Assessing Library Contributions to University Outcomes 9th Northumbria International Conference University of York, England Joe Matthews August 2011

Indirect Measures

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

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

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

Book Use Goodall & Pattern (2011)

eResources Library visits

Direct Measures

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

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

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

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

Shavelson’s Student Learning Outcomes Model

Total Collegiate Experience

Time Spent Studying

Disengagement Compact

Areas of Impact StudentFacultyUniversity Enrollment Retention & graduation Success Achievement Learning Experiences, attitudes & perceptions of quality Research productivity Grants Teaching Institutional reputation & prestige

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

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

Broad-based Data Analysis

Library Data Farm

Processes Load Clean Normalize Anonymize Analysis Export

Assessment Management Systems

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)

Anonymity & privacy are not incompatible

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

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

Library Use & GPA

Virtual Space

Community Space

Combine the Data

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

Privacy Institutional Review Board Partnering

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

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

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

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?

Questions?