It’s been a long, long time since the last blog. Sorry! We have been busy – more of which later. But for now, I’d like to share some initial results from the work we’ve been doing looking at time-of-day of usage and outcome.

Previous work by Manchester Metropolitan University suggested that overnight usage is linked to poor grades for students. This makes intuitive sense: surely it can’t be a good sign if someone based in the UK is using library resources at 3am on a regular basis – that looks like someone who is struggling with their workload. We wanted to test whether this finding would hold true with the Huddersfield data.

At first glance it seemed like it might. Look at this graph, showing the e-resource usage patterns for students with all four grade outcomes over a 24 hour period*. The firsts have much higher usage than anyone else during the daytime; at night, although the timings become similar, the students who achieved thirds seem to have the highest use (around 4-5am).

But that’s not really a fair comparison. We know from the Phase 1 work that there’s a positive relationship between usage and outcome – in other words, the higher your grade, the higher your usage. So maybe what the first graph is showing is just that people with firsts have higher usage – full stop. The difference isn’t related to time of day at all.

To test this, we created a new variable, looking at the proportion of usage that happened within a certain hour, measured as a percentage of overall usage. This eliminates any bias which might emerge by considering overall usage. For example, a student who logged in to e-resources 20 times, 2 of them between 10-11am would have the same figure (10%) as a student who logged into e-resources 400 times, 40 of them between 10-11am. The much higher overall number of the second student becomes irrelevant, and we’re able to look at a true reflection of usage patterns. The following graph shows what happens.

What a difference! The lines are almost identical. Again, you can see that there’s a point around 9pm where users who go on to achieve a third overtake users who go onto achieve a first, and they maintain their dominance around 3-7am. But the overall message is that patterns of e-resource use don’t differ very much by outcome, it’s just the volume.

We tested these findings and, sure enough, although there is a statistically significant difference in overall usage between 9pm and 9am for researchers with different grades, there is no statistically significant difference in the percentage of overall use which takes place within that timeframe.

So we’ve found something different from Manchester Met. I should stress that our methodology is different, and more simplistic, so it may just be that we haven’t managed to identify a relationship that does exist – perhaps, once you factor out lots of the other variables which affect outcome, a relationship can be detected. But you’d need a lot more data to do that, and a much more complicated model.

As a final footnote – what’s happening around 8pm in the proportion of usage graph?! We’ve tentatively termed this the ‘Eastenders gap’: high-achieving students seem to have a much greater resistance to TV-soap-based distractions than those who go on to get a Third! One to explore in the focus groups, perhaps…

* When we say ‘usage patterns over a 24 hour period’, we’re actually aggregating the whole year’s data. So 3 logins between 10-11am means a user has logged in 3 times during that hour over the course of a year. We don’t count multiple logins in a single hour on the same day (e.g. someone logs in, times out, and logs in again – that’s just one login as far as we’re concerned).