We’re moving on in this second of our final blogs to look at the relationship between discipline and usage. There are some interesting – although not necessarily unexpected – findings here, and bigger effect sizes than we saw with the demographic variables. Again, I’ve only shown the differences that are statistically significant: effect sizes are highlighted by the depth of colour, with small effects pale, medium effects a little darker, and large effects very dark.

You might remember that we had to aggregate some of the demographic categories – such as ethnicity and country of origin – both to protect student confidentiality and to get meaningful results. The same is true of our discipline-based analysis. But, because we suspected that this was going to be quite an important area, we’ve decided to go a bit more granular. So, we’ve created two levels of analysis. Each of the 100-odd courses offered by Huddersfield to its full-time, undergraduate students has been classified into one of 17 ‘clusters’. These clusters have then been aggregated to form six ‘groups’. We can compare the groups to see some overarching differences, and then drill down to compare clusters within groups, to get a more detailed understanding.

It’s important to mention that the grouping work was done by librarians and student support staff, so it represents the relationships that they see between the different courses. I suspect that if we’d asked course tutors, lecturers or students themselves we might have seen slightly different combinations. Also, the grouping was slightly driven by numbers: we had to make sure that there were enough people in each category to make the statistical tests viable and to ensure that anonymity was protected.

Limitations duly mentioned, let’s go on to look at the findings. First, the aggregated subject groups. We used the social science group as our control, as it was the biggest. As you can see from Figure 1, it is also the higher user in most comparisons where significant differences exist: the only exceptions are the comparisons with the number of items borrowed and the number of e-resources accessed by health students. We think this might be because health students are often out on placements, limiting their opportunities to visit the library but making e-resources more important. Furthermore, e-resource use is heavily embedded into the nursing curriculum, and most students will have classes which require them to go into the library’s e-resources and search for items.

Figure 1: Aggregated subject groups

The overall takeaway from this figure, I think, is that computing and engineering students are less intensive users on a number of dimensions, with a medium effect for the number of items borrowed. Arts students are very low users compared to the social sciences, with medium effects on several dimensions.

Let’s move on now to look at how the smaller clusters relate to each other within the groups. Poor old science was in a group all by itself: it wasn’t possible to sub-divide this one so we’ll skip over it and look at health. This has been divided into nursing and other health disciplines (including subjects such as sports therapy, physiotherapy, podiatry and occupational therapy) and you can see from Figure 2 that nursing is a bigger user on pretty much every dimension. There’s a large effect for the number of e-resources accessed, suggesting that health students are reading more widely than their nursing counterparts. This might be because nurses are required to use a certain number of documents for some of their assignments – ten pieces of a specific kind of research for systematic reviews, or four journal articles for an early assignment; the wide use might represent their efforts to find exactly the right kind of resource. There are also medium-sized effects for library visits, hours logged into e-resources, number of e-resources accessed 5 or more times and number of PDF downloads and small effects for hours logged into library PCs and number of e-resources accessed 25 or more times; in each case, nurses are the higher users.

Figure 2: Health group

The computing and engineering group has been divided into – errr – computing and engineering! The differences here are fewer and smaller. Perhaps unsurprisingly, engineers use the library PCs more often – presumably the computing students are happily tapping away on their personal laptops. And computing students are downloading more PDFs. But on the whole, the behaviour of these two clusters is quite similar.

Figure 3: Computing and engineering group

 

Now we move onto the humanities group, which has been subdivided into three clusters: English, drama and media and journalism. The first thing to note is that there are no statistically significant differences between English and drama students. But there are differences between these two clusters and the media students, and where those differences exist the media students have the lower level of usage. Most of these differences relate to e-resource use: the English students have higher use on pretty much all the e-resource dimensions with medium-sized effects. The differences between media and drama are smaller.

Figure 4: Humanities group

Now, on to Figure 5 and the social science group, which looks colourful and complicated! The first thing to note is the number of cells which are shaded in dark colours: there are a lot of big effects within this group. Overall, behavioural sciences dominate. They have higher usage, at a statistically significant level, than every other cluster on at least one dimension. Business is the next-most-dominant discipline, although it’s worth noting that business students borrow fewer items than their colleagues in every other discipline except law, and that the effect sizes here are medium or large. Lawyers, in fact, have the lowest use compared to most subjects, but they do use the library, and especially its computers, more than their counterparts in social work and education. Finally, there are no significant differences between social work and education: perhaps it’s unsurprising that these two vocational courses have similar patterns of usage.

Figure 5: Social science group

Finally, we turn to the arts group. This one might have been skewed a bit by the inclusion of music, which contains a couple of courses which might have fitted alongside English and drama in the humanities group as well as more technology-focused subjects which fit with the design courses. Musicians are heavier users than every other cluster on at least four dimensions. They borrow more items than all of the other clusters, in each case with a large effect. They are also higher users of electronic resources, particularly when compared to the 2D and 3D designers. Elsewhere, there are fewer significant differences. Architects do not visit the library very often compared to the other clusters in this group, but fashion designers do. It’s possible that the architects’ relatively low level of use is because they have a ‘Design Centre’ in the department, which offers access to computers, journals and other materials: the ‘Art and Design Resource area’ in the library has traditionally been more focused on textiles and fashion design, and might explain their higher number of library visits. And, as a final footnote, 3D designers are fond of e-resources.

Figure 6: Arts group

The main finding from this section of the analysis – that discipline has a big effect on patterns of library usage – might not be earth shattering. But it does provide statistical backing for something that many librarians will already know anecdotally or from their own observations. This could be a really useful starting point for conversations with academics – checking whether the low usage by their students is a cause for concern and, if so, what might be done to increase it.