Good news! We have acquired some extra data from various very helpful departments at Huddersfield, which allows us to explore a few additional angles for both indicators of usage and the relationship between usage and outcomes.  The new data relates to the UCAS points of the students within the study, and to the percentage of each student’s total e-resource usage that occurs on campus.

Let’s start with the UCAS points. We’ve treated these as a kind of ‘demographic’ characteristic and, as with the ones we’ve already posted about, we wanted to see whether there was a correlation between them and the library usage variables. Because both the UCAS points and the usage data are continuous variables, but not normally distributed, we used a slightly different measure than in our previous work – Spearman’s correlation coefficient. Figure 1 shows the findings.

I’ve only included effect sizes for the ones that were statistically significant and, as you can see, there aren’t many of them. And even for those that were, the effect sizes were pretty small. This is an interesting finding, suggesting that it’s not necessarily the case that high-achieving students are simply more likely to use the library and that this lies behind the relationship we’ve seen between usage and outcomes in phase 1 of the project – we’d have to run further tests to check whether this is in fact the case, of course.

Next, we moved on to look at the percentage of e-resource usage which occurred on Huddersfield’s campuses (as opposed to offsite – for example, at home or in a coffee shop). Again, as with the earlier measures of usage, we wanted to see whether this varied with some of the demographic variables. Figure 2 shows our findings: I’ve only included the ones with statistically significant variations.

Younger students and men spend proportionally more time than mature students and women accessing e-resources on campus (as compared to other locations). The same is true for Asian and black students, compared to white students;  for computing and engineering students compared to social scientists; and for students based in the UK compared to those based in China. Remember, these are just the ones with statistically significant differences, so in fact there are not that many – and they are all small effect sizes.

Finally – and perhaps most interestingly – the relationship between percentage of usage of e-resources on campus and final degree results. Figure 3 shows the findings from this analysis; again, only the statistically significant results are shown.

You can see that, although the effect sizes are small, there are differences between the percentage of on-campus usage for students who go on to achieve a First and 2.ii or Third, and a 2.i and a 2.ii. In each case, the students who go on to achieve higher grades are the ones who have had a lower percentage of on-campus e-resource usage. This tells us that there is some relationship between reading electronic content in locations other than the university and doing well in your degree.