How do engineering students and faculty use library resources?

In 2011, library staff at a large Research I university began looking for ways to track use of library resources by students, faculty, and staff. Access points tracked in Fall 2011 ranged from loans and use of digital resources to workshop attendance and appointments with peer research consultants. Access points related to consultations with archivists and media librarians were added in Spring 2012. Working with campus institutional research staff, we were able to correlate Fall 2011 library use with higher term GPA and retention for first year students while controlling for other variables related to student success. The Student Success line of inquiry is useful for demonstrating that successful students do find value in the library. However, as students move beyond their first year, the factors contributing to student success become increasingly complex and interrelated. Therefore, while we continue to collect first year data and plan to check the correlation strength each semester, we are not extending this area of study beyond first year students. The rich dataset used for the Student Success analysis lends itself to another use, one that is arguably more “actionable” than the first: By aggregating the collected data over college, level (undergraduate, graduate student, etc.), and other groupings, we have our first good look at who is using library services (and who is not) as well as what they are doing. The work in progress combines data collection in the Fall 2011 and Spring 2012 semesters and focuses on just science and engineering students, faculty, and staff. As of this writing, we have conducted basic demographic analyses on how College of Science & Engineering (CSE) faculty and students differ from their colleagues in other colleges, and have investigated possible correlations between library use and student success indicators for CSE first year students. We have also uncovered data limitations that will affect how we use the collected data, and how we refine data collection in the future.