Library Impact Data Project Phase IIthe data strikes back

Graham Stone

Information Resources Manager

NAG Conference5-6 September 2012

This work is licensed under a Creative Commons Attribution 3.0 Unported License

#lidphttp://eprints.hud.ac.uk/14514

Warning!You may experience data overload from this presentation

http://www.flickr.com/photos/opensourceway/5755219017/

• …Library Impact Data Project

Previously on…

http://www.ncdd.nl/blog/?p=2468

4

Non/Low Use Projectdigging deeper into data

Measuring Library Impact2008/9 honours graduates

Analysis of the results consistently revealed a correlation between e-resource use, book borrowing and student attainment

This appears to be the case across all disciplines

JISC Activity Data Call

• Obtained funding from the JISC Activity Data Call

• 6 month project (Feb-Jul 2011)

Library Impact Data Project

To prove the hypothesis that…

“There is a statistically significant correlation across a number of universities between library activity data

and student attainment”

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

Library Impact Data Projectbook loans & Athens (2009/10)

Library Impact Data Projectlibrary PC logins & visits (2009/10)

Linking back to non/low usage

• Our research showed that for books and e-resource usage, there appeared to be a statistical significance across all partner libraries

• While identifying a relationship is of great importance in both academic library use, identifying specific groups of high or low users of resources and their level of achievement will provide data which can be used more extensively to the benefit of library users.

Library Impact Data ProjectPhase II (Jan-Oct 2012)

Library Impact Data ProjectPhase II (Jan-Oct 2012)

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

• Phase II looks at around 2,000 FT undergraduate students at Huddersfield

Library Impact Data ProjectAims and objectives

• Addition of other relevant data such as demographics, retention data etc.

• To use the enriched data to provide better management information

• To conduct a feasibility study on the viability of a JISC shared service that involves collection and analysis of library impact data for all UK HE libraries

Library Impact Data ProjectAdditional data

• We had some new library usage metrics which weren’t available during Phase I– Demographics– Overnight usage– The number of e-resources accessed

• as distinct from the hours spent logged into e-resources• the number of e-resources accessed 5 or more times

• the number of e-resources accessed 25 or more times.

Library usageAge and usage

Library usageGender and usage

Library usageEthnicity and usage

Library usageCountry of domicile and usage

Library usageBy discipline

• Each of the 100 (ish) courses has been classified into one of 17 ‘clusters’

• Clusters have been aggregated to form six ‘groups’ – Groups compared to see some overarching differences– Then drilled down to compare clusters within groups, to get a

more detailed understanding

• Statistically significant effect sizes are highlighted by the depth of colour– small effects pale, medium effects a little darker, and large effects

very dark.

Library usageAggregated subject groups

Library usageHealth group

Library usageComputing and Engineering group

Library usageHumanities group

Library usageSocial Science group

Library usageArts group

http://www.flickr.com/photos/brianscott/3745357149/

Library usageRetention

• Looking at one year of data for every student• Only looking at people who dropped out in term three• Using a cumulative measure of usage for the first two

terms of the 2010-11 academic year• 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.

Library usageRetention

Time of day of usage and outcomesaverage hourly use

Time of day of usage and outcomesaverage hourly use as percentage

Final results and usageNumber of e-resources accessed

• The number of e-resources accessed– as distinct from the hours spent logged into e-resources– the number of e-resources accessed 5 or more times– the number of e-resources accessed 25 or more times

• Showing how many of Huddersfield’s 240+ e-resources, a student has logged into during the year, once, at least five times or at least 25 times.

Number of e-resources accessedDepth and breadth

LIDP Phase IIMyReading

Profiling users

• Investigate the use of reading lists– Matching attainment with use of essential, recommended and

wider reading

• Working with copacAD

LIDP Phase IILemon Tree

LIDP Phase IIStill to do

• UCAS points vs. library usage– A proxy for value added services?

• Predicting final grade• % use for on and off campus vs. library usage• A final data blog• Toolkit ver.2.0• Student workshops• Due mid October

Looking forwardWhy is Google so easy and the library so hard?

Other factorsNumber of e-resources accessed

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

• Heavy usage does not equate to high information seeking or academic skills

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

EBEAMLearning analytics

Measureable targetsadding value

• To contribute towards a dashboard for student retention– to use non-use of library e-resources as an indicator of possible

retention issues

• To tie LIDP II results in with the MyReading project– to measure depth and breadth of student reading

• To use the outputs from the LIDP to work with a number of academic ‘champions’– To improve attainment– To improve retention 

Measureable targetsadding value

• To demonstrate VfM from the library’s resources– Using UCAS points, attainment and usage to show value

• To create staffing efficiencies– Using impact data to concentrate staff resources at the right

point

A shared service for Library Impact Data?

• Can we build a suite of management reports to aid the above targets

• A national shared service to allow manipulation of data and benchmarking?

• Joint copacAD/LIDP questionnaire to go to SCONUL directors in September 2012

• RLUK/SCONUL workshop to be held in October 2012

Acknowledgements

Ellen Collins

Research Information Network

ellen.collins@researchinfonet.org

46

LookItsDavePattern

Thank you

Library Impact Data Project blog

http://library.hud.ac.uk/blogs/projects/lidp/

Graham Stoneg.stone@hud.ac.uk

@Graham_Stone

This work is licensed under a Creative Commons Attribution 3.0 Unported License

#lidphttp://eprints.hud.ac.uk/14514