ABSTRACT
This paper considered the increasingly sophisticated role of artificial neural networks (ANNs) after its applications at the interdisciplinary discipline incorporating neuroscience, education, and cognitive sciences. Recently, those applications have resulted in some interesting findings which recognized and adopted by neurological, educational, in addition to linguistic researchers. Accordingly,ANNmodels vary in relation to the nature of assigned brain functioning to be modeled. For example, as human learning that takes place autonomously according to received stimuli that are realistically simulated through self-organization modeling. This paper adopts the conceptual approach of (ANN) models inspired by functioning of highly specialized biological neurons specified in reading brain based on the organization the brain’s structures/substructures. Additionally, in accordance with the prevailing concept of individual intrinsic characterized properties of highly specialized neurons, presented models closely correspond to performance of these neurons for developing reading brain in a significant way. More specifically, introducedANNmodels herein concerned with the importance of reading brain’s cognitive goal in fulfillment of enhanced academic achievement. That’s to translate visualized (orthographic word-from) into a spoken voiced word (phonological word-form). In this context, the presented work illustrates viaANNsimulation and practical obtained results: How ensembles of highly specialized neurons could be dynamically involved in performing the cognitive function of developing reading brain. In more details, this paper presents an interdisciplinary approach adopting a fairly realistic approach of comparative academic performance assessment of two diverse educational methodologies. More specifically, this piece of research aims to improve conventional (classical) academic performance of Teaching How to Read Arabic Language using Methodology via application of a designed Computer Based Learning module. That’s shown to be in well agreement likewise the Artificial Neural Network (ANN), associative memories theories, cognitive multimedia, and classical conditioning. More specifically, coincidence detection learning process has been adopted for evaluation of brain reading performance. Interestingly, presented comparative study originated from the children’s brain response time till reaching learning process convergence that is mapped into academic achievement (outcome mark) values. Accordingly, there response time has been adopted as an appropriate ANN’s candidate parameter for assessment of both educational methodologies. Moreover, analysis of students’ individual differences has been presented after reaching desired output (correct) answer.