Abstract:

The effects of the physical learning environment in classrooms includes three distinct effective factors namely: noisy level in classes, overcrowded classroom space, and housing and neighborhood quality. Specifically, this work explores pupils' ability to listen to, and follow, one speaker in the presence of others. More precisely, it considers investigational answer for a challenging question: How students could focus on teachers' interactive speaking in noisy crowd environment?. When discussing the auditory system it is important to understand the difference between the physical mechanism of the ear and the central auditory nervous system in the brain responsible for processing auditory information [1]. Commonly, this process experienced as following one speaker in the presence of another. Such common experience, we may take it for granted as called: “the cocktail party problem” CPP. It can be trivial experienced process for a normal human (student) listener. From a neurological P.O.V., sounds all enter the ear as one cacophonous roar, but the brain processes all the information and tunes into one sound, such as a person’s voice, and filters out the rest [2]. Interestingly, referring to brain functions and anatomical structure, sound and light are processed by different receptors and neural pathways in the brain. However, by considering current knowledge of how auditory and visual stimuli sensations are responding to sound and light respectively. They are represented in the nervous system in similar complexity and that undergo with similar initial processing by the nervous system [3]. Furthermore, by referring to findings announced after some experimental work , the results published therein at [3] have implicitly declared that auditory and visual short term memory employ similar mechanisms. Consequently, modeling of Artificial Neural Networks(ANNs) has been adopted for realistic simulation for students' selective attention in overcrowded classrooms. Therefore, an ANN unsupervised model has been suggested herein, to measure performance of selective attention and recognition for visual signal specifically optical character recognition (OCR) subjected to various contaminating noisy levels (Signal to noise ratios). Finally, obtained simulation results declared the effect of Neural Network's parameres' relation between extrinsic {various noisy levels (corresponding learning rate values)} and intrinsic{individual students' differences (gain values)} factors on recognition and selective attention performances. Additionally, presented obtained findings proved to be in well agreement with recently published results considering the dealing with noisy environmental learning problem [4].

References:
[1] Helena Karabulut "The Neuro-Building Blocks of Learning: Improving School Readiness and Overcoming Learning Difficulties" Journal of Education and Future, year: 2013, issue: 4, pp.1 – 15 .
[2] http://abcnews.go.com/blogs/health/2012/04/18/the-cocktail-party-effect-how-we-tune-in-to-one-person-at-a-loud-party/
[3] http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.0050056
[4] Mustafa H, et al. "On Quantified Evaluation Of Noisy Data Impact On Children's Mental Development Using Artificial Neural Networks" Published at ICERI2013, the 6th International Conference of Education, Research and Innovation held in Seville (Spain), on the 18th, 19th and 20th of November, 2013.

Keywords:

Artificial neural network modeling, noisy crowded learning environment, signal to noise ratio, cocktail party problem.