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ANALOGIC NON-PROPERLY PREPARED TEACHERS VERSUS NOISY CONTAMINATED OPTICAL CHARACTER RECOGNITION REGARDING STUDENTS’ ACADEMIC PERFORMANCE, ADOPTING ARTIFICIAL NEURAL NETWORKS’ MODELING

Hassan M. H. Mustafa1, Mohamed I. A. Ibrahim2, Hany S. Ramzy3

Abstract: This Research papers tackles an important and interestingly complex, and a challenging educational problematic phenomenon. Specifically, it addresses two analogously interrelated issues namely: the non-properly prepared teachers that characterized by undesirable impact on students' academic achievement inside classrooms. Additionally, Herein, this issue shown to be analogous to recognition process of noisy contaminated Optical Character Recognition (OCR). Briefly, this comparative study objectively illustrates analogous relationship between contaminated noisy information provided by non-properly prepared teaching process versus noisy contaminated (OCR) process By more details, various noisy power level values which changed in learning environment, results in considerable correspondence with different learning rate values. The unfavorable amount of teacher’s improperness is mapped similar to well-known communication technology term namely signal to noise (S/N) ratio. Which quantitatively measures the clarity degree related to received desired learning / teaching signal across the educational communication channel. In other words, it illustrates simulated outcome presented as percentage of lessons’ focusing degree versus # Neurons for different learning rate values. More properly. Performance of non-properly prepared teacher results in noisy information submitted to children’s brain in classrooms. Accordingly. it observed annoyance of learning environment and negatively affects the quality of children’s learning performance. Herein, this research work illustrates specifically the analogy between learning under noisy data environment in Artificial Neural Networks (ANNS) models versus the effect of physical environment on quality of education in classrooms. The observed non-properly prepared teachers' phenomenon in classrooms observed to have negatively undesired effect on the evaluated educational process performance. Analogously, the observed effect of additively contaminating noise power on any of map size made with the resolution of (3x3) pixels. These pixels were associated to diverse three English clear characters (T&L, or H) which originally written over (3x3) binary (black & white) digitized retina. Herein; obtained interesting findings shaded light over more complex challenging research directions towards in future more elaborated investigational study for such interdisciplinary observed educational phenomena.
Keywords: Artificial Neural Networks / contaminated / models / children / noisy / properly prepared teachers

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