This chapter provides a case study in the development of 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.
|Title of host publication||Social media tools and platforms in learning environments|
|Editors||Bebo White, Irwin King, Philip Tsang|
|Place of Publication||Germany|
|Number of pages||14|
|Publication status||Published - 2011|
Cameron, D., Finlayson, A., & Wotzko, R. (2011). Visualising social computing output: Mapping student blogs and tweets. In B. White, I. King, & P. Tsang (Eds.), Social media tools and platforms in learning environments (pp. 337-350). Springer. https://doi.org/10.1007/978-3-642-20392-3