On Analysis and Evaluation of Early Math Giftedness Prediction, Regarding Children’s Individual Differences, and Mind Brain Education Science (Neural Networks’ Modeling Approach

أ.م.د/ حسن محمد حسن


ABSTRACT This paper adopted the concept that that learning process viewed as three layered system resulting in effective pedagogy in children’s classrooms while discovery therein any mathematically gifted individual child. In more detailed words, that system suggested to be decomposed of human brain, personality psychology, and mathematical classroom pedagogy. Interestingly, it is known that analysis and evaluation of Mathematical giftedness observed at children classrooms is a challenging and complex educational phenomenon. Consequently, the process of early mathematically gifted childhood discovery of any individual child revealing his high capabilities’ level in mathematics -while dealing with some math assigned problem-is an interesting and challenging transdisciplinary educational research issue. Herein, this piece of research focuses on quantitative analysis and evaluation of Mathematical giftedness considering individual children’s differences. provided different response time. I.E. regarding learning convergence time response of gifted children is very effective and efficient in completing their educational assignments faster. Specifically, this research work adopted Artificial Neural Networks (ANNS) for modeling of prediction of early math gifted children. That modeling approach covers two discipline folds namely as follows :1) educational neuroscience may considered as the study of "educational brain hardware” (organizational tissues and structure) It includes number of neural cells in addition to the ratio variation between the numbers of neurons and glial cell. That’s besides 2) educational psychology considered as the study of the brain operating system It operates with the memory notions, intelligence, information processing, comprehension level, etc. Herein, the math learning process is simulated using self-organized (autonomous) ANN model based on Hebbian rule paradigm. Moreover, individual differences’ prediction of math gifted children is measured via the response convergence time after reaching correct answer of a long division math process. Interestingly, this presented work agrees well with the latest research in the science of Mind, Brain and Education (MBE) and the six principles of that approach. are available to help 21st Century teachers and learners achieve success. Finally, results obtained after running realistic simulation programs shown to be in agreement with practically obtained results at our case study. Conclusively, as an extension of this transdisciplinary research work in the future, studies should investigate the factors discovered to be correlated with early math giftedness in a more specific manner and determine exactly how individual factors may contribute to gifted math ability. Consequently, this research work resulted in highly recommendation for application of suggested novel trend aiming to improve children’s mathematical giftedness in accordance with the math giftedness factors.

الكلمات الدلالية

Neural Networks Modeling, Mind Brain Education Science, Long Division Process, Children Mathematical Giftedness, Individual Differences, Synaptic Plasticity.

Shareon Facebook