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In collaborative filtering recommender systems, there is little room for users to get involved in the choice of their peer group. It leaves users defenseless against various spamming or ''shilling'' attacks. Other social Web-based systems, however, allow users to self-select peers and build a social network. We argue that users' self-defined social networks(More)
In this paper, we present an open architecture that combines different SQL learning tools in an integrated Exploratorium for database courses. The integrated Exploratorium provides a unique learning environment that allows database students to take complimentary advantages of multiple advanced learning tools.
In a recent study, we discovered a new effect of adaptive navigation support in the context of E-learning: the ability to motivate student to work more with non-mandatory educational content. The results presented in this paper extend the limits of our earlier findings. We describe the implementation of adaptive navigation support for the SQL domain, and(More)
Rich, interactive eLearning tools receive a lot of attention nowadays from both practitioners and researchers. However, broader dissemination of these tools is hindered by the technical difficulties of their integration into existing platforms. This article explores the technical and conceptual problems of using several interactive educational tools in the(More)
This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the(More)
With the growth of adaptive educational systems available for students, semantic integration of user modeling information from these systems is emerging into an important practical task. Ontologies can serve as the major representational framework for such integration. However, not all adaptive systems rely on ontologies for representing domain knowledge.(More)
This paper aims to examine whether users' watching networks can improve collaborative filtering-based recommendations (CF). Watching networks are established by users upon their perceived usefulness or interests about other users' information collections. The networks do not require mutual agreement between a watching party and a watched party. The typical(More)