Application of Artificial Neural Networks Modeling for Evaluation of E-Learning/Training Convergence Time

Abstract

This paper addresses the challenging issue of how to evaluate dynamically learning performance of E-learners' convergence time on the basis of recently adopted interdisciplinary trend. Objectively, this work investigates systematically and interprets realistically some of observed brain functions’ educational phenomena while E-learning process proceeds. Herein, ANNs modeling is adopted for realistic measurements of an E-learning performance parameter. More specifically, this parameter considers timely changes of learners' intelligence level before and during learning / training process. At any time instant, the state of synaptic connectivity pattern inside E-learner's brain supposed to be presented as timely dependent weight vector. This synaptic state expected to lead to obtaining spontaneously some 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 measuring 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, some interesting remarks illustrating comparative analogy between learning performance at neural systems and cooperative learning at Ant Colony System (ACS) are presented.