Hierarchical optimization of personalized experiences for e-Learning systems through evolutionary models
Recent researches in e-Learning area highlight the need to define novel and advanced support mechanism for commercial and academic organizations in order to enhance the skills of employees and students and, consequently, to increase the overall competitiveness in the new economy world. This is due to the unbelievable velocity and volatility of modern knowledge that require novel learning methods which are able to offer additional support features as efficiency, task relevance and personalization. This paper tries to deal with these features by proposing an adaptive e-Learning framework based on Computational Intelligence methodologies by supporting e-Learning systems designers in two different aspects: (1) they represent the most suitable solution, able to support learning content and activities, personalized to specific needs and influenced by specific preferences of the learner and (2) they assist designers with computationally efficient methods to develop in time e-Learning environments. Our work attempts to achieve both results by exploiting an ontological representation of learning environment and a hierarchical memetic approach of optimization. In detail, our approach takes advantage of a collection of ontological models and processes for adapting an e-Learning system to the learner expectations by efficiently solving a well-defined optimization problem, through a hierarchical multi-cores memetic approach.
e-learning systems memetics algorithms distributed evolutionary computation