Dynamical evaluation Of academic performance in e-learning systems using neural networks modeling (time response approach)

This paper explores a relatively new methodological approach for the field integrating learning and education, with other research areas, such as neurobiological, cognitive, and computational sciences. Specifically, presented work is an interdisciplinary piece of research aiming to simulate appropriately a challenging and critical issue concerned with academic performance in e-learning systems. Namely, considering face to face tutoring phenomenon observed while an interactive e-learning process is performed. Referring to strong interest announced by educationalists to know how neurons´ synapses inside the brain are interconnected. Together to perform communication processing among brain regions. Herein, a special attention has been developed towards dynamical academic evaluation of timely based brain learning via face to face (FTF) interactive tutoring. In other words, this piece of research presents an interdisciplinary realistic dynamic investigation. For academic performance phenomenon associated with e-learners´ contribution as time response performed human´s brain neuronal function. Accordingly, Artificial Neural Networks (ANN^S) have been adopted for realistic modeling of academic performance evaluation based on timely dependant student´s response till attaining learning convergence (desired output). After running of designed realistic simulation program, some interesting results have been presented. Interestingly, individual differences´ phenomenon observed via after statistical analysis of obtained simulation results.