Abstract:

The Web is one of the most important information media and it is influencing in the development of other media, as for example, newspapers, journals, books, libraries, etc. Consequently, in last years has led to a proliferation of personalized services, such as recommender systems, that have been developed to provide the users with relevant information, according with their preferences or needs.

These advances in Web technologies are promoting too the development of new pedagogic models. These models, that complement the present education, are known as e-learning. The new technologies improve the teaching-learning processes, aiding the information broadcasting in an efficient and easy manner, and providing tools for the personal and global communications that allow encourage the collaborative learning.

Digital libraries could help to carry out this aim. Digital libraries are the logical extensions of physical libraries in the electronic information society. These extensions amplify existing resources and services. As such, digital libraries offer new levels of access to broader audiences of users and new opportunities for the library. In practice, a digital library makes its contents and services accessible remotely through networks such as the Web or limited-access intranets. Concretely, University Digital Libraries provide information resources and services to students, faculty and staff in an environment that supports learning, teaching and research. Moreover we can use the connectivity inherent in digital libraries to support collaborative filtering, where users rate or add value to information objects and these ratings are shared with a large community, so that popular items can be easily located or people can search for objects found useful by others with similar profiles.

In this paper we propose a fuzzy linguistic recommender system to facilitate learners the access to e-learning resources interesting for them. Suggesting didactic resources according to the learner’s specific needs, a meaning learning is encouraged, influencing directly in the teaching-learning process. The system allows a personalized automatic dissemination of learning resources relevant to the students (bibliography, exercises, Web links, slides, and so on). Moreover the system helps to discover collaboration possibilities with other learners and to form multi-disciplinar groups to encourage the tutorial action and collaborative learning.

We combine a recommender system, to filter out the information, with a multi-granular Fuzzy Linguistic Modeling, to represent and handle flexible by means of linguistic labels. The system is oriented to students and it is able to recommend three types of resources: Resources of the course to achieve the student specialization; other resources as complementary formation; partners or collaborators, in order to include other learners that could be interesting to discover collaboration possibilities and to form multi-disciplinar work groups. The system filters the incoming information stream and delivers it to the suitable students according to their skill levels.

The proposed system has been validated with the pupils of the course “Introduction to the Computer Sciences” developed in the Economic Faculty in University of Jaén. We show results that indicate the satisfactory performance of our system.

Keywords:

recommender systems, fuzzy linguistic modeling, university digital libraries.