Cost Per User
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The document discusses analyzing EZProxy logs to understand library resource usage at the user level and calculate a cost per user. It describes how EZProxy logs can be matched to student data to attribute usage to individual users. Calculating cost per user provides insights not available from typical usage statistics like most-used resources, heaviest users, and subject areas of use. The analysis can reveal which user groups use which resources, how resource use relates to student attributes like class standing and GPA, and the return on investment for individual subscriptions. A live demonstration shows interactive visualizations of usage data. Analyzing this granular level of usage data helps libraries understand user behaviors and the value of resources.
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Cost Per User
- 1. Cost Per User Analyzing EZProxy Logs For Collection Development Tiffany LeMaistre 2015 Charleston Conference
- 2. About Nevada State College First Generation
- 3. Proxy What? WHAT IT MEANS AND HOW WE DO IT
- 4. Proxy What? • EZProxy is OCLC’s authentication system for library resources • EZProxy logs are web logs that can be setup to store and save usage data • Those log files can be compared with student data using the username as a match point Library Information & Technology Services Institutional Research
- 5. Why Cost Per User? THAT SEEMS LIKE A LOT OF WORK FOR ANOTHER CPU SPREADSHEET
- 6. Why Cost Per User? • True comparison • Available for every resource • No concerns about: • Missing data • Unclear standards of measurement • Estimates based on incomplete information
- 8. Online Resource Use in Tableau
- 9. Select Filters to Change Display
- 10. Collect Use That Vendors Don’t Provide
- 11. Collect Use of Open Access Resources
- 13. Why Cost Per User? • Is the library achieving economies of scale? • Is the resource core or supplemental? • What are the characteristics of users? • Interdisciplinary or discipline-specific • Students or faculty • Freshman or seniors • GPA, grades on research assignments Users Value Cost
- 14. ROI with Individual Subscriptions
- 15. Why Cost Per User? • Is the library achieving economies of scale? • Is the resource core or supplemental? • What are the characteristics of users? • Interdisciplinary or discipline-specific • Students or faculty • Freshman or seniors • GPA, grades on research assignments Users Value Cost
- 16. Access Time Is Inconsistent
- 17. Compare New to Repeat Users Instead
- 18. Use of the Discovery Search New User 24% Repeat User 76%
- 19. Why Cost Per User? • Is the library achieving economies of scale? • Is the resource core or supplemental? • What are the characteristics of users? • Interdisciplinary or discipline-specific • Students or faculty • Freshman or seniors • GPA, grades on research assignments Users Value Cost
- 20. Add in Student Data for More Insights
- 21. Mostly Seniors Use the Discovery Search
- 22. Interdisciplinary Use of Historic NY Times
- 23. Heavily Nursing Use of UpToDate
- 24. Many Factors Could Influence GPA
- 25. Learn More University of Minnesota
- 26. Citations Collins, E., & Stone, G. (2014). Understanding patterns of library use among undergraduate students from different disciplines. Evidence Based Library and Information Practice, 9(3), 51–67. Cox, B., & Jantti, M. (2012a). Capturing business intelligence required for targeted marketing, demonstrating value, and driving process improvement. Deputy Vice-Chancellor (Education) - Papers. http://doi.org/10.1016/j.lisr.2012.06.002 Cox, B., & Jantti, M. (2012b, July 18). Discovering the Impact of Library Use and Student Performance. Retrieved from http://www.educause.edu/ero/article/discovering-impact-library-use-and-student-performance Fransen, J. (2013). How do Engineering Students and Faculty use Library Resources? American Society for Engineering Education. Retrieved from http://conservancy.umn.edu/handle/11299/151819 Haddow, G., & Joseph, J. (2010). Loans, Logins, and Lasting the Course: Academic Library Use and Student Retention. Australian Academic & Research Libraries, 41(4), 233–244. Jantti, M., & Cox, B. (2011). Measuring the value of library resources and student academic performance through relational datasets. Deputy Vice-Chancellor (Education) - Papers. Retrieved from http://ro.uow.edu.au/asdpapers/121 JISC. (2015). Library Analytics and Metrics Project. Retrieved February 9, 2015, from http://jisclamp.mimas.ac.uk/ JISC, & University of Huddersfield. (n.d.). Library Impact Data Project. Retrieved February 7, 2015, from https://library3.hud.ac.uk/blogs/lidp/
- 27. Citations Magnuson, L., & Davis, R. C. (2014, October 29). Analyzing EZProxy Logs. Retrieved from http://acrl.ala.org/techconnect/?p=4684 Nackerud, S., Fransen, J., Peterson, K., & Mastel, K. (2013). Analyzing Demographics: Assessing Library Use Across the Institution. Portal: Libraries and the Academy, 13(2). Retrieved from http://muse.jhu.edu/journals/portal_libraries_and_the_academy/v013/13.2.nackerud.html Nevada State College. (2014). Nevada State College: Facts & Figures. Retrieved February 13, 2015, from http://nsc.nevada.edu/2069.asp Oakleaf, M. (2010). The Value of Academic Libraries: A Comprehensive Research Review and Report. Chicago, IL: Association of College and Research Libraries. Retrieved from http://www.ala.org/acrl/sites/ala.org.acrl/files/content/issues/value/val_report.pdf OCLC. (2015). Log File Analysis. Retrieved February 13, 2015, from http://www.oclc.org/support/services/ezproxy/documentation/loganalysis.en.html Sharkey, M., & Thanki, S. (2014, November). How It’s Made: Predictive Analytics. Panel Presentation presented at the WCET 2014 Annual Conference, Portland, Oregon. Retrieved from http://wcetconference.wiche.edu/session/how-it%E2%80%99s-made-predictive-analytics Soria, K., Fransen, J., & Nackerud, S. (2013). Library Use and Undergraduate Student Outcomes: New Evidence for Students’ Retention and Academic Success. Portal, 13(2), 147–164. Soria, K. M. (2013). Factors Predicting the Importance of Libraries and Research Activities for Undergraduates. The Journal of Academic Librarianship, 39(6), 464–470. http://doi.org/10.1016/j.acalib.2013.08.017
- 28. Citations Stone, G., & Collins, E. (2013). Library usage and demographic characteristics of undergraduate students in a UK university. Performance Measurement and Metrics, 14(1), 25–35. http://doi.org/10.1108/14678041311316112 Stone, G., Ramsden, B., & Pattern, D. (2011a). Library Impact Data Project Toolkit (p. 15). University of Huddersfield. Retrieved from http://eprints.hud.ac.uk/11571/1/Toolkit_final.pdf Stone, G., Ramsden, B., & Pattern, D. (2011b). Looking for the Link Between Library Usage and Student Attainment. Ariadne, (67). Retrieved from http://www.ariadne.ac.uk/issue67/stone-et-al The Chronicle of Higher Education. (n.d.). Retrieved November 3, 2015, from https://chronicle.com/subscribe/?PK=M1224 University of Minnesota Libraries. (n.d.). Library Data and Student Success. Retrieved February 7, 2015, from http://blog.lib.umn.edu/ldss/ White, S., & Stone, G. (2010). Maximizing use of library resources at the University of Huddersfield: Based on a breakout session held at the 33rd UKSG Conference, Edinburgh, April 2010. Serials: The Journal for the Serials Community, 23(2), 83–90. http://doi.org/10.1629/2383 Whitmire, E. (2003). Cultural diversity and undergraduates’ academic library use. The Journal of Academic Librarianship, 29(3), 148–161. http://doi.org/10.1016/S0099-1333(03)00019-3 Wong, S. H. R., & Webb, T. D. (2011). Uncovering Meaningful Correlation between Student Academic Performance and Library Material Usage. College & Research Libraries, 72(4), 361–370. http://doi.org/10.5860/crl-129
- 29. Tiffany LeMaistre Electronic Resources and Discovery Librarian Nevada State College Marydean Martin Library E-mail: tiffany.lemaistre@nsc.edu Phone: (702) 992-2807
Editor's Notes
- Tiffany LeMaistre – Electronic Resources and Discovery Librarian at Nevada State College
- Nevada State College founded in 2002 One of the fastest growing higher ed institutions in the country and the fastest growing in the state, currently 3,400 students Diverse and committed to helping traditionally underserved students succeed Strong institutional focus on using data in predictive analytics and proactively recommend services positively correlated with student success – the library wants to be a part of that institutional project The Marydean Martin Library is the first completely digital library in the state of Nevada – 4 librarians and 2 full time staff members
- NSC Uses EZProxy to mediate on and off-campus access Most libraries collect brief logs for security or troubleshooting. NSC collects a detailed log file with the user id of people accessing library resources, referring URLs, and which resources are being accessed With that data alone you can calculate cost per user, but in order to take it a step further we partnered with IT and IR departments to compare EZProxy log data with student data. IT’s Kat Mulvey created a Powershell Script to parse our logs for SQL Upload. A SQL database with student data is managed by Mick Haney in the Office of Institutional Research and library data is added into it. Sandip Thanki creates a visualization in Tableau that includes student data points. With this workflow the library has access to anonymized data in the aggregate (i.e., 35 nursing students with a GPA above 3.5, NOT Jane Doe with a GPA of 2.8 accessed WorldCat Discovery on October 5th)
- Many institutions are concerned about the effort/possibility of developing these campus partnerships and the difficulty of analyze the log files. Is it worth it?
- Even without partnerships proxy log analysis can offer a lot that anonymous usage statistics do not. First – they are a true comparison. Not all resources are COUNTER compliant and even among those that are there are nuances (i.e., BR1 title requests vs BR2 section requests) User and session counts from EZProxy measure all resources on the same standard.
- This dashboard was created with proxy log data only, it did not require any matching from the process involving IT and IR departments.
- In the live dashboard you can select filters or dropdown menus to change what data is being displayed
- You can use proxy data to collect use for resources that don’t provide any statistics from the vendor NSC may have cancelled the NCTE journals package if it weren’t for this evidence of use.
- You can collect use of open access resources by proxying the links anywhere that the library manages access (i.e., in the library catalog, discovery, knowledgebase, or website listings). This gives a sense of return on investment for the work of managing these resources.
- You can compare resources that provide statistics but not COUNTER statistics to other resources using the same metric.
- Beyond the true comparison, there are many other advantages of looking at use from the user rather than the retrieval perspective.
- Comparing to individual subscription costs rather than ILL costs may be more palatable to administrators when you can say something like the library saved students and faculty more than $5,000 rather than the library was very efficient with subscription budgets.
- A couple of ways to look at whether a resource is core or supplemental based on the usage patterns of users over time.
- First attempt was looking at access time, but this statistic can’t be compared consistently because users don’t “log out” of EZProxy and some platforms create more activity in the log file than others. Also it is unclear what a “good” access time is when you consider the high variability in the statistic and the contribution of activities like researching in one database and linking to the full text in another via the link resolver.
- Comparing user to session counts is more consistent.
- You can get an idea of repeat vs new users, and may even be able to take it a step further looking at users who used a resource 1, 2-5 times, 5-10, etc.
- The real power though is in comparing to user data.
- This dashboard was created by our Director of Institutional Research. It shows the characteristics of library users so we can determine if they are representative of the campus population as whole or if some groups are underrepresented in library users.
- Limiting on the overall use pie chart can show the characteristics for one database or resource. The Gender and Ethnicity pie charts have been removed from the following screenshots as they are not relevant to the collection development analysis presented.
- Many other campus dashboards are used to show how different factors influence student success. Ultimately, we want to see if there is a positive correlation between library use and student success. In the spring we plan to run a multiple regression analysis with GPA and 1-year retention and graduation rates that will control for many of the factors you see here such as High School GPA, and Academic Load. We are also in the midst of a pilot study with an English 101 class to see if there is a correlation between library use and grades on a research assignment. Plans to present our findings at conferences and through a publication in 2016.
- Here are a few other institutions doing similar work.
- Thank you!