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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Prof. Dr. Steve Thatcher
QUniversity, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good-quality papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the related fields.

IJIET 2016 Vol.6(12): 917-922 ISSN: 2010-3689
DOI: 10.7763/IJIET.2016.V6.817

On Comparative Analogy of Academic Performance Quality Regarding Noisy Learning Environment versus Non-properly Prepared Teachers Using Neural Networks' Modeling

Hassan M. Mustafa and Ayoub Al-Hamadi
Abstract—This piece of research presents analytical evaluation of comparative analogy between two educational phenomena considering academic performance (achievement) observed inside our classrooms. These two phenomena are: the effect of noisy learning environment on educational field academic achievement quality. In addition to the impact of interactive teaching by non-properly prepared teachers on academic performance.
The comparative analogy investigated herein, is presented systematically via adopting Artificial Neural Networks' (ANNs) modeling. By more details: noisy data considered as the main cause of environmental annoyance which negatively affects the quality of academic performance. Furthermore, considering student's learning ability problem faced by non-properly prepared teachers during their academic tutoring performance inside classrooms.
On the basis of the adopted supervised learning ANN model, two basic neural networks' parameters have been explicitly considered. That's while running of presented simulation program, both parameters are: learning rate value (η) and gain factor value (λ).

Index Terms—Artificial neural networks models, academic performance, signal to noise ratio.

Hassan M. Mustafa is with Computer Eng. Department, Faculty of Engineering. Al-Baha University (K.S.A). He is on leave from Educational Technology Department Faculty of Specific Education-Bana University Egypt (e-mail: prof.dr.hassanmoustafa@gmail.com).
Ayoub Al-Hamadi is with Institute for Electronics, Signal Processing and Communications (IESK) Otto-von-Guericke-University Magdeburg Germany (e-mail: Ayoub.Al-Hamadi@ovgu.de).


Cite: Hassan M. Mustafa and Ayoub Al-Hamadi, "On Comparative Analogy of Academic Performance Quality Regarding Noisy Learning Environment versus Non-properly Prepared Teachers Using Neural Networks' Modeling," International Journal of Information and Education Technology vol. 6, no. 12, pp. 917-922, 2016.

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