The rapid changes in modern knowledge, due to exponential growth of information sources, are complicating learners’ activity. For this reason, novel approaches are necessary to obtain suitable learning solutions able to generate efficient, personalized and flexible learning experiences. From this point of view, the use of different cooperative intelligent agents can be exploited to analyze learner’s preferences and generate high quality learning presentations which provide attractive learning solutions. In particular, to achieve this goal this paper exploits an ontological representation of the learning environment and an adaptive memetic algorithm based on a cooperative multiagent framework. In this framework different agents analyze the e-learning instance and solve it in a parallel way, cooperating among them. This cooperation is performed by jointly exploiting data mining, via fuzzy decision trees, together with a decision making framework exploiting fuzzy methodologies. As will be shown in the experimental results section, this multi-agent strategy is capable of speeding up the convergence to high-quality personalized e-learning experiences.

An Adaptive Multi-Agent Memetic System for Personalizing e-Learning Experiences

ACAMPORA, GIOVANNI;GAETA, Matteo;VITIELLO, AUTILIA
2011

Abstract

The rapid changes in modern knowledge, due to exponential growth of information sources, are complicating learners’ activity. For this reason, novel approaches are necessary to obtain suitable learning solutions able to generate efficient, personalized and flexible learning experiences. From this point of view, the use of different cooperative intelligent agents can be exploited to analyze learner’s preferences and generate high quality learning presentations which provide attractive learning solutions. In particular, to achieve this goal this paper exploits an ontological representation of the learning environment and an adaptive memetic algorithm based on a cooperative multiagent framework. In this framework different agents analyze the e-learning instance and solve it in a parallel way, cooperating among them. This cooperation is performed by jointly exploiting data mining, via fuzzy decision trees, together with a decision making framework exploiting fuzzy methodologies. As will be shown in the experimental results section, this multi-agent strategy is capable of speeding up the convergence to high-quality personalized e-learning experiences.
9781424473151
9781424473168
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/3030431
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 2
social impact