1. Using Data to Improve Learning, Teaching and Administration
Brendan Fawcett
Alex Moseley
Ian Rowlands
Using Data to Improve Learning, Teaching and Administration
In all the many and varied interactions students have with us as an institution, they generate data. Some of this data is aggregated and used in national returns to the government or other organisations; other data we use internally to look at retention, system load etc. But this just touches the surface. There is potential to use data in a much more focussed way which hones in on particular courses, sessions or even students. Learning analytics describes the use of such data to analyse and (ideally) improve the learning experience for students: but how would it be useful at this University, what would we want to analyse, and what form would it need to be in to be useful to staff and the students themselves?

2. What data do we have? Centrally? Locally? (departmental) Nationally?
Mapping data we currently have on white wall (input from attendees too).

3. What data could we use….. Demographics Academic Performance
Age, Gender, Ethnicity, Postcode, Household Income, School attended, parental experience, NS-SEC, Commuting distance, IMD, MOSAIC ….
Academic Performance
Pre-arrival qualifications, assignment / module marks (absolute and relative), submission timings, course preferences ….
On campus activities
Door swipes, eduroam logins, geographic app permissions, clicker data, lecture/lab attendance, surveillance cameras, clubs & societies …
Online Behaviour
VLE, library & website logs, Social Network Analysis –friends/followers, mood, content analysis
The full list generated in the previous activity.

4. Planning Office Typical questions:
return, check and validate data in our statutory returns
help strategic planning by providing data and intelligence
Typical questions:
Can you provide a profile of students on my course?
If we take students with a tariff of X how likely are they to graduate with a good degree, go on to good job?
Is Y a good predictor of student drop out?
The last of these questions is very important financially - every drop out loses the university £9K – particularly important in an era of Student Number Control.

5. Identifying at risk students…
Example from another institution: with each student mapped according to a set of indicators (have they been engaging with the course, the content, their peers and do they have the right background) and shown in a network given that their peers are possibly one of the biggest influences a traditional on-campus student has.

6. Identifying at risk students…
But did prove it was (just about) possible to link:
student record
accommodation record
Blackboard
swipe card systems
attendance monitoring sheets
library records

7. Over to you…. How could you identify which of your students were at risk? Is it ethical to do so? Are there data which would be helpful to the students themselves?
An activity.

8. Evidence-based library services
Library management systems generate vast amounts of highly aggregated statistics that don’t – in practice – make much real difference to service development. Need to make this data more relevant and actionable.
Point of the graphic? When Lancaster built their campus in the late 60s, they did not lay down paths: waited to see what routes people actually took when they had unfettered choice, and paved them over later (evidence-based landscape gardening!).
What is the library’s niche in an increasingly borderless information world? Need to be much more evidence-led than we have been.
Big interest in library analytics kicked off by Huddersfield (next slide).

9. Library use and degree class outcomes
Brief description of `Huddersfield’ data analysis (based just on library turnstile, loans and e-resource accesses).
Powerful story – and useful up to a point, but not really actionable data. We want to use library systems data to improve services / enhance the student experience / demonstrate VFM.
Business studies students
at eight UK universities (Jisc data)

10. Using data to improve Library services
physical library visits
digital library visits id
use of book stock
Very brief description of three datasets we’re investigating and how they link to demographic data via shared identifiers.
Applications could easily include:
Capacity planning (especially use of building for study rather than `library’ use)
Early NSS alarm bells (e.g. high numbers of holds for a particular cohort in Semester 1 probably indicates something big gone wrong).
[Lancaster] Time training interventions to better effect: e.g. why inductions in week 1 not week 4?
New behaviour-driven segmentations of library users to help us create more meaningful web personas and think about how we deliver services across a range of media (incl. mobile devices) and spaces.

11. Digital fingerprinting
How do individuals negotiate complex digital libraries?
Can we create settled paths based on user preferences?
We know little about digital library behaviour.
But we can measure number, length and complexity of sessions; search preferences (Google style discovery layer, catalogue search, structured search within publisher resources), numbers of downloads, and so on.

12. Over to you…. Looking at a curriculum level: How could you tell that a change to a course was successful? What other data would be useful?
Activity

13. A potential use of the ‘Amazon recommends’ approach to student learning.
Could also apply to Careers Advice

14. Where do we go from here?
Activity