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Photograph of documents from ALTCA quick trip to Manchester yesterday to take part in a Symposium at ALT-C  on ‘Big Data and Learning Analytics’ with colleagues from the OU (Simon Buckingham Shum, Rebecca Ferguson, Naomi Jeffrey and Kevin Mayles) and Sheila MacNeill from JISC CETIS (who has blogged about the session here).

It was the first time I’d been to ALT-C and it was just a flying visit on the last morning of the conference so I didn’t get the full ALT-C experience.  But I got the impression of a really big conference, well-organised and with lots of different types of sessions going on.  There were 10 sessions taking place at the time we were on, including talks from invited speakers.  So lots of choice of what to see.

But we had a good attendance at the session and there seemed a good mix of people and a good debate and questions during the symposium.  Trying to both summarise an area like Learning Analytics and also give people an idea of the range of activities that are going on is tricky in a one-hour symposium but hopefully gave enough of an idea of some of the work taking place and some of the issues and concerns that there are.

Cross-over with other areas
Sheila had a slide pointing out the overlaps between the Customer Relationship Management systems world, Business Intelligence and Learning Analytics, and it struck me that there’s also another group in the Activity Data world that crosses over.  Much of the work I mentioned (RISE and Huddersfield’s fanstastic work on Library impact)  came out of JISC’s Activity Data funding stream and some of the synthesis project work has been ‘synthesised’ into a website ‘Exploiting activity data in the academic environment’ Many of the lessons learnt that are listed here, particularly around what you can do with the data, are equally relevant to Learning Analytics.  JISC are also producing an Activity Data report in the near future.

Interesting questions
A lot of the questions in the session were as much around the ethics as the practicality.   Particularly interesting was the idea that there were risks of Learning Analytics in encouraging a view that so much could be boiled down to a set of statistics, which sounded a bit like norms to me. The sense-making element seems to be really key, as with so much data and statistics work.

I’d talked a bit about also being able to use the data to make recommendations, something we had experimented with in the RISE project. It was interesting to hear views about the dangers of them reducing rather than expanding choice by narrowing the choices as people are encouraged to select from a list of recommendations which reinforces the recommendations leading to a loop.  If you are making recommendations based on what people on a course looked at then I’d agree that it is a risk, especially as I think there’s a huge probability that people are often going to be looking at resources that they have to look at for their course anyway.

When it comes to other types of recommendations (such as people looking at this article also viewed this other article, and people searching for this search term look at these items) then there is still some chance of recommendations reinforcing a narrow range of content, but I’d suggest that there is still some chance of serendipitous discovery of material that you might not ordinarily have seen.  I’m aware that we’ve very much scratched the surface with recommendations and used simple algorithms that were designed around the idea that the more people who viewed that pattern the better the recommendation.  But it may be that more complex algorithms that throw in some ‘randomness’ might be useful.

One of the elements I think that is useful about the concept of recommendations is that people largely accept them (and perhaps expect them) as they are ubiquitous in sites like Amazon.  And I wonder if you could almost consider them as a personalisation feature that indicates that your service is modern and up-to-date and is engaging with users.  For many library systems that still look to be old-fashioned and ‘librarian’-orientated then perhaps it is equally important to be seen to have these types of features as standard.

Update: Slides from the introductory presentation are here

When I was looking for an image to start off the RISE presentation I tried to find an image that visualised the challenge and issues that users face in finding and accessing library resources. So the phrase ‘needle in a haystack’ came to mind, particularly as the first slide was about the difficulties that users found coping with the old federated search tools we used in 2009. And I found a great image to use.

The intended metaphor was that the difficulty of finding resources was the ‘needle in a haystack’, but the image of the oversized needle also seemed to me to be symbolic of our aim of improving our systems.  So introducing a new discovery system and creating a system through RISE to provide activity data-driven recommendations would make the process of finding library resources easier for users.

It’s probably a good thing that there is a person on the right of the image to give a sense of scale.  Otherwise you could intrepret it differently. The needle isn’t oversized at all, but is normal size. And it’s the haystack that has shrunk as library budgets shrink and our haystack (the licensed content) shrinks in size. But maybe that is rather too pessimistic?

New activity data project
I blogged back in November on some reflections on preparing and submitting a funding bid.  Well, we’ve recently received word that we are one of eight projects to be funded as part of the JISC programme and we’re really pleased to be successful.  It’s a fairly short six month project but is working in an area that I find particularly interesting, that of user activity data and how it can (and should be used) to improve services.

Our new project RISE is part of the JISC Activity Data programme and the projects cover quite a wide range of data from VLEs, library systems and other corporate systems so it will be really interesting to see what comes out of them.  We’re going to be looking at data from our ezproxy system which we use as the primary method of getting students and staff access to licenced library resources.   As most of our use is remote (unsurprisingly as I work at a distance learning institution) it’s a critical system for us.  We’ve recently put in place a new aggregated search system (Ebsco Discovery Solution) and we are going to explore how we can use our ezproxy search data to provide recommendations to searchers and see what difference it makes to their behaviour.  Using data to provide recommendations are an everyday part of commercial life and taken for granted if you’re a regular user of services such as Amazon.  But libraries (with notable exceptions such as Huddersfield) have been strangely reluctant to exploit the rich store of activity data that they can collect to improve the user experience.

RISE gives us the opportunity to see what we can do with the data in terms of making recommendations, what the challenges are, what we can’t do with it, and whether it is of interest to anyone else.  It’s going to be an interesting few month. RISE will shortly have it’s own project blog and twitter tag #ourise and I’m aiming to blog some personal reflections and notes on how we get on here.

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July 2020

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