Artificial Neural Networks’ Application for Comparative Recognitional Study of Children Correctly Pronounced Reading Arabic Words Associated with Two Diversified Educational Methodologies
The function of artificial neural networks (ANNs) has become increasingly sophisticated as a result of their applications in interdisciplinary disciplines such as neuroscience, education, and cognitive sciences. Recently, such applications have yielded some intriguing results that have been recognised and embraced by neuroscientists, educators, and linguists. As a result, ANN models differ depending on the type of brain activity to be modelled. As an example, consider human learning, which occurs autonomously in response to stimuli that are realistically simulated using selforganization modelling. This paper takes a conceptual approach to (ANN) models that is inspired by the functioning of highly specialised biological neurons specified in the reading brain based on how the brain’s structures/substructures are organised. Furthermore, the presented models closely correspond to the output of these neurons for developing the reading brain in a significant way, in accordance with the prevalent definition of individual intrinsically characterised properties of highly specialised neurons. More precisely, the ANN models presented herein are concerned with the role of the reading brain’s cognitive target in achieving improved academic achievement. That is, to convert a visualised (orthographic word from) voiced word (phonological word form). In this sense, a neural network is a group of highly specialised neurons within the human brain. The presented work demonstrates how ensembles of highly specialised neurons could be dynamically involved in performing the cognitive role of the developing reading brain using ANN simulation and realistic results. In more depth, this paper provides an interdisciplinary approach to student performance evaluation that is based on a reasonably objective comparison of two separate educational methodologies. This study aims to enhance traditional (classical) academic success in Teaching How to Read Arabic Language Using Techniques through the use of a specially developed Computer Based Learning module. The Artificial Neural Network (ANN), associative memory theories, cognitive multimedia, and classical conditioning have all been shown to be in strong agreement. For the purpose of evaluating brain reading results, the coincidence detection learning process has been used. The current comparative research was inspired by the children’s brain reaction time before they reached learning process convergence, which is then mapped into academic achievement (outcome mark) values. As a consequence, the response time of both educational methodologies has been adopted as an acceptable ANN’s candidate parameter for evaluation. Furthermore, after achieving the desired output (correct) response, an examination of students’ individual differences was presented.
Author (s) Details
Hassan M. H. Mustafa
Department of Educational Technology, Banha University, Egypt.
Mohamed I. A. Ibrahim
Department of Early Childhood and Education, Banha University, Egypt.
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