On Thursday & Friday I was in Manchester for a 2 day HEA summit on big data in the social sciences. The event focussed on how social scientists could engage with big data, and the implications of that for teaching and learning (and capacity building). This of course can be seen in the context of claims (e.g. by Chris Anderson) of ‘the end of theory’ in the age of big data and to some extent an increasing pressure for all to engage in quantitative analysis, and of course an increasing availability of tools (and data sources) to conduct such analysis.
We kicked off with Rebecca Eynon giving reminders of some of the pitfalls of big data and a naivity around regarding ‘big data’ as ‘complete data’ – which reminded me of the provocations for big data (and my provocations for learning analytics post).
Next up we had Jon Whittle from Lancaster, talking about co-design and community engagement through data, http://www.catalystproject.org.uk/ with some nice examples of data projects to save/make money including ‘Clasp‘, a stress-ball (or other personal item) type device for those on the autistic-spectrum to monitor anxiety through phone-sync and (if setup) contact someone in a support network. I first saw this sort of approach in LSE’s ‘mappiness‘ app, and while I think some of them are a bit gimicky and I’m not sure what you’d do with the data I still like the idea and think event sampling, etc. has huge potential in mobile apps. Jon also noted the approach they were taking: PROTEE, “a methodology that aims to increase the reflexivity of research and innovation projects by helping to sensitise practitioners to the demarcations their projects enact, and to think through how these may affect the relevance of the outcomes”, which I think is very salient for current learning analytics research.
Third on was Ali Fisher talking about work on counter-terrorism and data analysis (through the eyes of a historian). Two key messages from Ali’s presentation were 1) the desire to get people to the stage of asking the most important question in data analysis: “Is it possible to […]?”, and 2) that the only way we can hope to make sense of the deluge of information is by understanding the meaning making signs – semiotics. Another interesting point was re: physical, versus digital location – we might be physically separate from a location, but by digital means still be a “digital insider” (e.g., if we’re tweeting outside of an event, but hugely connected to on the ground happenings).
The second day we had more discussion, with some interesting discussion of tools and resources including the UK Data Service, which holds social science datasets for research and teaching purposes, and a set of tools and resources Farida Vis uses on her Sheffield iSchool/Journalism MA course in data analysis, for example: https://discovertext.com/.
I also did my talk on the second day, based on the update of the LAK13 paper for the Journal of Learning Analytics (here). Having not thought I’d get to ‘return’ to that presentation post-conference, it was nice to think about how to update it and use it as a partial ‘introduction to learning analytics’. I’m also incredibly grateful to the reviewer who suggested the ‘interpretive flexibility’ paper, as we made much greater use of this notion in the journal version, and it is obvious that policy/practice context of technology use is absolutely fundamental (e.g. mini-whiteboards for formative assessment and collaboration, v. traditional whiteboards for chalk n talk).
The HEA is interested in big data in the context of curriculum design, innovative pedagogy, and student and staff transitions. And this focus is interesting for big data, given the difficulties, for example of writing for multiple genres of journal, and being a computer scientist doing social science or vice versa. This is something I’ve thought a bit about around the narratives of ‘what am I doing a PhD in?‘ and the issue in my subject of ‘what does a learning analytics PhD look like?’ is an interesting one.
This is particularly problematic given the need for interdisciplinary (not just multi-disciplinary) support, and the ways in which the REF mitigates against this. For students too, this is an issue: How do we a) support, and b) assess, interdisciplinary work – particularly so that we’re fair in comparison to students with only a single skill set/discipline? These aren’t easy questions to answer or solve, but certainly consideration of departmental and funding structures – and the support of subject centre organisations – play a role in moving things forwards.