e-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. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way. © 2008 IEEE.
Optimizing learning path selection through memetic algorithms / Acampora, Giovanni; Gaeta, Matteo; Loia, Vincenzo; Ritrovato, Pierluigi; Salerno, Saverio. - (2008), pp. 3869-3875. (Intervento presentato al convegno 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008)) [10.1109/IJCNN.2008.4634354].
Optimizing learning path selection through memetic algorithms
Acampora Giovanni;
2008
Abstract
e-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. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way. © 2008 IEEE.File | Dimensione | Formato | |
---|---|---|---|
Optimizing learning path selection through memetic algorithms.pdf
non disponibili
Tipologia:
Documento in Post-print
Licenza:
Accesso privato/ristretto
Dimensione
522.44 kB
Formato
Adobe PDF
|
522.44 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.