The coolest thing to do with your data will be thought of by someone else

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“The coolest thing to do with your data will be thought of by someone else”

Dave Pattern

Library Systems Manager University of Huddersfield [email protected] www.daveyp.com/blog/

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• _{…to improve existing services}

• _{…to gain insights into user behaviour} • _{…to measure the impact of the library}

– _{do the students who use the library the}

most get the highest grades?

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• _{User activity data}

– _{record of a user’s actions on a website or software} system or other relevant institutional service

• _{Attention data}

– _{record of what a user has viewed on a website or} software system or other relevant institutional service

http://bit.ly/g6S5wH

Defining Using Usage

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• _{Circulation Transactions}

– _{user A borrowed this book on 23/Jan/2011}

• _{E-Resource Usage}

– _{user B logged in and accessed ScienceDirect 126} times during 2009

• _{Entry Stats}

– _{user C entered the library 12 times in Dec/2009}

Defining Usage Data

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Keyword Cloud

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Borrowing Suggestions

based on circulation data

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Borrowing Suggestions

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Course Level New Book Lists

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Search Suggestions

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Guided Keyword Searches

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Journal Suggestions

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13

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Trends in Borrowing

unique titles circ’ing per acad. year

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000

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Trends in Borrowing

# of items borrowed per acad. year

10 15 20 25

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 average number of items borrowed per academic year

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Discussing Open Data

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• _{“The coolest thing to do with your data}

will be thought of by someone else.”

– _{Rufus Pollack (Open Knowledge Foundation)}

http://m.okfn.org/files/talks/xtech_2007/

Sharing Data

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• _{“Absolutely, couldn’t agree more.}

The point is not so much whether this statement might be true or not, so

much as what it does to your thinking and planning if you decide to take it as an article of faith.”

– _{Paul Walk (UKOLN)} _{http://bit.ly/9ao3c4}

Sharing Data

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Sharing Data

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Sharing Data

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Non & Low Usage Project

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Non & Low Use Project

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Measuring Library Impact

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Non & Low Use Project

Results

• _{Not a cause and effect} relationship

• _{Never proven}

statistically significant

• _{Potential for}

collaboration on future projects

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To prove the hypothesis that…

“There is a statistically significant correlation across a number of

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Project reports

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

• _{Themed posts}

– _{The Project Plan} – _Hypothesis

– _Users – _Benefits

– _{Technical and Standards}

– _{Licensing & reuse of software}

and data

– _{Wins and fails (lessons along}

the way)

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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

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Legal issues

• _{Consultation with JISC Legal, University legal officer and data}

protection officer

• _{Ensured that any identifying information is excluded before it}

is handled for analysis

• _{Excluded any small courses to prevent identification of}

individuals e.g. where a course has less than 35 students and/ or fewer than 5 of a specific degree level

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Data issues

• _{Anticipated that there may be problems in getting enough}

data to make the project viable

– _{Potential partners were asked to confirm that they could}

provide at least 2 of the 3 measures of usage as well as student grades

– _{Huddersfield has provided definitions on the data required}

and the form the data can be accepted in

• _{Some partners have already run into some issues with data}

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Can we prove the hypothesis?

• _{Not quite!}

• _{Due to the data not}

being continuous, a correlation cannot be calculated

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Further statistical tests

• _{Running a Kruskal-Wallis}

test

– _{to indicate whether there is a}

difference between values e.g. between levels of

e-resource usage across degree results

– _{THEN we analyse the data}

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What we think we can prove

• _{That the relationship and}

variance means that you can believe what you see

• _{And you can believe it}

across a range of data, e.g. subjects

• _{So library usage does}

impact on students attainment 70 60 45 30 83 65 41 27 0 20 40 60 80 100

1 2:1 2:2 3

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Remember the disclaimer!

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Library Impact Data Project

book loans inc. renewals (2009/10)

First class honours Upper second class honours Lower second class honours Third class honours Pass - degree awarded without honours 0 50 100 150 200 250 300 350 400 218 230 170 124 105 249 242 180 135 151 279 236 156 126 100

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Linking back to non/low usage

• _{Our research shows that for books and}

e-resource usage, there appears to be a statistical significance across all partner libraries

• _{If we know that there is a link between usage}

and attainment

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2008/9 – library visits

15.5% of students who gained a 1st never visited the library

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2008/9 – MetaLib usage

70% of those who gained a 3rd logged in to e-resources

20 times or less over 3 years

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2008/9 – book loans

15% of students who gained a 1st_{never borrowed a book}

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Profiling non/low users

• _{Flesh out themes from the focus groups}

– _{to advise on areas to work on}

• _{Check the amount and type of contact subject teams}

have had with the specific courses

– _{to compare library teaching hours to attainment}

• _{Baseline questionnaire or exercise for new students}

– _{To establish the level of information literacy skills for}

new students

• _{Target our users by concentrating staff resources at the}

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Next steps for the project

• _{Pull out any themes from the focus groups}

• _{Release the data on an Open Data Commons}

Licence

• _{Release a toolkit to help others benchmark their}

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Further work

• _{Do cuts to the information budget mean that attainment}

will fall?

• _{Can we add more value by better use of resources?} • _{What about gender and socio-economic background?} • _{Can we link books borrowed on reading lists to}

attainment

• _{What about VLE use?}

• _{Interest from ACRL (USA), University of Wollongong}

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Acknowledgements

• _{Dave Pattern and Bryony Ramsden}

• Phil Adams, Leo Appleton, Iain Baird, Polly

Dawes, Regina Ferguson, Pia Krogh, Marie

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Thank you

• _{http://library.hud.ac.uk/blogs/projects/lidp/}

• _{http://eprints.hud.ac.uk/10949/}

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