J. (2011). Remote laboratory – new possibility for school experiment. Procedia Social and Behavioral Sciences, 12, 164-167. https://doi.org/10.1016/j.sbspro.2011.02.023
Sugden, S.J. (2009). Problem Solving with Delphi. New York: Nova Science Publishers.
Windschitl, M. (2004). Folk theories of “inquiry”: How preservice teachers reproduce the discourse and practices of an atheoretical scientific method. Journal of Research in Science Teaching, 41(5), 481 – 512. https://doi.org/10.1002/tea.20010
Yurkovych, N., Seben, V., & Mar'yan, M. (2017). Computer modeling and innovative approaches in physics: optics. Presov: Prešovska univerzita v Prešove. https://dspace.uzhnu.edu.ua/jspui/handle/lib/48051
Yurkovych, N., Seben, V., & Mar’yan, M. (2017). Fractal approach to teaching physics and computer modeling. Journal of Science Education, 18(2), 117-120. http://www.chinakxjy.com/downloads/V18-2017-2.html
Downloads
Published
How to Cite
License
Copyright (c) 2023 Nataliya Yurkovych, Mykhaylo Mar’yan, Magdalena Opachko, Vladimir Seben
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Authors grant the journal a right of the first publication of the work under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)that allows others freely to use (read, copy and print) submissions, search content and link to published articles, disseminate their full text and use them for any legitimate non-commercial purposes (i.e. educational or scientific) with the mandatory reference to the article’s authors and initial publication in this journal.
- Original published articles cannot be used by users (exept authors) for commercial purposes or distributed by third-party intermediary organizations for a fee.
