Big data, higher education and the paradox of size

I appear to be one of the last people still around at work, so I’m remedying that by taking a couple of weeks leave, but I thought it might be useful to explore one of the aspects of new technology that doesn’t always crop in our discussions, namely big data.

This is the time of year when lots of data becomes available to British universities: profiles of new students, student satisfaction, competitor analysis and the rest. I’m seeing more of it than before as I get ready to step up to my new role next month, but it’s all stuff that has really taken off in recent years across the sector.

From a managerial perspective, this is all really useful, pulling together lots of different strands to highlight general trends and patterns that might otherwise be missed. An article in The Guardian this week showcased several very interesting uses of data within institutions to identify issues, sometimes even before they became issues. Some of those uses are ones that I would want to find out more about and see how we could use them here.

However, I have to note a certain ambivalence on my part here.

Last week, I had the pleasure of meeting one of my counterparts from Reading University, which is about 20 miles away from here. Emma is a regular reader of this blog (hello there!) and we discussed some of the things we each do. One of the things that became evident was that we work with rather different sized groups of students.

For us, the luxury of small group sizes means that we can do a lot more in the way of individualised support and direction than would be possible with the kinds of numbers that Emma has to look after. True, the pay-off is that there are some things that we can’t do, because we’re too small – including this MUN module, which has been very positively received by students – but the basic approach has to be different.

What does this have to do with big data?

The issue is essentially one of perspective. Big data tells me to look at high levels of aggregation to see what works, but my practice tells me to focus on individuals. I know that when I get a percentage figure for ‘Politics’ on some report, I know that each student will count for a noticeable percentage individually, and I’ll have a good idea who the students are with any problems and why.

Perhaps the resolution of this is to marry up the two approaches: take the lessons of big data and aggregation, but then apply them in a moderated fashion, suitable to the needs of our students. Indeed, I would hope that we would do this for all data – big or not – if we are to be truly student-focused and -led in our teaching.

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