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View all- Gronberg NKnutas AHynninen THujala M(2021)Palaute: An Online Text Mining Tool for Analyzing Written Student Course FeedbackIEEE Access10.1109/ACCESS.2021.31164259(134518-134529)Online publication date: 2021
Examining manual and semi-automated methods of analysing MOOC data for computing education
Authors: 
Michael Morgan, 
Aletta Nylén, Matthew Butler, Anna Eckerdal, 
Neena Thota, Päivi KinnunenAuthors Info & Claims
Pages 153 - 157
Abstract
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.
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- Examining manual and semi-automated methods of analysing MOOC data for computing education
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- Univ. Eastern Finland: University of Eastern Finland
- University of Warwick: University of Warwick
- Joensuu University Foundation: Joensuu University Foundation
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Published: 16 November 2017
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Koli Calling 2017
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- University of Warwick
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Koli Calling 2017: 17th Koli Calling International Conference on Computing Education Research
November 16 - 19, 2017
Koli, Finland
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View all- Gronberg NKnutas AHynninen THujala M(2021)Palaute: An Online Text Mining Tool for Analyzing Written Student Course FeedbackIEEE Access10.1109/ACCESS.2021.31164259(134518-134529)Online publication date: 2021
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