Exploring e-Learning Knowledge through Ontological Memetic Agents

E-Learning systems have proven to be fundamental in several area of tertiary education and in business companies where lifelong learning is a must. There are many significant advantages for the people who learns online as convenience, portability, flexibility and costs. However, the remarkable velocity and volatility of modern knowledge, due to the exponential growth of World Wide Web, require novel learning methods that offer additional features as information structuring, efficiency, task relevance and personalization. The paper proposes a novel multi-agent e-Learning system empowered with (ontological) knowledge representation and memetic agents in order to efficiently manage complex and unstructured information that characterizes e-Learning environments. In particular, different from other similar approaches, our proposal uses 1) ontologies to provide a suitable method for modeling knowledge about learning content and activities, and 2) memetic agents as intelligent explorers in order to create “in time” and personalized e-Learning experiences that satisfy learners’ specific preferences. The proposed method has been tested by realizing a multi-agent software plug-in for an industrial e-Learning platform and performing an on-field experimentation that validates our memetic proposal in terms of flexibility, efficiency and interoperability