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Clustering by usage: Higher order co-occurrences of learning objects

: Niemann, K.; Schmitz, H.-C.; Kirschenmann, U.; Wolpers, M.; Schmidt, A.; Krones, T.


Dawson, S.:
LAK '12. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
New York: ACM, 2012
ISBN: 978-1-4503-1111-3
International Conference on Learning Analytics and Knowledge (LAK) <2, 2012, Vancouver>
Conference Paper
Fraunhofer FIT ()

In this paper, we introduce a new way of detecting semantic similarities between learning objects by analyzing their usage in a web portal. Our approach does not rely on the content of the learning objects or on the relations between the users and the learning objects but on usage-based relations between the objects themselves. The technique we apply for calculating higher order co-occurrences to create semantically homogenous clusters of data objects is taken from corpus driven lexicology where it is used to cluster words. We expect the members of a higher order co-occurrence class to be similar according to their content and present the evaluations of that assumption using two teaching and learning systems.