Nonlinear dynamics and synchronization of computational cognitive model in educational science
Journal of nonlinear sciences and its applications, Tome 14 (2021) no. 1, p. 15-28.

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A computational cognitive model is derived from nonlinear interactions of (Neo) Piagetian-Vygostkian constructs to explain, and predict cognitive processes during collaborative learning. Learning is re-conceptualized as continuous perturbations of cognitive state which unfolds stable cognitive trajectories near Piagetian equilibrium. The model explicates topologically equivalent cognitive patterns, attributed to multi-modal representation of sensory information presented to the learners. Synchronization of the cognitive model is obtained via active control functions which predicts convergence of cognitive states. The synchronized cognitive model is stabilized using Lyapunov matrix equation. These qualitative behaviors emerged due to learner-to-learner and instructor-to-learner scaffolding driven by cognitive executive functions. The dynamical behaviors of the cognitive model are simulated using control parameters with estimated datasets showing viable cognitive trajectories.
DOI : 10.22436/jnsa.014.01.03
Classification : 37C75, 91E10, 97C30, 97M10
Keywords: Cognitive model, equilibrium, learning, stability, synchronization, Lyapunov matrix equation

Akpan, Ekemini T.  1 ; Joshua, Enobong E.  2 ; Uduk, Ignatius E.  3

1 Department of Science Education, Mathematics Unit, Faculty of Education, University of Uyo, Uyo
2 Department of Mathematics, Faculty of Science, University of Uyo, Uyo
3 Department of Human Kinetics, Faculty of Education, University of Uyo, Uyo
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Akpan, Ekemini T. ; Joshua, Enobong E. ; Uduk, Ignatius E. . Nonlinear dynamics and synchronization of computational cognitive model in educational science. Journal of nonlinear sciences and its applications, Tome 14 (2021) no. 1, p. 15-28. doi : 10.22436/jnsa.014.01.03. http://geodesic.mathdoc.fr/articles/10.22436/jnsa.014.01.03/

[1] Bhattacharya, J. Cognitive neuroscience: Synchronizing brain in the classroom, Current Biology, Volume 27 (2017), pp. 339-363

[2] Bormanaki, H. B. The role of equilibration in Piaget's thoery of cognitive development and it implication for receptive skills: A theoretical study, J. Lang. Teach. Res., Volume 8 (2017), pp. 996-1005

[3] Dikker, S.; Wan, L.; Davidesco, I.; Barel, J. J. Van; Ding, M.; Poeppel, D. Brain-to-brain synchrony tracks real-world dynamics group interactions in the classroom, Cuurent Biology, Volume 27 (2017), pp. 1375-1389

[4] Ennis, C. D. Reconceptualizing learning as dynamical system, J. Curriculum Supervision, Volume 7 (1992), pp. 115-130

[5] Farrell, S.; Lewandowsky, S. An introduction to cognitive modelling, in: An introduction to model-based cognitive neuroscience, Springer, New York, 2015 | DOI

[6] Gistrap, D. L. Dissipative structures in educational change: Prigogine and the academic, Int. J. Leader. Educat. Theor. Prac., Volume 10 (2007), pp. 49-69

[7] Greenwood, P.; Parasuraman, P. Neuronal and cognitive plasticity: A neurocognitive framework for ameliorating cogntitive aging, Frontiers in Aging Neuroscience, Volume 2 (2010), pp. 1-14 | DOI

[8] Guastello, S. J.; Liebovich, L. S. Introduction to nonlinear dynamics and complexity, In: Chaos and complexity in psychology: The theory of nonlinear dynamical systems, Cambridge University Press, Cambridge, 2009

[9] Haddad, W. M.; Chelloboina, V. S. Nonlinear dynamical systems and controls: A Lyapunov-based approach, Princeton University Press, Princeton, 2008

[10] Heath, R. A. Nonlinear dynamics: Techniques and applications in psychology, Mahwah, Erlbaum, 2000

[11] Konar, A. Artificial intelligence and soft computing: Behavioural and cognitive modelling of the human brain, CRC Press, Boca Raton, 2000

[12] Koopsman, M.; Stamovlasis, D. Complex dynamical systems in education: Concepts, methods and applications, Springer, Cham, 2016 | DOI

[13] Lamb, R.; Cavagnetto, A.; Akmal, A. Examination of the nonlinear dynamic systems associated with student cognition while engaging in science information processing, Int. J. Sci. Math. Edu., Volume 14 (2016), pp. 187-205 | DOI

[14] Liao, X. X.; Wang, L. Q.; Yu, P. Stability of dynamical systems, Elsevier B. V., Amsterdam, 2014 | Zbl

[15] Lin, L. Investigating chinese HE EFL classrooms, Springer, New York, 2005 | DOI

[16] Lockwood, P.; Klein-Flügge, M. Computational modelling of social cognition and behaviour--a reinforcement learning primer, Social cognitive and affective neuroscience, Volume 2020 (2020), pp. 1-29 | DOI

[17] Milescu, N.; Rusu, M. V.; Berlic, C. A nonlinear dynamical system approach of learning, Romanian Reports in Physics, Volume 5 (2013), pp. 1547-1556

[18] Nicoleiscu, B. N.; Petrecu, T. C. Dynamical system theory: A powerful tool in educational sciences, Procedia-Social and Behavioral Sciences, Volume 76 (2013), pp. 581-587

[19] Piaget, J. To understand is to invent: The future of education, Free Press, New York, 1973

[20] Poston, T.; Stewart, I. Catastrophe theory and its applications, Pitman, London, 1978 | Zbl

[21] Rabinovich, M. I.; Huerta, R.; Varona, P.; Afraimovich, V. S. Transient cognitive dynamics, metastability, abd decision making, Comput. Biol., Volume 4 (2008), pp. 1-9

[22] Reithmeier, E. Periodic solutions of nonlinear dynamical systems: Numerical computation, stability, bifurcation and transition to chaos, Springer-Verlag, Berlin, 1991 | DOI

[23] Saari, D. G. A Qualitative Model for the Dynamics of Cognitive Processes, J. Mathematical Psychology, Volume 15 (1977), pp. 145-168 | Zbl | DOI

[24] Stamovlasis, D. Catastrophe theory: Methodology, epistemology, and applications in learning science, in: Complex dynamical systems in education: Concepts, methods and applications, Springer International Publishing, Cham, 2016

[25] Sweller, J.; Ayres, P.; Kalyuga, S. Cognitive load theory, Springer, New York, 2011

[26] Vallacher, R. R.; Nowak, A. The dynamics of human experience: Fundamental of dynamical social psychology, in: Chaos and complexity in psychology: Theory of nonlinear dynamical sytems, Cambridge University Press, New York, 2009

[27] Vallacher, R. R.; Nowak, A.; Zochowski, M. Dynamics of social coordination: The synchronization of internal states in close relationship, Interaction Studies, Volume 6 (2005), pp. 35-52

[28] Mass, H. L. J. Van der; Molenaar, P. C. Stagewise Cognitive Development: An Applicatiion of Catastrophe Theory, Psychological Review, Volume 99 (1992), pp. 395-417

[29] Geert, P. Van A Dynamic Systems Model of Cognitive and Language Growth, Psychological Review, Volume 98 (1991), pp. 3-53

[30] Geert, P. Van; Steenbeck, H. The dynamics of scaffolding, New Ideas in Psychology, Volume 23 (2005), pp. 115-128

[31] Wang, Z.; Liu, Z.; Zheng, C. Qualitative analysis and control of complex neural networks with delays, Springer, Berlin, 2016

[32] Weichhart, G. The learning environment as a chaotic and complex adaptive system, Systema: Connecting Matter, Life, Culture and Technology, Volume 1 (2013), pp. 36-53

[33] Wiggins, S. Introduction to applied nonlinear dynamical systems and chaos, Springer-Verlag, New York, 2003

[34] Young, G. Development and causality: Neo-Piagetian perspectives, Springer, New York, 2011

[35] Zak, M.; Zbilut, J. P.; Meyers, R. E. From instability to intelligence: Complexity and predictability in nonlinear dynamics, Springer-Verlag, Berlin, 1997

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