Computing Services

Library analytics:measuring the impact oflibrary use on arts disciplines

Graham Stone

Information Resources Manager

This work is licensed under aCreative Commons Attribution 3.0Unported License

#lidp #jiscLAMP

http://eprints.hud.ac.uk/ 19775

…to improve existingservices

…to gain insights intouser behaviour

…to measure theimpact of the library

Using Usage Data since 2005…

Library Impact Data Project

To support the hypothesis that…

“There is a statistically significant correlationacross a number of universities between libraryactivity data and student attainment”

Library Impact Data Project 1Original data requirements

•For each student who graduated in a given year, thefollowing 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 cardaccess

–School/Faculty

Library Impact Data ProjectPhase I

–Showed a statisticalsignificance between:

•Final grade achieved

•Number of booksborrowed

•Number of times e-resources wereaccessed

–Across all 8 partners

Not a cause and effect relationship

Library Impact Data Project

Phase I looked at over 33,000 students across 8universities

Phase II looked at around 2,000 FT undergraduatestudents at Huddersfield

Library Impact Data Project 2Additional data

•Demographics

•Discipline

•Retention

•On/off campus use

•Breadth and depth of e-resource usage

•UCAS points (entry data)

•Correlations for Phase 1

Library usageEthnicity

Library usageCountry of domicile

Library usageAggregated subject groups

Library usageArts group

Library usageArts group

•Music skewed the data and should probably have goneinto the humanities group

•There are fewer significant differences in the rest of thearts group

•Architects do not visit the library very often compared tothe other clusters in this group…

•…fashion designers do

•3D designers are fond of e-resources

Library usageThe effect of discipline

•Discipline has a big effect on patterns of library usage

•Although probably not earth shattering news in itself,LIDP does provide statistical evidence

•A starting point for conversations with academics

–Low usage by their students is a cause for concern

–What might be done to increase it?

Library usageThe ‘personalized boutique service’

•Library analytics can help to identify the ‘context’, which,“…allows us to prioritize areas of our service forimprovement” (Tilley, 2013, p.91)

•“Knowledge of users’ needs and activity - theirpreferences, the irritants - and their methods of working”(Tilley, 2013, p.82) could be used to target known ‘low-use’ subjects in order to give a more personalizedboutique service to the end user

Tilley, E. (2013). Personalized boutique service:critical to academic library success? New review ofacademic librarianship, 19(1), 78-97.http://dx.doi.org/10.1080/13614533.2012.753464

Library Impact Data ProjectSpreading the word

Number of e-resources accessedDepth and breadth

Other factorsNumber of e-resources accessed

•Both borrowing books and logging onto electronicresources does not guarantee the item has been read,understood and referenced

•Heavy usage does not equate to high informationseeking or academic skills

•Additionally, students on particular courses may be usingmore primary materials only available outside of libraryresources: non-use of library resources does not meanstudents are using poor quality information

Library usageRetention

•Looking at one year of data for every student

•Using a cumulative measure of usage for the first twoterms 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 theuniversity in the first two terms, and they have all hadexactly the same opportunity to accumulate usage.

Library usageRetention

Going forward@Huddersfield

•Identifying retention issues and our impact on loweringthem as part of a University dashboard

Engagement

Workload

Performance

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Thanks to Lee O’Farrell,

University College Dublin,

for the inspiration for this image

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Going forward@Huddersfield

•Two spin off projects

–Lemon Tree

–Roving Librarian

•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

•Creating an action plan to engage with academics

•Showing value for money and the impact of the serviceon the student experience

Librarygameby Running in the Halls

Drivers for change 1

And we know all this is firmly onLibraries’ radars

Our survey:

How important will analytics be toacademic libraries now and in thefuture, and what is the potential for aservice in this area?

Significant appetite foranalytics services

•There’s a significant appetite (96%) for analyticsservices…

•…but hesitation over sharing entry data and otherstudent data than other forms of usage data

•Only 46% would be willing to share data if the institutionwas named…

•…but if institutional identity can be anonymised, thatchanges to 91%

Is this a current strategic priority?

What about the next five years?

JiscLAMPLibrary Analytics and Metrics Project

•Looking at the benefits of scale

•To develop a prototype shared library analytics servicefor UK academic libraries

–Envisioned as a data dashboard

–Enabling libraries to capitalise on the many types of datathey capture in day-to-day activities

–To support the improvement and development of newservices and demonstrate value and impact in new waysacross the institution

JiscLAMPProject partners

JiscLAMPA brief (important) word on ethics

•Should we be holding and analyzing this kind of data

–Data protection issues and ‘Big brother’ concerns

–All students pay the same fees – shouldn’t they be treated thesame?

• But what if we didn’t do this

–What would the reaction be if it was found that we had this databut didn’t act on it?

–We have a duty to care for the individual wellbeing of ourstudents

Job storiesConsulting with the community

JiscLAMPNormalisation of Data

JiscLAMPWhat did we achieve?

•LAMP project outputs

–We managed to clean up and process the data from all of the partners

–We created a prototype – our analytics engine

–We performed a benchmarking exercise

•We showed that the idea of a shared library analytics service wasfeasible

JiscLAMPWhat can we do with the data?

•We can demonstrate usage by cohorts:

DepartmentDegree nameCourseCourse ‘type’?Gender/Ethnicity/Nationality/Disability/AgeLevel of attainmentAttendance mode (full time/part time)UCAS points

•We can demonstrate correlations between usage andattainment/usage and cohort)