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Capuano N.,Centro Of Ricerca In Matematica Pura Ed Applicata | Gaeta M.,University of Salerno | Ritrovato P.,University of Salerno | Salerno S.,University of Salerno
Computers in Human Behavior | Year: 2014

The aim of a recommender system is to estimate the relevance of a set of objects belonging to a given domain, starting from the information available about users and objects. Adaptive e-learning systems are able to automatically generate personalized learning experiences starting from a learner profile and a set of target learning goals. Starting form research results of these fields we defined a methodology and developed a software prototype able to recommend learning goals and to generate learning experiences for learners using an adaptive e-learning system. The prototype has been integrated within IWT: an existing commercial solution for personalized e-learning and experimented in a graduate computer science course. © 2013 Elsevier Ltd. All rights reserved.


Acampora G.,University of Salerno | Gaeta M.,Centro Of Ricerca In Matematica Pura Ed Applicata | Munoz E.,European Center for Soft Computing | Vitiello A.,University of Salerno
IEEE International Conference on Fuzzy Systems | Year: 2011

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 multi-agent 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. © 2011 IEEE.


Gaeta M.,University of Salerno | Gaeta M.,Centro Of Ricerca In Matematica Pura Ed Applicata | Ritrovato P.,University of Salerno | Ritrovato P.,Centro Of Ricerca In Matematica Pura Ed Applicata | Talia D.,University of Calabria
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans | Year: 2011

Nowadays, we are witnesses of a transformation in the e-learning arena. This transformation has different drivers involving all the actors in the learning value chain, from final users to learning institutions through technology providers. All those actors share a common goal: making the learning processes more effective through the information and communication technologies. This is happening through the promotion of a paradigm shift from content-centered to process-centered solutions. In this paper, we present the results from the European Learning Grid Infrastructure project concerning models, processes, and services supported by a service-oriented software architecture for creating dynamic and adaptive virtual organizations for learning using Grid technologies. © 2011 IEEE.


Capuano N.,Centro Of Ricerca In Matematica Pura Ed Applicata | Iannone R.,Centro Of Ricerca In Matematica Pura Ed Applicata | Gaeta M.,Centro Of Ricerca In Matematica Pura Ed Applicata | Gaeta M.,University of Salerno | And 4 more authors.
Communications in Computer and Information Science | Year: 2013

The aim of a recommender system is to estimate the utility of a set of objects belonging to a given domain, starting from the information available about users and objects. Adaptive e-learning systems are able to automatically generate personalized learning experiences starting from a learner profile and a set of target learning goals. Starting form research results of these fields we defined a methodology to recommend learning goals and to generate learning experiences for learners of an adaptive e-learning system. © Springer-Verlag Berlin Heidelberg 2013.

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