Since the project began, I’ve been thinking about all the issues surrounding our hypothesis, and the kind of things we’ll need to consider as we go through our data collection and analysis.
For anyone who doesn’t know, the project hypothesis states that:
“There is a statistically significant correlation across a number of universities between library activity data and student attainment”
The first obvious thing here is that we realise there are other factors in attainment! We do know that the library is only one piece in the jigsaw that makes a difference to what kind of grades students achieve. However, we do feel we’ll find a correlation in there somewhere (ideally a positive one!). Having thought about it beyond a basic level of “let’s find out”, the more I pondered, the more extra considerations leapt to mind!
Do we need to look at module level or overall degree? There are all kinds of things that can happen that are module specific, so students may not be required to produce work that would link into library resources, but still need to submit something for marking. Some modules may be based purely on their own reflection or creativity. Would those be significant enough to need noting in overall results? Probably not, but some degrees may have more of these types of modules than others, so could be worth remembering.
My next thought was how much library resource usage counts as supportive for attainment. Depending on the course, students may only need a small amount of material to achieve high grades. Students on health sciences/medicine courses at Huddersfield are asked to work a lot at evidence based assignments, which would mean a lot of searching through university subscribed electronic resources, whereas a student on a history course might prefer to find primary sources outside of our subscriptions.
On top of these, there all kinds of confounding factors that may play with how we interpret our results:
- What happens if a student transfers courses or universities, and we can’t identify that?
- What if teaching facilities in some buildings are poor and have an impact on student learning/grades?
- Maybe a university has facilities other than the library through the library gates and so skews footfall statistics?
- How much usage of the library facilities is for socialising rather than studying?
- Certain groups of students may have an impact on data, such as distance learners and placement students, international students, or students with any personal specific needs. For example some students may be more likely to use one specific kind of resource a lot out of necessity. Will they be of a large enough number to skew results?
- Some student groups are paid to attend courses and may have more incentive to participate in information literacy related elements e.g. nurses, who have information literacy classes with lots of access to e-resources as a compulsory part of their studies.
A key thing emerging here is that lots of resource access doesn’t always mean quality use of materials, critical thinking, good writing skills… And even after all this we need to think about sample sizes – our samples are self-selected, and involve varying sizes of universities with various access routes to resources. Will these differences between institutions be a factor as well?
All we can do for now is take note of these and remember them when we start getting data back, but for now I set to thinking about how I’d revise the hypothesis if we could do it again, with a what is admittedly a tiny percentage of these issues considered within it:
“There is a statistically significant correlation between library activity and student attainment at the point of final degree result”
So it considers library usage overall, degree result overall, and a lot of other factors to think about while we work on our data!