1. Making Data work for you The Library Impact Data Project Graham Stone Information Resources Manager This work is licensed under a Creative Commons Attribution 3.0 Unported License Creative Commons Attribution 3.0 #lidp #jiscLAMP http://eprints.hud.ac.uk/19570

2. Library Impact Data Project

3. To support the hypothesis that… “There is a statistically significant correlation across a number of universities between library activity data and student attainment”

4. Library Impact Data Project 1 Original data requirements For each student who graduated in a given year, the following data was required: –Final grade achieved –Number of books borrowed –Number of times e-resources were accessed –Number of times each student entered the library, e.g. via a turnstile system that requires identity card access –School/Faculty

5. Library Impact Data Project –Showed a statistical significance between: Final grade achieved Number of books borrowed Number of times e- resources were accessed –Across all 8 partners –Not a cause and effect relationship

6. Library Impact Data Project Phase I looked at over 33,000 students across 8 universities Phase II looked at around 2,000 FT undergraduate students at Huddersfield

7. Library Impact Data Project 2 Additional data Demographics Discipline Retention On/off campus use Breadth and depth of e- resource usage UCAS points (entry data) Correlations for Phase 1

8. Library usage Age

9. Library usage Ethnicity

10. Library usage Country of domicile

11. Library usage Aggregated subject groups

12. Library usage Retention Looking at one year of data for every student Using a cumulative measure of usage for the first two terms of the 2010-11 academic year Only looking at people who dropped out in term three All the students included in this study were at the university in the first two terms, and they have all had exactly the same opportunity to accumulate usage.

13. Library usage Retention

14. Going forward @Huddersfield Identifying retention issues and our impact on lowering them as part of a University dashboard Engagement Workload Performance Thanks to Lee for the inspiration for this image

15. Going forward @Huddersfield Identifying retention issues and our impact on lowering them as part of a University dashboard Look at specific subjects in order to work towards: –A best practice toolkit for information skills sessions –Further understanding by holding focus groups with target areas Create an action plan to engage with academic colleagues Showing value for money and the impact of the service on the student experience

16. Library Analytics Survey We asked: How important will analytics be to academic libraries now and in the future, and what is the potential for a service in this area? With thanks to the team at MIMAS for the initial survey analysis

17. How important will analytics be to academic libraries Significant appetite for analytics services among this sample –96% were interested in the automated provision of analytics demonstrating the relationship between student attainment and library usage Strong willingness to share a broad range of data –preference to be identified by JISC band (91%) –as opposed to named institution (47%)

18. Key strategic drivers 1.Enhancing the student experience 2.Demonstrating value for money 3.Supporting research excellence

19. JiscLAMP Library Analytics and Metrics Project Looking at the benefits of scale To develop a prototype shared library analytics service for UK academic libraries –Envisioned as a data dashboard –Enabling libraries to capitalise on the many types of data they capture in day-to-day activities –To support the improvement and development of new services and demonstrate value and impact in new ways across the institution

20. JiscLAMP Library Analytics and Metrics Project

21. JiscLAMP A brief word on ethics? Should we be holding and analyzing this kind of data –Data protection issues –‘Big brother’ –All students pay the same fees – shouldn’t they be treated the same? But what if we didn’t do this –What would the reaction be if it was found that we had this data but didn’t act on it? –We have a duty to care for the individual wellbeing of our students

22. JiscLAMP Normalisation of Data

23. JiscLAMP The data dashboard

25. JiscLAMP What can we do with the data? We can demonstrate usage by cohorts: Department Degree name Course Course ‘type’? Gender/Ethnicity/Nationality/Disability/Age Level of attainment Attendance mode (full time/part time) UCAS points We can demonstrate correlations between usage and attainment/usage and cohort)

26. JiscLAMP The ugly prototype

27. JiscLAMP What could we do?

28. JiscLAMP Where do we go from here? LAMP Phase 2 –We have funding for Phase 2 –We start testing the ‘ugly’ prototype in late-March –Next steps include putting a project plan together Key Use cases for next phase –NSS scores and how LAMP would enable analysis of the data –Individual usage data (possibly outside LAMP and wider Jisc analytics issue) –Financial data –eBooks content usage (possibly separate strand of work?)

29. JiscLAMP Where do we go from here? Workshop with SCONUL (London 7 May 2014 tbc) –engaging the wider library community, specifically library directors Key contacts/relationships for next phase –HESA (NSS) –Shibboleth/Athens –SCONUL (performance group) Business case ideas –Possibility of a LAMP service which provides consultancy whereby institutions could buy-in the analysis of data that they want

30. Thank you! Ben Showers (Jisc) b.showers@jisc.ac.uk Graham Stone (Huddersfield) g.stone@hud.ac.uk http://jisclamp.mimas.ac.uk This work is licensed under a Creative Commons Attribution 3.0 Unported License Creative Commons Attribution 3.0 #lidp #jiscLAMP http://eprints.hud.ac.uk/19570