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In recent times there has been a growing trend for more information and knowledge to enhance learning based on cognitive load theory with increased use of Educational Technology based on Web ICT with online education resources. There are also various instructional and expert models being developed for the adaptive e-learning mechanisms as required for effective learning based on Genetic, Neural Networks, Swarm Intelligence and other Bio inspired algorithms or Evolutionary algorithms. Computational Intelligence or Artificial Intelligence has also established new paradigms for Learning Systems in creating virtual class rooms in the physical absence of tutors. It is also possible to create virtual labs on which students that are registered can carry out their experiments from remote with Interactive Intelligent Tutoring Systems. The current paper will develop a problem model using Markov Chain and greedy algorithms for Tutor or Instructor –Student interactive model for virtual labs and virtual class rooms which will be built on the Deep Belief Network architecture as a novel approach for state-of-art courses related to Energy and Wireless Communication for Higher Education proposed as part of Inter University Research Groups. Further, a Bio inspired Differential Evolution algorithm will be deployed for the virtual labs and virtual class rooms to meet the specific learning requirements for the Instructor-Student interactions specific to this E-learning platform. The proposed study will help improve the learning environment to stimulate creativity and innovation as envisaged by the Ministry of Education under the Saudi Arabia Vision 2030 and will help in moving towards creating an international benchmark for deploying innovative E-learning mechanisms for Higher Education.

Developing Virtual Class Room Models with Bio Inspired Algorithms for E-Learning: A Survey for Higher Technical Education for Saudi Arabia Vision 2030