Assessing Library Contributions to University Outcomes

1. Assessing Library Contributionsto University Outcomes 9th Northumbria International Conference University of York, England Joe Matthews August 2011

3. Indirect Measures

4. National Survey of Student Engagement • Academic challenge • Opportunities for collaborative learning • Interactions with faculty • Enriching extra-curricular experiences • Supportive environment for learning

5. NSSE & Libraries • Library use & educational purposeful activities are correlated at small liberal arts colleges • Larger universities – no correlation • Students who use the library more likely to work harder – meet faculty expectations

6. Library Experiences • Do not lead to gains in information literacy • Do not lead to gains in student satisfaction • Do not lead to what students gain overall from college

8. Book Use Goodall & Pattern (2011)

9. eResources Library visits

10. Direct Measures

11. Student Learning The contribution of the university in assessing student learning is indirect, at best.

12. Assess Learning • The Collegiate Learning Assessment (CLA) • The Collegiate Assessment of Academic Proficiency (CAAP) • The Measure of Academic Proficiency and Progress (MAPP)

13. Collegiate Learning Assessment • Critical thinking • Judgment • Analytical reasoning • Problem solving • Writing skills

14. Astin’s IEO Model Institutional Characteristics Classes Graduating Student Characteristics Entering Student Characteristics Programs Fellow Students Faculty Place of Residence Library Services Campus Environment

15. Shavelson’s Student LearningOutcomes Model

16. Total Collegiate Experience

17. Time Spent Studying 1964 2004

18. Disengagement Compact

20. Areas of Impact

21. Limitations Need – Demonstrations of Value • Micro-level studies • Inward looking • Small samples sizes

22. One Model • School libraries & standardized test scores • Controlled for school & community differences and found high correlations with use of library & test scores • 20 studies in different states

23. Broad-based Data Analysis

24. Library Data Farm

25. Processes • Load • Clean • Normalize • Anonymize • Analysis • Export

26. Assessment Management Systems

27. Expand Data Sets • In addition to library data • Partner with the Office of Institutional Research • NCES • IPEDS • NSSE • CLA • Campus surveys • Student registrar data (enrollment, grades)

28. Anonymity& privacy are not incompatible

29. Library Needs to Support Assessment Collections & Services Space Virtual Space Community Space

30. Collections & Services Space • ILS data • In-library use data • ILL data • Use of IT services • Reference services • Instructional services • Other

31. Library Use & GPA

32. Virtual Space

34. Community Space

35. Combine the Data

36. David Shulenburger Library Assessment Conference Building Effective, Sustainable, Practical Assessment Baltimore, Maryland 2010

37. Partnering Privacy Institutional Review Board

38. Broad-based Data Analysis Enables a library to prepare a credible analysis of the library’s impact in the lives of Students Faculty Researchers

39. The Goal “until libraries know that that student #5 with major A has downloaded B number of articles from database C, checked out D number of books, participated in E workshops and online tutorials, and completed courses F, G, and H, libraries cannot correlate any of those student information behaviors with attainment of other outcomes. Until librarians do that, they will be blocked in many of their efforts to demonstrate value.” Megan Oakleaf

40. Library Impact Model Use Books Print journals Special collections Intellectual development Assessment = Grade Success eJournals eBooks eResources Use Tangible Product Intangible

41. The Goal Get a better handle on: • Who is using the library? • Why are they using the library? • What impact does library use have in their life?

42. Questions? www.joematthews.org joe@joematthews.org