The need of a common environment where to share information and knowledge is of particular interest in the field of special education not only to support the access to a large amount of available information (along with the ability to derive value from this information) but also to foster synergistic actions involving different special education operators. In this paper we present the results of a research aimed at defining a Web-based environment for special education providing, to operators of the field, personalized information and digital assets covering both their expressed and latent information needs. Offered personalization features are based on the definition and the implementation of a hybrid recommender system based on a mix of cognitive and collaborative approaches, the first based on the similarities among digital objects, the latter leveraging on similarities among user profiles. By combining these two approaches the system is able to provide meaningful but not obvious recommendations with a fair level of serendipity. The encouraging results of an experimentation with real users are also reported.

A Web-Based Knowledge Hub for Special and Inclusive Education

Capuano, N;Mangione, GR
2015-01-01

Abstract

The need of a common environment where to share information and knowledge is of particular interest in the field of special education not only to support the access to a large amount of available information (along with the ability to derive value from this information) but also to foster synergistic actions involving different special education operators. In this paper we present the results of a research aimed at defining a Web-based environment for special education providing, to operators of the field, personalized information and digital assets covering both their expressed and latent information needs. Offered personalization features are based on the definition and the implementation of a hybrid recommender system based on a mix of cognitive and collaborative approaches, the first based on the similarities among digital objects, the latter leveraging on similarities among user profiles. By combining these two approaches the system is able to provide meaningful but not obvious recommendations with a fair level of serendipity. The encouraging results of an experimentation with real users are also reported.
2015
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4863357
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