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 elearning performance considering convergence (response) time. That's based on an interdisciplinary recent approach named as Artificial Neural Networks (ANNs) modeling. Which incorporate Nerophysiology, 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 elearner'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.
This piece of research introduces an investigational systematic study of an interdisciplinary challenging phenomenon observed in natural world. Interestingly, this study belongs to the two emerging fields of nature-inspired computing (NIC) and computational intelligence (CI) with focusing on the physics-and biology-based approaches and algorithms .Herein, by more details this article concerned with the conceptual analysis and evaluation of quantified learning creativity phenomenon via simulation and modeling of two diverse natural biological systems (human & nonhuman creatures). More precisely, it is associated to diverse aspects of measurable behavioral learning performance of both biological systems. Therefore, this paper introduces comparative analogy between two diverse biological behavioral systems considering quantification of learning creativity. Referring to, the definition of Swarm intelligence which considered as a relatively new discipline that deals with the study of self-organizing processes both in nature and in artificial systems. Researchers in ethology and animal behavior have proposed many models to explain interesting aspects of social insect behavior such as self-organization and shape-formation. Accordingly, the presented study observed during human interactive tutoring/learning processes with natural environment. Versus ecological behavioral learning of swarm intelligence agents (Ants), while performing foraging process. Systematic investigational study of quantified human learning creativity phenomenon is an interdisciplinary, challenging, and interesting educational issue. At education field practice (classrooms) , while face to face tutoring sessions are performed, learning creativity phenomenon is detectable via bidirectional feedback between teacher and pupil. In short, this research work adopts comparative study of simulation and modeling for educational creativity issue considering two disciplinary approaches are namely: swarm intelligence, and neural networks. Both simulated realistically for systematic investigational modeling of creatures' creativity phenomenon observed in nature. Presented creativity models mainly consider observed behavioral learning of ant colony system in addition to in field educational classrooms. Conclusively, presented results herein, for both swarm intelligence and neural networks models seemed to be well promising for future more elaborate, systematic, and innovative research in evaluation of human learning creativity phenomenon regarding (NIC) and (CI).
This piece of research addresses an interesting comparative analytical study, which considers two concepts of diverse algorithmic computational intelligent paradigms related tightly with Neural and Non-Neural Systems' modeling. The first computational paradigm was concerned with practically obtained psycho-learning behavioral results after three animals' neural modeling. These are namely: Pavlov's, and Thorndike's experimental work. In addition, the third model is concerned with optimal solution of reconstruction problem reached by a mouse's movement inside Figure 8 maze. Conversely, second algorithmic intelligent paradigm was originated from observed activities' results after Non-Neural bio-inspired clever modeling namely Ant Colony System (ACS). These results were obtained after attaining optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance was shown to be similar for both introduced systems. Finally, performances of both intelligent learning paradigms have been shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application was for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.
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