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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/923155

Title
Visualising social computing output: mapping student blogs and tweets
Author/Creator
Cameron, David;  Finlayson, Amalie;  Wotzko, Rebecca
Description
This chapter provides a case study in the development of a data mining approach to assess blogging and microblogging (‘tweets’) in a higher education setting. Data mining is the use of computational algorithms to analyse large datasets, and this chapter describes the use of the Leximancer software tool to perform a conceptual analysis of the blogs and tweets published by students in an undergraduate course about social media. A Leximancer analysis is represented visually as a ‘concept map’ showing the relationships between the concepts and ideas drawn out of the data automatically, rather than using predefined terms and keywords. In this chapter, Leximancer is used to produce a concept map of the student blogs and tweets to enhance the evaluation of conceptual understanding of the syllabus, as well as more general observations about the use of these social media tools in higher education. This suggests a possible approach to analysing the potentially large volume of text-based information that can be produced by students in these social computing settings.
Relation
Social Media Tools and Platforms in Learning Environments p. 337-350
Publisher Link
http://dx.doi.org/10.1007/978-3-642-20392-3_20
Date
2011
Publisher
Springer
Keyword(s)
social media;  journalism;  communication;  education;  Twitter;  blogging
Resource Type
book chapter
Rights
The final publication is available at www.springerlink.com
Identifier
http://hdl.handle.net/1959.13/923155
Identifier
ISBN:9783642203916
Language
eng
 
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"Visualising social computing output: mapping student blogs and tweets"
 
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