Understanding SEaM student comments from a Big(ger)Data perspective: what are students saying?

Ullmann, Thomas; Lay, Stephanie; Coughlan, Tim; Lister, Katharine; Cross, Simon; Rienties, Bart and Whitelock, Denise (2018). Understanding SEaM student comments from a Big(ger)Data perspective: what are students saying? In: CARLG 2018 Conference 18-19 June 2018 Conference Proceedings p. 39.

URL: http://www.open.ac.uk/blogs/CALRG/wp-content/uploa...

Abstract

Each year, students contribute tens of thousands of comments about their student experience via the Student Experience on a Module Survey (SEaM survey). There remains a challenge as to how best utilise this data effectively for understanding module performance and planning module revisions. This presentation takes a big data perspective analysing tens of thousands of comments of the OU wide administered SEaM survey. It uses automated empirical text analysis methods to detect hot topics students talk about during an academic year and it evaluates the sentiment that students express towards these topics. This presentation shows results from several lines of investigation that started in research about the automated detection of reflective keywords in writings (Ullmann, 2015c, 2017b)to its first feasibility study in the context of the Open University (QE PID 'Applicability of Natural Language Processing to analyse SEaM open comment data'), to its first application in the context of quality enhancement at the Open University, such as the SEaM comment analyses for WELS (Ullmann, 2015b, 2015a), in the context of widening access (Coughlan, Ullmann, & Lister, 2017), group tuition (Ullmann, 2017a), and the latest Data Wrangler Scholarly Insight Report Spring 2018.

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