Digging deeper into library data: Understanding how library usage and other factors affect student outcomes

The original Library Impact Data Project (LIDP) found a statistically significant relationship across a number of universities between library activity data (specifically the number of items borrowed and logins to e-resources in the library) and student attainment.

Phase II of LIDP seeks to deepen our understanding of this relationship by investigating additional data such as gender, age, ethnicity, declared disability, retention, VLE and reading list use and engagement with in-house projects. This data will be used to identify predictors for student outcomes, with a particular focus on engagement with library services, in order to understand better how library activity relates to student attainment, including causal relationships.

This paper will show some of the results from our quantitative data analysis:

1. Demographic factors and library use, examining whether there is a relationship between demographic variables and all measures of library use, and to see which factors carry the most weight in such a relationship.

2. Retention vs. non-retention, to see whether there is a relationship between patterns of library use (including increasing or decreasing intensity of use and time of use) and retention.

3. Value added, using UCAS points data (used in the UK to total up further education grades for application to universities) and library usage data to establish whether use of library services has resulted in higher than expected outcomes for students.

4. VLE use and outcome. Subject to data availability, we will test to see whether there is a relationship between VLE use and outcome.

5. MyReading and Lemon Tree. Although data will not be available in the lifetime of the project, we will discuss these new projects to encourage greater use of library resources and describe how we will test to see whether there is a relationship between demographic characteristics and engagement with these projects.

6. Predicting final grade. We will use demographic and library usage data to test whether you can predict a student’s final grade from available data.

In addition the paper will discuss initial findings from three case studies of courses which exhibit non/low use of library resources. The qualitative data will improve our understanding of student behaviour, and help ensure library resources are allocated in the best way to meet student needs.