Building a “Library Cube” from Scratch

Library assessment research in academic libraries has grown over the last several years with a particular emphasis on measuring the effects of library resources on student success (often GPA and retention) to demonstrate value and impact. 1 Through assessment departments, often in partnership with institutional researchers, academic libraries can build foundational datasets important for reporting value and impact.At Florida State University (FSU), these efforts within departments and divisions have resulted in silos of data that speak to temporary or singular questions or decisions.However, when brought together, these data might impact broader decisions and gain attention from campus administrators with influence over budgeting and resource allocation.These studies might be momentarily compelling or important for specific divisions but could contribute to telling the larger story about the collective impact of an academic library's services, spaces, and resources.Building a multidimensional data warehouse could help an institution gather and connect these studies and datasets in one unified database for easy querying and reporting.Translating this concept for use within academic libraries, we will discuss the many steps involved in planning a library cube.Ultimately, this database brings together measures of student demographics, resource usage, and outcomes such as GPA and retention rates.This enables assessment librarians and administrators to make connections between the impact of library services, spaces, and collections on student success in a more cohesive and organized way.Additional environmental factors could include instruction and learning, grades, extracurricular activities, parental educational attainment, use of other campus resources, jobs after graduation, etc.A library cube can help libraries streamline data analysis and reporting integral to engaging with campus decision-makers, which is especially helpful in navigating a higher education landscape that emphasizes performance metrics and demonstrations of value and impact.Ideally, these advancements will lead to multidimensional, real-time datasets from which library stakeholders could ask research questions using myriad variables at the point of query.Standardized data could automatically be pulled from different data sources already cleaned, merged, stored, and ready to use.Data points on library usage, such as circulation, equipment borrowing, tutoring services, study rooms, library databases, and working with librarians, can all be linked via a unique identifier with student demographics, pre-college, and engagement variables.This, when combined with institutional research data, such as student grades, course load, major, and GPA, can then be used to gauge usage and trends of library use and services among the student population that uses the library.Academic libraries should engage in many discussions about the theoretical and empirical reasons for including certain variables regarding libraries and student success.Taken together, these variables should facilitate the most complete framework for understanding the impact of libraries on students, making their collection and selection particularly important in the decision-making process.There are many models on the various factors affecting student success that might be of value to libraries when making these decisions, for example, Astin's Input-Environment-Output (I-E-O) Model of Student Involvement. 2 By increasing the number of data sources available for analysis, a more complete and comprehensive picture can be provided of the impact of library usage on student and faculty success.