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This paper presents an analysis of resource access patterns in two recently conducted online courses. One of these has been a master level university lecture taught as a blended learning course with a wide range of online learning activities and materials, including collaborative wikis, self-tests, and thematic videos. The other course has been offered in the form of a MOOC. As a specialty of this course, master level students from two different universities could participate as a regular university class and receive credits for successful completion. In both courses, online learning resources such as videos, scientific literature, and wikis played a central role. In this context, the motivation for our research was to investigate characteristic patterns of resource usage of the learners. In order to gain deeper insights into the usage of learning materials, we have adapted methods from social network analysis and applied them to dynamic bipartite student-resource networks built from event logs of the students' resource access. In particular, we describe the clustering of students and resources in such networks and propose a method to identify patterns of the cluster evolution over time.

In this paper we will demonstrate the potential of processing and visualising the dynamics of computermediated communities by means of Social Network Analysis. According to the fact that computer-mediated community systems are manifested also as structured data, we use data structures like e-mail, discussion boards, and bibliography sources for an automatic transformation into social network data formats. Currently our developed converter DMD (Data Multiplexer Demultiplexer) supports GraphML, UCINET, and Pajek formats besides our own data formats which are used for real-time analysis of CSCL (Computer Supported Collaborative Learning) activities. In the case of communication data our converters utilize conversation graphs reflecting aspects of speech act and conversational theory to produce directed graphs in the cases where one-mode person networks are desired.The paper will demonstrate a 3-dimensional visualisation of an author community based on Bibtex bibliography data converted into GraphML. Based on this dataset we visualise publications network with a tool called Weaver, which is developed in our research group. According to Lothar Krempel's algorithm, Weaver uses the first two dimensions to embed the network structure within a common solution space. The third dimension is used for representing the time axis and thus the dynamics of co-authorship relations.Concluding we aim to discuss potential issues and problems of our approach and the possibilities especially concerning the appropriate visualisation and segmentation of long term communications, such as mailing lists.

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 studentresource 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.

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