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Examining manual and semi-automated methods of analysing MOOC data for computing education

Published: 16 November 2017 Publication History

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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  • (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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  1. Examining manual and semi-automated methods of analysing MOOC data for computing education

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      cover image ACM Other conferences
      Koli Calling '17: Proceedings of the 17th Koli Calling International Conference on Computing Education Research
      November 2017
      215 pages
      ISBN:9781450353014
      DOI:10.1145/3141880
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 16 November 2017

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      Author Tags

      1. MOOC
      2. data analysis
      3. online discussion
      4. programming

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      Koli Calling 2017
      Sponsor:
      • Univ. Eastern Finland
      • University of Warwick
      • Joensuu University Foundation

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      • (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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