The week's pick
Random Articles
Reseach Article
Developing Virtual Class Room Models with Bio Inspired Algorithms for E-Learning: A Survey for Higher Technical Education for Saudi Arabia Vision 2030
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 8 |
| Year of Publication: 2017 |
| Authors: Khalid A. Fakeeh |
10.5120/ijais2017451709
|
Khalid A. Fakeeh . Developing Virtual Class Room Models with Bio Inspired Algorithms for E-Learning: A Survey for Higher Technical Education for Saudi Arabia Vision 2030. International Journal of Applied Information Systems. 12, 8 ( Nov 2017), 8-21. DOI=10.5120/ijais2017451709
Abstract
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.
References
- Moise, G., Gabriela, M., Loredana, N., & Alina, T. F. (2017). Bio-Inspired E-Learning Systems A Simulation Case: English Language Teaching A Simulation Case: English Language Teaching, (April). https://doi.org/10.5772/31268
- Al-asmari, A. M., & Khan, M. S. R. (2014). E-learning in Saudi Arabia: Past, present and future.
- Bose, S. (n.d.). I t s & e u e f, 1–26.
- Pratt, J., Hoult, E., Nairne, B., & Ashenden, S. (2011).Collaboration Between Universities.
- Adaptive learning. (n.d.).
- Delhi, N. (2016). Report on the N. V Varghese Sayantan Mandal Report on the N . V Varghese Sayantan Mandal.
- Britton, K., & Officer, P. (2016). Date?: I-Note Number: Contact?:, (June).
- Xu, L., Huang, D., & Tsai, W. (2014). Cloud Based Virtual Laboratory for Network, 57(3), 145–150.
- Oladimeji, I. W., Technology, A. I., & Polytechnic, O. S. (2016). International Journal of Science and Applied Information Technology Design of Cloud based E-learning System for Virtual Classroom, 5(1), 1–6.
- Ferreira, E., Ferreira, D., Kim, S., Siirtola, P.,Röning, J., Forlizzi, J. F., & Dey, A. K. (n.d.). Assessing real-time cognitive load based on psycho- physiological measures for younger and older adults.
- https://elearningindustry.com
- http://tincanapi.com/scorm-vs-the-tin-can-api/
- Technologies, N. (2015). A Hybrid Bioinspired Algorithm for Facial Emotion Recognition Using CSO-GA-PSO-SVM,472-477https://doi.org/10.1109/CSNT.2015.124
- Joseph D. Novak & Alberto J. Cañas (2006). "The Theory Underlying Concept Maps and How To Construct and Use Them", Institute for Human and Machine Cognition. Accessed 24 Nov 2008.
- G. Malini, T. Mala., & A. Kannan.(2016),Asian Journal of Information Technology 15(17):3280-3286. Markov Model Based Prediction for Effective E-content delivery in Cloud.
- Deeb, B. (2014). An Adaptive HMM Based Approach for Improving E-Learning Methods.
- Salehi, M. (2013). Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egyptian Informatics Journal, 14(1), 67–78. https://doi.org/10.1016/j.eij.2012.12.001
- Chandra, S. (n.d.). Creating the Knowledge based Economy in Kingdom of Saudi Arabia to Solve the Current Unemployment Crisis.
- The Observatory on borderless Higher Education (2006), (March), 1–4
- INTERNATIONAL COLLABORATION IN HIGHER EDUCATION: THE CANADIAN-UKRAINIAN CURRICULUM DEVELOPMENT PARTNERSHIP by Valentyna Kushnarenko A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Theory and Policy Studies in Education Ontario Institute for Studies in Education University of Toronto © Copyright by Valentyna Kushnarenko (2010) INTERNATIONAL COLLABORATION IN HIGHER EDUCATION: THE CANADIAN-UKRAINIAN CURRICULUM DEVELOPMENT PARTNERSHIP . De
- Mohanapriya, M. (2016). IOT enabled Future Smart Campus with effective E-Learning?: i-Campus, 3(4), 81–87. https://doi.org/10.5176/2251-3701
- Fakeeh, K. A. (2016). KSA 2030 Vision (Kingdom of Saudi Arabia’s 2030 Project) and its Focus on Families and Students, 149(1), 46–48.
- Achuthan, K., Nedungadi, P., Raman, R., & Nair, B. (n.d.). Complementing Education via Virtual Labs?: Implementation and Deployment of Remote Laboratories and Usage Analysis in South Indian Villages, 8–15.
- Hinton, G. E., & Osindero, S. (2006). A fast learning algorithm for deep belief nets *
- Kim, J. W. (n.d.). Classification with Deep Belief Networks.
Index Terms
Keywords