Abstract. Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved-may assist students in the learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.
Learning Objects (LOs) are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.
The aim of this paper is to propose a customized recommendation model of educational resources for virtual courses that incorporates the benefits of ubiquitous computing and intelligent agents. This model is intended to provide relevant and personalized information to students about their virtual courses planning, online assessment, search and retrieval of learning objects. The methodology used for the construction of the system is MAS-CommonKADS which offers useful models for the phases of conceptualization, analysis and design expressed through artifacts provided by the Agent Unified Modeling Language (AUML). The prototype developed exhibits intelligent proactive and deliberative agents that allow the search and recommendation of information adapted to the student's profile. To validate the system a testing phase on a learning scenario was performed which demonstrate the effectiveness of using this kind of technology within ubiquitous virtual learning environments.
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