Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Building bridges within learning communities through ontologies and "thematic objects"
CSCL '05 Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
CSCL'07 Proceedings of the 8th iternational conference on Computer supported collaborative learning
An event-based framework for characterizing the evolutionary behavior of interaction graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Tracking the Evolution of Communities in Dynamic Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Matchballs --- a multi-agent-system for ontology-based collaborative learning games
CRIWG'12 Proceedings of the 18th international conference on Collaboration and Technology
Analyzing the flow of ideas and profiles of contributors in an open learning community
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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This paper presents an analysis of resource access patterns in a recently conducted master level university course. The specialty of the course was that it followed a new teaching approach by providing additional learning resources such as wikis, self-tests and videos. To gain deeper insights into the usage of the provided learning material we have built dynamic bipartite student -- resource networks based on event logs of resource access. These networks are analysed using methods adapted from social network analysis. In particular we uncover bipartite clusters of students and resources in those networks and propose a method to identify patterns and traces of their evolution over time.