Library Analytics and Metrics Project
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This was a presentation delivered at the 10th Northumbria Conference in York during July 2013. It provides a background, and introduction and overview to the Library Analytics and Metrics Project (LAMP) work that Jisc, Mimas (University of Manchester) and University of Huddersfield are collaborating on. The project will develop a prototype shared library analytics service for UK universities and colleges.
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Library Analytics and Metrics Project
- 1. Presenter or main title… Session Title or subtitle… 22nd July, 2013. York Safety in Numbers: Developing a shared analytics service for academic libraries
- 2. who we are… Ben Showers (Jisc) Programme manager, Digital Infrastructure @benshowers b.showers@jisc.ac.uk
- 3. who we are… GrahamStone (University of Huddersfield) Information Resources Manager/ Senior Research Fellow @graham_stone g.stone@hud.ac.uk
- 4. about LAMP… jisclamp.mimas.ac.uk #jiscLAMP This work is licensed under a Creative Commons Attribution 3.0 Unported License http://eprints.hud.ac.uk/17913
- 5. Library Impact Data Project
- 6. To support the hypothesis that… “There is a statistically significant correlation across a number of universities between library activity data and student attainment”
- 7. 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
- 8. 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
- 9. 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
- 10. 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
- 11. Library Analytics Survey We asked: Ho w im po rtant willanalytics be to acade m ic librarie s no w and in the future , and what is the po te ntialfo r a se rvice in this are a? With thanks to Joy Palmer and the team at MIMAS for the initial survey analysis
- 12. Automated provision of analytics demonstrating the relationship between student attainment and resource/library usage within your institution
- 13. In principle, would your institution be willing to contribute data that could be linked to anonymised individuals? • Significant appetite for analytics services among this sample – But more hesitation over sharing entry data and other student data than other forms of usage data • Strong willingness to share a broad range of data – preference to be identified by JISC band (91% in favour) – as opposed to named institution (47%)
- 14. Is this a current strategic priority?
- 15. What about the next five years?
- 16. Key strategic drivers 1. Enhancing the student experience 2. Demonstrating value for money 3. Supporting research excellence
- 17. Appetite for a national analytics service • An analytics service providing libraries with actionable data to transform the services and support institutions provide to students and researchers
- 18. LAMP: Library Analytics and Metrics Project • Running January 2013 – December2013 • A partnership between Jisc, Mimas (University of Manchester) and the University of Huddersfield • UK library community are part of the Community Advisory and Planning Group
- 19. LAMP: Library Analytics and Metrics Project The project will 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. data
- 21. Use cases •Demographics •Discipline •Student usage •Staff usage •Collections •Outcomes
- 22. Data types and sources Institutional: Gate count, circulation, usage, student records, school/faculty data. Shared services: Jusp, IRIS, Raptor, KB+, OpenURL
- 23. Identifiers The need foridentifiers that don’t identify! Gaining deeper insights The problem of updates The burden of data submission
- 24. technology
- 25. APIs LAMP APIs and local library development External APIs
- 26. Infrastructure
- 28. ethics
- 29. legal Registry and the burden of compliance (hot) Legal and ethical framework (medium) Project principles (lukewarm)
- 31. ethical How does this fit with the wider institutional mission?
- 32. keep up with developments The blog http://jisclamp.mimas.ac.uk Twitter#jiscLAMP Autumn workshop(s)
- 33. feedback Via the blog Via the Community Advisory Group Via SCONUL http://farm5.staticflickr.com/4115/4865344581_f770820a11_o.jpg
- 34. forthcoming events… • We are planning a series of webinars and events in the Autumn • More details will be available on the blog http://farm6.staticflickr.com/5113/6913646700_e98d7e943b_o.jpg
- 35. getting involved If you think your institution wants to be involved – talk to us or If you are doing something similar– please get in touch http://farm6.staticflickr.com/5088/5349392325_e990bb18ba_o.jpg
- 36. thankyou 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 Licensehttp://eprints.hud.ac.uk/17913
Editor's Notes
- LAMP is essentially all about the data. Dashboard wireframes – not so much about the design, more about thinking through the use-cases. Want to see if we can actually tell new stories. Is it possible to combine the data in a meaningful way – is it even possible to combine data? Or will we have to show it side-by-side and let the librarian do most of the interpretation?
- Current institutions are: Huddersfield, Lincoln, Wolverhampton, Manchester, De Montfort, Exeter At this stage of the project our data is all a little nepotistic (it’s all our data or from our friends and family!). But as the project moves from prototype and proof of concept we’ll begin to work with vendors to understand how we can potentially deliver data through and from them. Ownership of data – this could become a big issue.
- Issue of identifiers was one that is currently testing or brightest minds – specifically the developer we have working on the project. If you want to read more about it check our the blog and a post he’s recently written on the problem. Insights – Need to be able to make sure that data can be assigned to an anonymous individual even if that data isn’t next to each other or on the same line in a spread sheet etc. We can begin to analyse things statistically and gain some interesting insights, but making sure we can track the data associated with someone is critical if we want to take into account attainment etc,. It also seems to need to be done on the institutional side… otherwise if updates are submitted the identifier will get lost, and we wouldn’t be able to apply it consistently on the new data. But all these considerations point to a bigger issue around the data, which is one of burden : The burden to the institution in submitting data should be as minimal as possible. It may be that in this pilot/project stage that burden is slightly more on the institutional side, but as we transition (hopefully) into a service that burden should be shifting all the time, so that it is the service that deals with the complexity and the institution has little to do. This also may mean we need to think about the balance of ease versus functionality.
- APIs – if we’re providing institutions with a view on their data (potentially alongside other data sets), it also seems to make sense that institutions might want to pull that data and get it into other systems or present it in different ways. Can we pull in data from elsewhere?
- The time and resources of librarians should be spent on acting upon data, not collecting it. This also means that, as far as possible, analysis should begin to be baked into the visualisations being delivered. Point is to show something more than would be possible if you just had access to this data via different channels. The point is also to push the visualisations to such an extent that interpretation is half-way there already – the burden of analysis should be lighter.
- Currently significant legal and compliance burden on institutions wanting to work with us. We are working to reduce that and put at ease the concerns of registry and CIOs. Currently drafting a T&Cs document which lays out the legal status of Mimas (and their DPA code etc). But need to develop a legal policy and guidance that we can share with the sector and ensure the future process is as seamless as possible. We are also working with David Kay to develop a legal and ethical framework for the project – this will essentially be a document that outlines the legal and ethical landscape within which the project is operating. It ensures both the project and institutions are aware of all the legal and ethical implications of what they’re doing. Finally, and this is the least legally important, but is the projects statement of intent about how it will operate and work with its partners .