We examine a semi-automated approach to the analysis of data from MOOC discussion forums. Previous research had analysed a sample of discussion forum data and developed a manual analysis framework, however this process can be very time consuming, especially given the class size of some online courses. Therefore it is important to investigate appropriate and automated analysis techniques to improve timeliness of analysis and to reveal the topics that emerge from a semi-automated process. An analysis of a data set from a coding MOOC in 2015 using the automated Structural Topic Modeling (STM) technique in R is described and contrasted against a manual analysis conducted on a segment of data from the same course in 2014. The types of analyses available and the relevance to computing education research is highlighted, with a focus on providing a discussion of the contrasting capabilities of each approach. The aim is to enable computing education researchers to assess the relevance of these techniques for further work.
|Title of host publication||Proceedings|
|Subtitle of host publication||17th Koli Calling Conference on Computing Education Research - Koli Calling 2017|
|Editors||Calkin Suero Montero, Mike Joy|
|Place of Publication||New York NY USA|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||5|
|Publication status||Published - 16 Nov 2017|
|Event||Baltic Sea Conference on Computing Education Research 2017 - Koli, Finland|
Duration: 16 Nov 2017 → 19 Nov 2017
Conference number: 17th
|Conference||Baltic Sea Conference on Computing Education Research 2017|
|Abbreviated title||Koli Calling 2017|
|Period||16/11/17 → 19/11/17|
- Data analysis.
- Online discussion