On Artificial Neural Networks’ Modelling of Non-Properly Prepared Teachers’ Implication on Students’ Academic Performance, Adopting Noisy Contaminated Optical Character Recognition (OCR)

This research looks into a multidisciplinary, intriguing, and crucial educational topic. The adopted phenomenon is inextricably linked to the educational environment’s clarity, which has an impact on the amplification and illumination of learning/teaching performance. It focuses on the serious problem of ill-prepared teachers negatively impacting pupils’ learning performance (achievement) in the classroom. The signal to noise ratio, a well-known communication phrase, is mapped to the unfavourable quantity of improperness. In the context of communication technology, this term is abbreviated as SNR or S/N, and it measures the clarity of the received intended signal over the transmission channel. The suggested Artificial Neural Network (ANN) model uses a feed forward (FF) structure that follows the Kohonen learning law, while bits training to recognise three figures with (T, H, and L) forms utilising (3X3) retina. Several intriguing results were achieved after executing the suggested realistic simulation software. For example, consider the relationship between the learning rate parameter h and the Gaussian additive noise power s in learning data submitted by a sloppy teacher. Furthermore, these characteristics have an impact on students’ learning achievement and convergence (response time) The parallel between learning in a noisy data environment in Artificial Neural Networks models and the effect of the physical environment on the quality of teaching in classrooms is demonstrated in this paper.

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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