An Ontological Approach for Memetic Optimization in Personalised E-Learning Scenarios
Giovanni Acampora, Matteo Gaeta, Vincenzo Loia · 2008
Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. Our proposal attempts to deal with these additional features by exploiting an ontological representations of learning environment and a memetic approach of optimization, integrated into a cooperative distributed problem solving framework. In detail, this paper describes a novel multi-island memetic approach managing a collection of models and processes for adapting an e-learning system to the learner expectations and to formulate objectives in a effective and dynamic intelligent way.