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Web and social media metrics: library’s impact and value

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Lecture presented by Vivian Praxedes D. Sy at PAARL's Summer Conference on the theme "Library Analytics: Data-driven Library Management", held at Pearl Hotel, Manila on 20-22 April 2016

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Web and social media metrics: library’s impact and value

  1. 1. WEB AND SOCIAL MEDIA METRICS – LIBRARY’S IMPACT AND VALUE Vivian D. Sy 21 April 2016
  2. 2. Introduction  Philippines: transitioning to K to 12 program  Schools may be cutting their budgets  School administration to library: What is your impact and value to the success of your students and researchers?
  3. 3. The Library & the Strategic Planning Process (Allison, 2015) Library’s Mission: To provide quality intellectual property that have specific bearing upon students’ learning objectives analyze, synthesize Families Employers Society Funding Bodies/Donors Financiers Government/Private Institutions
  4. 4. The Library & the Strategic Planning Process  Changes in the environment:   “corporalization” of universities   social networking   availability of e-readers   mobile devices   Google’s share of searches in the Internet   use of library as ideal space for working
  5. 5. The Challenge  How to determine the impact and value of the library on student success
  6. 6. Determining the Library’s Impact and Value [1] Library Impact Data Project (in 2011 and 2012) [2] University of Minnesota Research (in 2011) [3]The Library Cube (data of 2010-2011)
  7. 7.  Hypothesis: There is a statistically significant correlation across a number of universities between library activity data and student attainment [1] Library Impact Data Project – Phase 1 (Showers, 2015;Stone and Ramsden, 2013)
  8. 8. [1] Library Impact Data Project – Phase 1 Data Requirements of the LIDP Project (Stone, Ramsden & Pattern, 2011)
  9. 9. [1] Library Impact Data Project – Phase 1 Data Requirements of the LIDP Project (Stone, Ramsden and Pattern, 2011)
  10. 10. [1] Library Impact Data Project – Phase 1 Item Loans vs. Final Degree Results (Pattern, 2012)
  11. 11. [1] Library Impact Data Project – Phase 1 E-resource Logins vs. Final Degree Results (Pattern, 2012)
  12. 12.  There is a positive relationship between student final degree result and:  Library loans  E-resource usage  Correlation not established because data used was non-continuous  Relationship between student final degree results and library visit not be established; many reasons why students enter the library [1] Library Impact Data Project – Phase 1
  13. 13.  Important concerns from focus groups:  Library resources  Referrals to resources  Library staff for consultation re technical and resource problems  Technical concerns re access to information and general technology problems [1] Library Impact Data Project – Phase 1
  14. 14.  Hypothesis: There is a statistical significance between  demographic data  discipline  student retention and library usage [1] Library Impact Data Project – Phase 2
  15. 15. [1] Library Impact Data Project – Phase 2 Dimensions of Usage (Stone and Collins, 2013)
  16. 16. [1] Library Impact Data Project – Phase 2 Demographic Data Examined in Phase 2 (Showers, 2015)
  17. 17.  Age  Mature students:  e-resource usage  Younger students:  library visits  Gender  Women:  resource usage,  library visits [1] Library Impact Data Project – Phase 2
  18. 18.  Country of Domicile  Black and Asian vsWhite students:   library visits   PC usage   e-resource usage within the campus  Chinese vs UK students:   library loans   e-resource usage  No relationship between Ethnicity and Final Grades  Overall, there is a relationship between demographics and library usage [1] Library Impact Data Project – Phase 2
  19. 19. [1] Library Impact Data Project – Phase 2 Course Enrollment (Collins and Stone, 2014)
  20. 20. [1] Library Impact Data Project – Phase 2 Course Enrollment (Collins and Stone, 2014)
  21. 21. [1] Library Impact Data Project – Phase 2  Of e-resources and PDF downloads  Social Sciences: higher user group  Arts: lowest user group  Social Sciences  Behavioral Sciences: highest user  Business: higher usage than law, social work, education  Law: extremely low users  Findings: There is a relationship between discipline and library usage
  22. 22. [1] Library Impact Data Project – Phase 2  Cumulative measure of usage was examined  Results were similar to those of Phase 1  There is a relationship between retention and library usage
  23. 23. [1] Library Impact Data Project  Recommendations  Determine reasons for differences in library usage among the different groups  Further breakdown data, e.g. age groups  Use results to modify services to target “low users”
  24. 24. [2] University of Minnesota Research  How often and how do students use library services and resources?  What impact does this usage have on students’ academic success?
  25. 25. [2] University of Minnesota Research  Digital access  Database, e-book and e-journal logins  Website logins  Circulation  Loans  Interlibrary loans  Workstation usage  Workstations
  26. 26. [2] University of Minnesota Research  Instruction  Workshops  Course-integrated instruction  Introduction to Library Research workshops  Reference  Peer Research Consultations  Online reference
  27. 27. [2] University of Minnesota Research  %age of Library Users  Undergraduate: 77%  Graduate: 85%  Students of all colleges or schools: 60% or higher were library users  School of Pharmacy: 100% were library users  College of Design UG and G students: highest users of physical collection  College of Human Development UG students: highest users of digital collection  GS students are generally heavy users
  28. 28. [2] University of Minnesota Research  UG library users vs UG non-library users: GPA  Students who did “Introduction to Libraries II”: more likely to enroll the next semester  Controlling for other factors, first-year non- transfer students who used the library at least once had higher GPAs and retention rates  One of these library-user students was associated with 0.23 increase in GPA compared to non-user  A unit-increase in type of use or library interaction was associated with 0.07 increase in GPA
  29. 29. [2] University of Minnesota Research  Data used to:  support continuity or increase of library budget  answer questions like percentage of undergraduates who used the libraries for a specific semester  include libraries mini-workshop in experience course for new students
  30. 30. [2] University of Minnesota Research  Data used to:  inform academic advisors of their advisees’ use of the library, their class schedules and previous grades  create interest in library services among academic department liaisons, because of the high library usage rate of other departments
  31. 31. [2] University of Minnesota Research  Recommend next study to determine  how and why patrons are using spaces in the libraries  what spaces and services will attract new spaces and services
  32. 32. [3] The Library Cube  Question: What is the value to the students when they use library information resources?
  33. 33. [3] The Library Cube  Library Cube is a database with reporting functions on:  Library usage data  Loans  E-resource usage  Subscription databases  E-books  E-readings materials  Demographic and academic performance data  Was deployed in May 2012
  34. 34. [3] The Library Cube  Re use of ezproxy logs to determine e-resource usage hours  A day is divided into 144 10-minute sessions  If a student has a log entry in a specific 10- minute session, add 1/6 of an hour to student’s access for that session  Then disregard any other entries for the same 10-minute session
  35. 35. [3] The Library Cube  Limitations  Borrowing <> learning  Skills in using library resources is only one of several factors that contribute to academic success  Correlation <> causality
  36. 36. [3] The Library Cube
  37. 37. [3] The Library Cube  Finding: Students who use library resources outperform students who do not use library resources
  38. 38. [3] The Library Cube  Areas of concern  For similar usage patterns, why are some student groups more successful than others?  Why do postgraduates gain significantly less benefit than undergraduate students?
  39. 39. For Philippines Libraries  Use any or a combination of the three cases as models  Also look at theValue of Academic Libraries Project of ACRL by Dugan, 2015  Decide on which library usage data to study against academic performance  Consider limitations of use of e-resource usage and social metrics  Supplement quantitative with qualitative data to investigate possible areas of concern
  40. 40. For Philippines Libraries  Be creative in viewing the data to:  Modify not-as-effective services  Terminate non-effective services  Come up with new services  Define and follow through with action plans based on the data  Try to coordinate for a constant data analytics service  Improve e-resource usage and social metrics
  41. 41. Conclusion  If the school administration asks, “What is your impact and value to the success of your students and researchers?” the library should be able to quantify its impact and value.
  42. 42. The End

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