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Kraker Peter, Wagner Claudia, Jeanquartier Fleur, Lindstaedt Stefanie

On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection

Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011), Springer, 2011

This paper presents an adaptable system for detecting trends based on the micro-blogging service Twitter, and sets out to explore to what extent such a tool can support researchers. Twitter has high uptake in the scientific community, but there is a need for a means of extracting the most important topics from a Twitter stream. There are too many tweets to read them all, and there is no organized way of keeping up with the backlog. Following the cues of visual analytics, we use visualizations to show both the temporal evolution of topics, and the relations between different topics. The Twitter Trend Detection was evaluated in the domain of Technology Enhanced Learning (TEL). The evaluation results indicate that our prototype supports trend detection but reveals the need for refined preprocessing, and further zooming and filtering facilities.

Beham Günter, Jeanquartier Fleur, Lindstaedt Stefanie

iAPOSDLE - An Approach to Mobile Work-Integrated Learning

Sustaining TEL: From Innovation to Learning and Practice, Proceedings of EC-TEL 2010, Wolpers, M., Kirschner, P. A., Scheffel, M., Lindstaedt, S. N., Dimitrova, V., Springer, 2010

This paper introduces iAPOSDLE, a mobile application enabling the use of work-integrated learning services without being limited by location. iAPOSDLE makes use of the APOSDLE WIL system for self-directed work-integrated learning support, and extends its range of application to mobile learning. Core features of iAPOSDLE are described and possible extensions are discussed.
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