Search - al-hamadi...

This paper motivated by an interdisciplinary research work approach integrating multi-sensory cognitive learning theory with interesting issue of tutoring quality improvement. It is worthy to note that adopted approach has been inspired by analysis and evaluation of phonics methodology applied in teaching children "how to read?". Herein, quantitative evaluation of this issue performed by considering two computer aided learning (CAL) packages concerned with a specific mathematical topic namely long division process. Via realistic modeling of packages using Artificial Neural Networks (ANNs) based upon associative memory learning paradigm. In more details, at educational field practice ; both CAL packages have been applied for teaching children algorithmic steps performing long division processes. Moreover, learning performance evaluation of presented packages considers children outcomes' achievement after tutoring for suggested Mathematical Topic either with or without associated tutor's voice. Interestingly, statistical analysis of obtained educational case study results at children classrooms (for both applied packages) versus classical tutoring proved to be in well agreement with obtained after ANNs computer simulation results.

Neurological researchers have recently revealed their findings about increasingly common and sophisticated role of Artificial neural networks (ANNs). That applied for systematic realistic modeling of interdisciplinary discipline incorporating neuroscience, education, and cognitive sciences. Accordingly, ANN Models vary in relation to the nature of assigned brain functioning to be modeled. For example, as human learning takes place according to received stimuli that is simulated realistically through self-organization paradigm by artificial neural networks modeling. This piece of research adopts the conceptual approach of (ANN) models inspired by functioning of highly specialized biological neurons in reading brain based on the organization the brain's structures/substructures. Additionally, in accordance with the prevailing concept of individual intrinsic characterized properties of highly specialized neurons, presented models closely correspond to performance of these neurons for developing reading brain in a significant way. More specifically, introduced models concerned with their important role played in carrying out cognitive brain function' outcomes. The cognitive goal for reading brain is to translate that orthographic word-from into a spoken word (phonological word-form). In this context herein, the presented work illustrates via ANN simulation results: How ensembles of highly specialized neurons could be dynamically involved in performing the cognitive function of developing reading brain.

This piece of research addresses an interdisciplinary challenging issue concerned with dynamical evaluation of e-Learning systems' performance. More precisely, it presents an interdisciplinary work integrating neuronal, psychology, cognitive, and computer sciences into educational environment. That's in order to introduce systematic analysis and dynamical evaluation of the adopted study for e-learners' time response (equivalently convergence time) phenomenon. Specifically, this work concentrates on dynamical evaluation of one measuring parameter fore-learning performance namely: time response. In other words, e-learner's response time has been adopted as an appropriate candidate learning parameter applicable for reaching optimal analysis and evaluation of e-learning systems performance. Herein, that time considered as period of time requested in order to reach correctly a pre-assigned (desired) output answer which determined by an e-learner while examined via Multiple Choice Questions (MCQ). At the macro-level, the paper proposed e-learner's response time affected mostly by two basic extrinsic and intrinsic educational factors. Firstly, that associated to effectiveness of e-learning environment such as communication signal to noise ratio, and learning rate value. Secondly, that tightly coupled with gain factor candidates' brain function and structure (synapses, axons, and dendrites).Such as the number of dynamically contributing neurons, and the gain factor of neuronal response function. Consequently, Artificial Neural Networks (ANNs) simulation has been adopted for realistic evaluation of timely dependent candidate's response till reaching desired correct output solution for any arbitrary MCQ exam. After successful timely updating of dynamical state pattern (synaptic weight vector), pre-assigned (desired) correct response is accomplished in accordance with coincidence learning modeling. The presented simulation has been developed towards quantified analysis of the highly specialized neurons' role performed to select correct answers to MCQ. Furthermore, the time response parameter considers individual differences of learners' brain role (considering various number of neurons), while performing selectivity (MCQ) processes. Finally, after running of suggested realistic simulation programs, some interesting conclusive results introduced.