Construction of Dynamic Academic Systems: A Preliminary Modeling
Nilo Serpa, Marcelo Alcântara, Emilly Moura da Cruz
Journal of Education, Society and Behavioural Science · pp. 1–9 · Published 3 Nov 2017
10.9734/JESBS/2017/37152Abstract
Aims: The aim of this theoretical study is to present and discuss a chaotic simulation model addressed to understand how an academic environment can evolve from chaos to stabilized states, providing a consistent basis to support new methodological initiatives that promote changes in the current paradigm of education. Study Design: The study was designed from the classical literature on chaotic systems. Place and Duration of Study: Civil Engineering Coordination, ICESP/Promove, Brasília, Brazil, between May 2015 and January 2017. Methodology: We consider, by hypothesis, a system formed hierarchically by the class of professors and researchers of a higher education institution. This hierarchy does not reflect an organization of power; rather, it is referred to acting positions, such as research group leaders, course completion counselors, scientific initiation counselors, etc. The individuals were classified by profiles of ordered abilities represented by binary strings defining a topology. Such topology fixed the type of the strings and their transcriptions to decimal system. Three differential equations were numerically integrated in convolution to simulate the evolution of the system, one of them referred to those strings converted to decimal signatures. We used Maple and R language to perform the simulations. Results: Simulations showed attractors for different time intervals of iterations. For wide ranges of individual propensities to develop the six abilities described in the work it was observed that the dissimilarities of individual profiles induced attractors with narrow boundaries. Growing the number of individuals, this tendency was maintained. Conclusion: The study showed simulations performed on representations of academic systems consisting of researchers and professors interacting within a change-resistant environment, pointing out that these systems may evolve from chaotic configurations to stability, inducing well defined attractors.
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