Usage-based Clustering of Learning Objects for Recommendation
Marc-Andre Orthmann, Martin Friedrich, Uwe Kirschenmann, Katja Niemann, Maren Scheffel, Hans-Christian Schmitz, Martin Wolpers · 2011
The growing amount of available information on the internet makes the process of filtering appropriate information an increasing challenge. Because currently existing approaches provide insufficient results in many cases, we propose a new way of relating objects based on their usage. We assume that objects which are significantly often used in the same session are semantically related. Thus, we build a usage-based relatedness graph, apply a graph-based clustering algorithm and evaluate the results with respect to semantic similarity measures. Our approach takes the learning domain into special consideration, its evaluation is performed within the Learning Object Repository MACE.