There are scientific reports suggesting striking similarities between the structures of networked systems, ranging from the tiny brain cells to atoms, to the Internet, and all the way up to even the galaxies. It is further argued that the similarities might be due to the existence of a universal natural growth process. At the microscopic level we do not yet know what that mechanism might be, however, we do have some significant clues at the macroscopic level, which do indicate that both mind (a network of the brain cells) and matter (i.e., network of atoms, molecules, planets, and galaxies) operate similarly. This article attempts to briefly explain such similarities using an abstract growth process and a structural representation along with some general concepts from computing, cognitive and natural sciences. This operational structure is well aligned with the latest empirical research on cognitive psychology and neuroscience. Technology tools whose pedagogical use is also aligned with our operational structure of the mind have been found to consistently increase student engagement and achievement in secondary schools. The same growth process and structural representation seem to describe the behavior and growth of the matter in the universe in many ways we can all relate to. Our interdisciplinary experience and analysis of analogies in different fields offer support to reports by the physicists and biologists about existence of a universal growth mechanism.
A Universal Process: How Mind and Matter Seem to Work, Science Discovery.
Vol. 3, No. 6,
2015, pp. 76-81.
Rundle, M. “Physicists Find Evidence That the Universe is a Giant Brian.” Huffington Post UK, November 27, 2012.
Ghose, T. “Universe, Brain, and Internet: Growth patterns Similar in Large & Small Networks, Computer Study Suggests.” Huffington Post UK. Also, “Universe Grows Like a Giant Brain.” Life Science, November 26, 2012.
Miller, M. (2006). Neuronal Network. Brandeis Univ. http://www.visualcomplexity.com/vc/project.cfm?id=307. Retrieved October 15, 2015.
Millennium Simulation. Science Daily. http://www.sciencedaily.com/releases/2005/06/050604061156.htm. June 4, 2005.
Krioukov, D., Kitsak, M., Sinkovits, R. S., Rideout, D., Meyer, D. and Boguna, M. “Network Cosmology.” Scientific Reports, Nature, Article 793. http://www.nature.com/srep/2012/121113/srep00793/full/srep00793.html. November 16, 2012.
Yaşar, O., Veronesi, P., Maliekal, J. and Little, L. (2015). “Computational Pedagogical Content Knowledge (CPACK).” In D. Slykhuis & G. Marks (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2015 (pp. 3514-3521).
Yaşar, O. and Maliekal, J. (2014). “Computational Pedagogy.” IEEE Comp Sci & Eng, 16 (3), 2014; 78-88.
Yaşar, O. (2014). “A Pedagogical Approach to Teaching Computing Principles in the Context of Modeling and Simulations.” J. Computing Teachers, Winter Issue.
Yaşar, O., Maliekal, J., Veronesi, P. and Little, L. (2014). “An Interdisciplinary Approach to Professional Development of Math, Science, and Technology Teachers.” J. Comp. in Math and Science Teaching, 33 (3), 2014; 349-374.
Yaşar, O. (2013). “Computational Math, Science, and Technology (C-MST) Approach to General Education Courses.” J. Computational Science Education, Vol. 4 (1), 2013; 2-10.
Yaşar, O. (2013). “Teaching Science through Computation.” Int. J. Science, Technology and Society, Vol. 1 (1), 2013; 9-18.
Yaşar, O., Maliekal, J., Little, L. and Jones, D. (2006). “A Computational Technology Approach to Education.” IEEE J. Comp. in Sci. & Eng., 8 (3), 2006; 76-81.
Yaşar, O., Little, L., Tuzun, R., Rajasethupathy, K., Maliekal, J. and Tahar, M. (2006). “Computational Math, Science, and Technology (C-MST).” Lecture Notes in Computer Science, 3992, 2006; 169-176.
Yaşar, O. (2004). “C-MST Pedagogical Approach to Math and Science Education.” Lecture Notes in Computer Science, 3045, 2004; 807-816.
Yaşar, O. and Landau, R. (2003). “Elements of Computational Science and Engineering Education,” SIAM Review, 45 (4), 2003; 787-805.
Yaşar, O. (2001). “Computational Science Education: Standards, Learning Outcomes and Assessment,” Lecture Notes in Computer Science, 2073, 2001; 1159-1169.
Yaşar, O., Rajasethupathy, K., Tuzun, R., McCoy, A. and Harkin, J. (2000). “A New Perspective on Computational Science Education.” IEEE Comp. in Sci & Eng, 5(2), 2000; 74-79.
Brown, P. C. Roediger, H. L. and McDaniel, M. A. (2014). Make it Stick. The Belknap Press of Harvard University.
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. and Goodman, N. D. (2011). “How to Grow a Mind: Statistics, Structure, and Abstraction.” Science, 331, 2011; 1279-1285.
Bransford, J., Brown, A. & Cocking, R. (2000). How People Learn. National Academy Press, Washington, D.C.
Armoni, M. (2013). “On Teaching Abstraction to Computer Science Novices.” J. Comp in Math & Science Teaching, 32 (3), 2013; 265-284.
Shakespeare, W. (1982). Hamlet. Ed. Harold Jenkins. London.
Mooney, C. G. (2013). An Introduction to Dewey, Montessori, Erikson, Piaget, and Vygotsky. Redleaf Press: St. Paul, MN.
Montague, R. (2006). How We Make Decisions. Plume Books: New York.
Restak, R. (2001). The Secret Life of the Brain. The Dana Press: New York.
Hawking, S. (1988). A Brief History of Time. Random House.
A Framework for K-12 Science Education. The National Academies Press, Washington, D.C. http://www.nap.edu.
Wing, J. M. (2006). “Computational Thinking,” Comm. ACM, 49 (3), 2006; 33-35.