Category: Huddersfield Uni

I promised I’d dig a bit deeper into the book data, so here goes!
We have seven academic schools in the university, so I thought it would be interesting to see how the range of titles broke down by each school. As previously noted, the borrowing patterns seem to have changed at the end of 2005/start of 2006, so here’s the percentage change for the two periods…

academic school       average range of titles borrowed    % change
                            2000-2005        2006-2008
Music, Humanities & Media      16,760           20,468      122.1%
Business                        9,431           11,402      120.9%
Computing & Engineering         7,033            6,771       96.3%
Education                      12,485           11,909       95.4%
Human & Health Sciences        16,427           20,274      123.4%
Applied Sciences                7,356            7,562      102.8%
Art, Design & Architecture      9,361           12,309      131.4%

So, first of all, the increase in range of titles being borrowed isn’t across the board. I knew Computing & Engineering borrowing had been in decline for a number of years, but I’m surprised to see that the same applies for Education. Applied Sciences has stayed pretty much the same, but the other 4 schools have seen sizeable increases in the range of titles being borrowed.
The Art & Design section of the library was revamped in 2005, so it could be that we’ve seen an increase in the number of students using the library and that has driven the increased borrowing since then for that school.
A few of the comments suggested that loans per borrower would be a useful metric. Unfortunately I don’t have the data for the total number of students in each school per year, so I’m using the total number of active borrowers instead…

academic school      average loans per active borrower    % change
                            2000-2005        2006-2008
Music, Humanities & Media        26.1             25.7       98.6%
Business                         10.2             12.3      121.4%
Computing & Engineering           8.3              7.7       93.6%
Education                        15.1             14.0       92.8%
Human & Health Sciences          15.3             18.8      122.6%
Applied Sciences                 11.8             13.3      112.1%
Art, Design & Architecture       10.6             10.4       98.4%

Again a decline in Computing and Education. Art & Design and Music & Humanities have remained pretty much the same. The other 3 schools have seen an increase in the number of loans per active borrower.
One final set of data — the number of active borrowers per school…

academic school    average active borrowers per school    % change
                                2000-2005    2006-2008
Music, Humanities & Media           1,537        1,976      128.5%
Business                            2,557        2,963      115.8%
Computing & Engineering             1,650        1,527       92.5%
Education                           1,526        1,988      130.3%
Human & Health Sciences             3,587        4,581      127.7%
Applied Sciences                    1,267        1,243       98.1%
Art, Design & Architecture          1,621        2,332      143.9%

It looks like there are a couple of things going on here…
1) In the last 3 years, the number of active borrowers (i.e. users who have borrowed at least one item) has increased. In the period 2000-2004, the total number of active student borrowers was relatively static (around 14,000) and since 2005 it’s been on the increase (with just over 17,000 in 2008).
2) Overall, there’s an increase in the average number of books borrowed per active borrower, primarily driven by the two schools with the highest number of active borrowers (Business and Human & Health). The increases in those two schools more than offsets the decreases seen in a couple of the other schools (Computing and Education).
At a time when some other UK academic libraries have reported a decrease in borrowing, both of the above are good news for our library. I’ll need to go back to the SCONUL stats to check, but I don’t think we’ve seen much of an increase in book stock in the last decade (I suspect it might actually have decreased).
So, can we actually say anything about the impact of serendipity? If we look in more depth at the average number of books borrowed per active borrower per year for all students, we get this…

…which closely resembles the original graph from the first post showing the range of unique titles borrowed per year…

…and the number of active borrowers per year also shows a similar trend…

It’s obvious that there’s a driver in there somewhere which has caused the average number of loans per active borrower to increase since 2005. Hand-in-hand there’s been a similar increases in the range of stock that’s being borrowed and the number of active borrowers.
As more people use the library, one would perhaps expect the range of stock being borrowed to increase. However, would you also expect the average number of loans per borrower to increase (bearing in mind that the stock levels have probably not increased and may have actually decreased during that period)?
I’m still not entirely sure I’ve shown that adding serendipity to an OPAC increases the range of stock being borrowed (that’s probably more influenced by the number of active borrowers), but there may well be a link with the average number of books loaned to each borrower.
Now, to change the topic, here’s one final graph that I included in the UKSG presentation — it shows the number of clicks per month on the books in the OPAC’s virtual shelf browser…

…seeing as this was just an experimental feature that added a bit of “book cover eye candy” to the OPAC, I’m amazed how heavily it’s being used. Whilst fixing one of our dedicated catalogue PCs in the library on Friday, I noticed that a student was carrying out a search, then picking a relevant search result, then using the shelf browser to look at all of the nearby books. And to think I’m usually dismissive of the benefits of browsing within OPACs 😀

In my UKSG presentation, I briefly touched on the need for library services (perhaps the OPAC, but perhaps not) to start joining users together in the same way that sites like Facebook do.
In the same way that a “people who borrowed this, also borrowed…” service starts exposing the hidden links between items on shelves, I think we need to start finding the connections between our users.
Using circulation data, we can start to locate clusters of users who’ve borrowed the same books. In an academic environment, these may be students who are studying on the same course. However, what if we discovered that two separate courses being run in different parts of the university had a strong overlap in borrowing? Would value be gained from introducing those students to each other?
No sooner had I tweeted that I was thinking about this kind of thing, Tony Hirst sent a response…

…a library dating service, then? Heh heh 😉

I’m keen to know what your first reaction to Tony’s comment is!
What if you were a lonely researcher who wanted to find someone similar to yourself, in order to collaborate on a project? By mining the circulation data and/or OpenURL article access data, a library could find your ideal partner — someone who’d been looking at the same books and resources that you’d been using. If libraries were aggregating their usage data at a national level, that perfect partner could well be a researcher at another institution.
To test this out, I tweaked our “people who borrowed this” code to generate the links between users (rather than the books). As an aside, I’ve been trying all day to figure out what the user equivalent of “people who borrowed this, also borrowed…” is, but haven’t been able to wrap my head around the logical linguistics of it!
Data Protection obviously means that I can share that prototype with you, but it did throw up some interesting results. For my partner Bryony, her closest match was one of her colleagues who works in the same department as her — they both share similar craft related interests, so have borrowed similar books. However, what if her closest match was someone working in another department? Maybe they’d want to meet up over a coffee and swap crafty ideas.
I also tried the same for one of my colleagues, who’s a lecturer, and found that his ideal match is himself! Or rather, the closest match for his current library account (as a member of staff) was his old library account from when he was a student. In other words, since becoming a lecturer, he’s re-borrowed quite a few of the books he used as a student.
Although I can’t show you the data for individuals, we can step back a level and look at the borrowing at the course level. I’ve put together a quick and dirty prototype to play with. The prototype will pick a course at random and then show the courses that have the closest matches in terms of book borrowing — if you’re unlucky and get an empty list (i.e. no matches were found), try refreshing the page.
Taking the BSC Applied Criminology course as an example — 59.3% of the books borrowed by students on that course were also borrowed by students on the BSC Behavioural Sciences course (HB100). The other top matches all seem to be related to criminology: psychology, social work, police studies, child protection, probation work, etc. However, there also appears to be some synergy with books borrowed by midwifery, history and hospitality management students.
I’ll try and add some extra code in tomorrow to show what the most popular books are that inhabit those course intersections.