Vol. 4 No. 2 (2017)

An Overview on Evaluation of E-Learning/Training Response Time Considering Artificial Neural Networks Modeling

Hassan M. H. Mustafa
Computer Engineering Department, Al-Baha Private College of Sciences Al-Baha, (KSA)
Fadhel Ben Tourkia
Computer Engineering Department, Al-Baha Private College of Sciences Al-Baha, (KSA)
Ramadan Mohamed Ramadan
Educational Psychology Department, Educational College Banha University, Egypt.

Published 2017-08-18

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  • Artificial neural network modeling, E-Learning performance evaluation, Synaptic connectivity, Ant colony system.

How to Cite

Mustafa, H. M. H., Tourkia, F. B., & Ramadan, R. M. (2017). An Overview on Evaluation of E-Learning/Training Response Time Considering Artificial Neural Networks Modeling. Journal of Education and E-Learning Research, 4(2), 46–62. https://doi.org/10.20448/journal.509.2017.42.46.62


The objective of this piece of research is to interpret and investigate systematically an observed brain functional phenomenon which associated with proceeding of e-learning processes. More specifically, this work addresses an interesting and challenging educational issue concerned with dynamical evaluation of e-learning performance considering convergence (response) time. That's based on an interdisciplinary recent approach named as Artificial Neural Networks (ANNs) modeling. Which incorporate Nero-physiology, educational psychology, cognitive, and learning sciences. Herein, adopted application of neural modeling results in realistic dynamical measurements of e-learners' response time performance parameter. Initially, it considers time evolution of learners' experienced acquired intelligence level during proceeding of learning / training process. In the context of neurobiological details, the state of synaptic connectivity pattern (weight vector) inside e-learner's brain-at any time instant-supposed to be presented as timely varying dependent parameter. The varying modified synaptic state expected to lead to obtain stored experience spontaneously as learner's output (answer). Obviously, obtained responsive learner's output is a resulting action to any arbitrary external input stimulus (question). So, as the initial brain state of synaptic connectivity pattern (vector) considered as pre-intelligence level measured parameter. Actually, obtained e-learner’s answer is compatibly consistent with modified state of internal / stored experienced level of intelligence. In other words, dynamical changes of brain synaptic pattern (weight vector) modify adaptively convergence time of learning processes, so as to reach desired answer. Additionally, introduced research work is motivated by some obtained results for performance evaluation of some neural system models concerned with convergence time of learning process. Moreover, this paper considers interpretation of interrelations among some other interesting results obtained by a set of previously published educational models. The interpretational evaluation and analysis for introduced models results in some applicable studies at educational field as well as medically promising treatment of learning disabilities. Finally, an interesting comparative analogy between performances of ANNs modeling versus Ant Colony System (ACS) optimization presented at the end of this paper.


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