An adaptive HMM based approach for improving e-Learning methods

Deeb, Buthaina; Hassan, Zainuddin; Beseiso, Majdi

doi:10.1109/wccais.2014.6916638

Cited by 7 publications

(3 citation statements)

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“…Based on the transition probability matrix and the current request of the learner, the future request based on the Markov chain will be obtained and replicated to the user's site reducing the access time whether the predicted content is present or not. Further, in [16] for web based interaction for delivering the resources related to e-learning is based on a 3-phase model which includes determining the learning style using the VAK model (which is Visual, Auditory and Kinesthetic learning), use the Kcluster algorithm for identifying the learning style and initializing the Hidden Markov Model for the content delivery using the content delivery network (CDN) to create an effective adaptive learning system and has been deployed using the following steps: …”

Section: Figure 14: Markov Model Based Prediction Methods For Elearninmentioning

confidence: 99%

“…Here, the learner will be associated with a cluster that is minimum distance to its score w.r.t centroid of the cluster. In [16] Here, D ij is the minimal distance between the learner i from the centroid of the cluster. Multiple iterations of the algorithm is computed where after assigning cluster for each student a new centroid for each cluster is calculated.…”

Section: International Journal Of Applied Information Systems (Ijais)mentioning

confidence: 99%

“…An architecture for determining the learning needs has also been used for the result and analysis in [16] at Figure 15 as follows: The K-means algorithm currently deployed to model learners/students in the VAK model can also be replaced and deployed using the fitness function from the evolutionary algorithm for optimized intelligent content delivery under the markov process chain during scalability issues during specific learning requirements with dynamic observation sequences.…”

Section: International Journal Of Applied Information Systems (Ijais)mentioning

confidence: 99%

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

Fakeeh¹

2017

IJAIS

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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.

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Section: Figure 14: Markov Model Based Prediction Methods For Elearninmentioning

confidence: 99%

Section: International Journal Of Applied Information Systems (Ijais)mentioning

confidence: 99%

Section: International Journal Of Applied Information Systems (Ijais)mentioning

confidence: 99%

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