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Creativity in Action: Exploring Cross-Cutting Models of Self-Organization and Emergence in Science and Technology

Manfred Euler

IPN - Leibniz Institute for Science and Mathematics Education, Olshausenstrasse 62, D-24118 Kiel, Germany

https://doi.org/10.1142/S2591722621400123Cited by:1 (Source: Crossref)

Abstract

This review presents a sequence of exemplary experience-based encounters with self-organizing systems on different levels of difficulty. Based on hands-on experiments and creative modeling it provides a viable educational road to build up a deeper understanding of self-organization principles and their comprehensive nature. Theories of self-organization describe how patterns, structures and new types of behavior emerge in energetically open systems, resulting from the local interaction of many components. As an external control instance is missing, the underlying philosophy is counterintuitive to our habits of causal thinking. This thematic and conceptual framework impacts on many STEM domains and presents a blueprint for modeling emergent structures and complex functions in natural and technological systems. It reveals unifying principles that can help in reducing, in structuring and, finally, in understanding and controlling the emerging complexity. An overview across diverse STEM domains highlights the role of this overarching concept. This cross-disciplinary approach can help in improving the dialogue and the knowledge exchange between the individual fields. Moreover, in a self-referential fashion, the modeling of self-organization provides us with fresh perspectives to reflect our own creative processes.

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References

1. M. Euler, The mechanics of creative cognition: Orchestrating the productive interplay of procedural and conceptual knowledge in STEM education, Proceedings of the Singapore National Academy of Science 15(2) (2021). Link, Google Scholar
2. D. Hestenes, Toward a modeling theory of physics instruction, American Journal of Physics 55 (1987) 440–454. Crossref, Google Scholar
3. M. Wells, D. Hestenes and G. Swackhamer, A modelling method for high school physics instruction, American Journal of Physics 63 (1995) 606–619. Crossref, Google Scholar
4. J. J. Clement and M. A. Rea-Ramirez, Model Based Learning and Instruction in Science. Springer (2008). Crossref, Google Scholar
5. P. S. Oh and S. J. Oh, What teachers of science need to know about models: An overview, International Journal of Science Education 33(8) (2011) 1109–1130. Crossref, Google Scholar
6. I. T. Koponen, Models and modelling in physics education: A critical re-analysis of philosophical underpinnings and suggestions for revisions, Science & Education 16 (2007) 751–773. Crossref, Google Scholar
7. M. Windschitl, J. Thompson and M. Braaten, Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations, Science Education 92 (2008) 941–967. Crossref, Google Scholar
8. M. Euler, The role of experiments in the teaching and learning of physics, In E. F. Redish & M. Vicentini (Eds.), Research on Physics Education, Varenna Couse CLVI, IOS Press, (2004), pp. 175–221. Google Scholar
9. A. Jenkins, Self-oscillations, Physics Reports 525 (2013) 167–222. Crossref, Google Scholar
10. M. Euler, Synergetik für Fußgänger I $+$ II, Physik in der Schule 33 (1995) 189–194, 237–242. Google Scholar
11. M. Euler, Selbstorganisation, Strukturbildung und Wahrnehmung - Versuche mit dem singenden Rohr, Biologie in unserer Zeit 30 (2000) 45–53. Crossref, Google Scholar
12. Th. Bell, Komplexe Systeme und Strukturprinzipien der Selbstregulation im fächerübergreifenden Unterricht – eine Lernprozessstudie in der SII, Zeitschrift für Didaktik der Naturwissenschaften. 10 (2004) 163–204. Google Scholar
13. M. Euler, Hands-on synchronization: An adaptive clockwork universe, The Physics Teacher 44 (2006) 28–32. Crossref, Google Scholar
14. A. Pikovsky, M. Rosenblum and J. Kurths, Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge UP (2001). Crossref, Google Scholar
15. P. Dallos, A. N. Popper and R. R. Fay (Eds.), The Cochlea. Springer (1996), p. 1–43. Crossref, Google Scholar
16. H. Fastl and E. Zwicker, Psychoacoustics Facts and Models. Springer (2007). Crossref, Google Scholar
17. J. G. Roederer, The Physics and Psychophysics of Music — An Introduction. Springer (2008). Google Scholar
18. D. T. Kemp, Otoacoustic emissions, their origin in cochlear function, and use, British Medical Bulletin. 63 (2002) 223–241. Crossref, Google Scholar
19. N. Jayant, J. Johnston and R. Safranek, Signal compression based on models of human perception, Proceedings of the IEEE 81 (1993) 1385–1422. Crossref, Google Scholar
20. B. V. Liengme, Modeling Physics with Microsoft Excel. Morgan & Claypool (2014). Crossref, Google Scholar
21. B. L. Sherin, How students understand physics equations, Cognition and Instruction 19 (2001) 479–541. Crossref, Google Scholar
22. R. Adler, A study of locking phenomena in oscillators, Proceedings of the IRE. 34 (1946) 351–357. Crossref, Google Scholar
23. Y. Kuramoto, Self-entrainment of a population of coupled nonlinear oscillators, In H. Araki (Ed.), International Symposium on Mathematical Problems in Theoretical Physics, Lecture Notes Phys. Vol. 39. Springer (1975), pp. 420–423. Crossref, Google Scholar
24. P. Landa, Regular and Chaotic Oscillations. Springer (2001). Crossref, Google Scholar
25. I. S. Gradshteyn and M. Ryzhik, Table of Integrals, Series, and Products, D. Zwillinger & V. Moll (Eds.), 8th ed., Academic Press. [equation 2.551-3] (2015). Google Scholar
26. D. B. Sullivan and J. E. Zimmerman, Mechanical analogs of time dependent josephson phenomena, American Journal of Physics 39 (1971) 1504–1516. Crossref, Google Scholar
27. H. Haken, Synergetics: Introduction and Advanced Topics. Springer (2004). Crossref, Google Scholar
28. T. Erneux and P. Glorieux, Laser Dynamics. Cambridge Univ. Press (2010). Crossref, Google Scholar
29. Th. Udem, R. Holzwarth and T. W. Hänsch, Optical frequency metrology, Nature 416 (2002) 233–237. Crossref, Google Scholar
30. A. Barone and G. Paterno, Physics and Applications of the Josephson Effect. John Wiley & Sons (1982). Crossref, Google Scholar
31. R. De Luca, A. Giordano and I. D’Acunto, Mechanical analog of an over-damped Josephson junction, Eur. J. Phys. 36 (2015) 055042. Crossref, Google Scholar
32. J. Lehn, Toward complex matter: Supramolecular chemistry and self-organization, Proceedings of the National Academy of Sciences 99 (2002) 4763–4768. Crossref, Google Scholar
33. J. D. Murray, Mathematical Biology I. An Intrduction. 3rd ed. Springer (2002). Google Scholar
34. J. Walleczek, Self-Organized Biological Dynamics and Nonlinear Control. Cambridge Univ. Press (2000). Crossref, Google Scholar
35. S. Boccaletti, A. N. Pisarchik, C. I. del Genio and A. Amann, Synchronization: From Coupled Systems to Complex Networks. Cambridge University Press (2018). Crossref, Google Scholar
36. B. Dresp-Langley, Seven properties of self-organization in the human brain, Big Data Cogn. Comput. 4 (2020) 10. Crossref, Google Scholar
37. R. Pfeifer and J. C. Bongard, How the Body Shapes the Way We Think: A New View of Intelligence. MIT Press (2006). Crossref, Google Scholar
38. J. A. S. Kelso, Dynamic Patterns. The Self-Organization of Brain and Behavior. MIT Press (1996). Google Scholar
39. G. Buzsaki, Rhythms of the Brain. Oxford Univ. Press (2006). Crossref, Google Scholar
40. S. Grossberg, Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support, Neural Networks 87 (2017) 38–95. Crossref, Google Scholar
41. D. J. Chalmers, Facing up to the problem of consciousness, Int. J. of Consciousness Studies 2 (1995) 200–219. Google Scholar
42. G. Ellis, How Can Physics Underlie the Mind? Top-Down Causation in the Human Context. Springer (2016). Google Scholar
43. M. Botvinick and J. Cohen, Rubber hands ‘feel’ touch that eyes see, Nature 391 (1998) 756. Crossref, Google Scholar
44. P. J. Aubusson, A. G. Harrison and S. M. Ritchi (Eds.), Metaphor and Analogy in Science Education. Springer (2007). Google Scholar
45. K. J. Holyoak and P. Thagard, Analogical mapping by constraint satisfaction, Cognitive Science 13 (1989) 295–355. Crossref, Google Scholar
46. N. B. Mozzer and R. Justi, Students’ pre- and post-teaching analogical reasoning when they draw their analogies, International Journal of Science Education 34 (2012) 429–458. Crossref, Google Scholar
47. M. Euler, Empowering the engines of knowing and creativity: Learning from experiments, In D. Sokołowska & M. Michelini (Eds.), The Role of Laboratory Work in Improving Physics Teaching and Learning. Springer (2018), pp. 3–14. Crossref, Google Scholar
48. G. A. Stillman and J. P. Brown, Lines of Inquiry in Mathematical Modelling Research in Education. Springer (2019). Crossref, Google Scholar
49. G. A. Stillman, G. Kaiser and C. E. Lampen, Mathematical Modelling Education and Sense-making. Springer (2020). Crossref, Google Scholar
50. C. Smith and C. Morgan, Curricular orientations to real-world contexts in mathematics, The Curriculum Journal 27(1) (2016) 24–45. Crossref, Google Scholar
51. U. T. Jankvist and M. Niss, Upper secondary school students’ difficulties with mathematical modelling, International Journal of Mathematical Education in Science and Technology 51(4) (2020) 467–496. Crossref, Google Scholar
52. J. S. Groff, Dynamic systems modeling in educational system design & policy, New Approaches in Educational Research 2 (2013) 72–81. Crossref, Google Scholar