The following libraries have made great strides working with library learning analytics. This list will continue to grow.
Dennis Krieb, Ed.D., Director, Institutional Research and Library Services | email: dkrieb@lc.edu | phone: 618-468-4300
To better understand any correlational relationships between student success and library services/collections, Lewis & Clark Community College has developed a technology platform for capturing student usage data within the library. Three components of the library are tracked: reference questions, attendance of library instruction classes, and checking-out library material. How it works. Any student using one of these three library services is asked if they would be willing to share their student ID. This serves the purpose of providing informed consent. If the student agrees to share their student ID, the number is scanned and sent to a data warehouse. Within the data warehouse, course, grade, retention, and completion data from the Student Information System are connected with library usage data. Using a reporting tool, student success measures and correlated with library usage data. In terms of the technology, student IDs are captured using a scheduling program called SARS Trak. SARS Trak then passes the student ID to a Blackboard Analytics data warehouse. The Blackboard Analytics data warehouse connects to an Ellucian Student Information System (SIS). A Pyramid Analytics reporting tool is used to query the data warehouse. Pyramid is able to perform calculated measures such as course grade averages, student GPA, and retention rates with student use of library services. Lastly, no personally identifiable student information is shared. All data are kept within Lewis & Clark’s SIS. All data are only accessible by college personnel with permitted authority.
Ken Varnum, Senior Program Manager and Discovery Strategist | email: varnum@umich.edu | phone: 734-615-3287
As part of the University of Michigan Libraries’ effort to be transparent about the ways it collects, analyzes, and uses data that connects U-M users and library resources, the library participates in the campus-wide ViziBLUE information site. ViziBLUE, a service of U-M’s Information Technology Services, provides a clearinghouse for campus units to describe the way they collect, store, share, and use data about U-M affiliated individuals.
The library has also participated in two IMLS-funded grants related to learning analytics: Connecting Libraries and Learning Analytics for Student Success (CLLASS), which produced a library profile for the Caliper learning data standard, and the Library Learning Analytics Project, which developed a proof of concept for collecting and analyzing library transaction data from a range of sources and connecting it with other campus data.
Selena Killick, Associate Director, Student and Academic Services | email: selena.killick@open.ac.uk | phone: +441908 659209
Learning analytics is a key organizational strategic driver at The Open University and the institution is known as a leader in this research field internationally. In 2014 the University worked in partnership with the Student Association to develop and agree on an Ethical use of Student Data for Learning Analytics Policy. In line with the wider organizational strategy, the Library embarked upon research into Library Learning Analytics in 2015, initially focusing on the relationship between library use and student performance and retention. The study analyzed online library resource data from access logs from the EZproxy and OpenAthens systems. A data set of 1.7 million online resource accesses was combined with student success data for around 90,000 undergraduate students and a series of analyses undertaken. The study found a pattern where students who are more successful are accessing more library resources. In 2017 the team went onto explore the relationship between attendance at library tutorials and student performance, combining Adobe Connect attendance data with student success data. Again, the study identified that students who attend library tutorials are more successful in their studies. The research in both areas has been repeated annually and we have continued to find a correlation between student attainment and engagement with the Library. The research is communicated with the academic faculty to inform service planning and curriculum developments. Our work is continuing in this area as we explore the opportunities our data provides us to support student success.
University of Minnesota - Twin Cities University Libraries
Shane Nackerud, MLIS, Interim Director, Content Services | email: snackeru@umn.edu | phone: 612-625-7880
Since 2011, The University of Minnesota Libraries have worked in this area through our Library Data and Student Success project (LDSS) which attempted to correlate student success measures such as GPA, retention, and 4 year graduation rates to library use. The UMN Libraries have also been enthusiastic participants in a number of grants spearheaded by Megan Oakleaf, including the Library Integration in Institutional Learning Analytics (LIILA) grant, and the Connecting Libraries and Learning Analytics for Student Success (CLLASS) grant which produced a Library Profile for the Caliper learning data standard. The UMN Libraries are now investigating using the Library Profile to share library data with institutional learning record stores for participation in campus learning analytics projects.
Becky Croxton, Ph.D., Head of Strategic Analytics & Special Projects | email: racroxto@uncc.edu | phone: 704-687-0480
To assess which engagement factors significantly contribute to student success at UNC Charlotte, a large, public research university in the southeastern United States, the university library, along with representatives from Academic Affairs, Student Affairs, and other academic support units across campus have agreed to contribute their co-curricular and extracurricular student data to a repository that is enabling multifaceted and evolving longitudinal study. This joint project, led by the library, is allowing library and university leaders to identify key resources, services, and activities within their units that are positively associated with student success. Alignment of student engagement data with measures of student success not only involves identifying key student success and engagement metrics, but also requires careful consideration and protection of student privacy. The findings from this study are helping the library and other support units and services across campus to help students succeed and graduate.
This project is part of an ongoing, longitudinal study of undergraduate student engagement and success data of students who matriculated in summer/fall 2012 through the present. The dataset includes yearly student engagements with 17 different co-curricular and extracurricular partners across campus at the "type of activity" level of specificity. The library collaborates with the university's Office of Institutional Research to align the engagement data with pre-college data (e.g., HS GPA, ACT/SAT Scores, # of Incoming Credits), demographic data (e.g., Race, Ethnicity, Pell Status, College of Enrollment, On/Off Campus Residence, Learning Communities), and measures of success (e.g., Year-to-Year Retention, GPA, and Graduation Rates). Recent research publications related to this work include:
Moore, A. C., & Croxton, R. (2021, March). Engagement Pathways to Transfer Student Success, Paper presentation at the 2020 Library Assessment Conference, Online.
Croxton, R., & Moore, A. C. (2020). Quantifying the Library’s Value: Aligning Library, Institutional, and Student Success Data. College & Research Libraries, 81(3), 399-434. DOI: https://doi.org/10.5860/crl.81.3.399.
Croxton, R., & Moore, A. C. (2019, April). From Matriculation to Graduation: Alignment of Library Data with University Metrics to Quantify Library Value. Proceedings of the 2019 Association of College & Research Libraries Conference, April 10-13, Cleveland, Ohio.
Croxton, R., & Moore, A. C. (2018, December). Quantifying the Value of the Academic Library. Proceedings of the 2018 Library Assessment Conference, December 5-7, Houston, Texas
Joe Pirillo, MLIS, M.Ed. Information Literacy/Online Learning Librarian | email: pirilloj@uwosh.edu
Since the Fall of 2017, Polk Library at the University of Wisconsin Oshkosh has been integrated into our University's Navigate platform (An EAB product). Early on, Polk Library demonstrated the good fit between library services and the value in contributing to our Institution's learning analytics platform. Through EAB's Navigate, we log such things as reference transactions, workshops and all other types of instructions events. We have also, and continue to, work with our institution's Office of Institutional Research in order to identify trends and correlations between students, retention, and section participation. This information has helped us plan and make adjustments to our information literacy program, in order to be of greater service to our students. As of spring of 2023, Polk Library is investigating the use of Navigate’s Intervention Effectiveness tool, which will allow Polk Librarians to further evaluate the outcomes of their interventions.
Margie Jantti, BA LIS, MBA, Director of Library Services | email: margie@uow.edu.au | phone: +61418445791
An exploration of ‘big data’ generated by the University of Wollongong Library (UWL), and the ability to con-join Library usage data with other institutional data to create relational datasets, resulted in the Library Value Cube, initiated in 2010. UWL, through the development of the Value Cube, had established the necessary architecture for the full-scale, systematic capture and reporting of students’ use, i.e. logins or borrowing of Library resources to test correlations with academic performance (grades). The UWL Cube demonstrated a positive and persistent correlation in student use of Library information resources and improved academic performance outcomes as evidenced in their grades. The reporting cycle for the Value Cube is, however, sessional as the original intent was to produce reports associated with grades. Regardless, Library usage data is uploaded weekly and can be decoupled from grades. This means it could be readily harvested and contribute ‘near real-time data’ mapped to teaching weeks. Library Cube data was used for the early iterations of learning analytics dashboards. Over time, reporting has become more sophisticated and embedded within the learning management system platform (Moodle), with an array of reports and visualizations available to teaching staff and students. Library data in the form of interaction with etexts remains an element of monitoring student activity within the learning management environment. This remains an important and significant milestone in terms of how interaction with library resources can contribute to the ‘whole of student’ learning experience and the success goals of the institution.
The Library Learning Analytics Project (LLAP) was a collaborative study of how academic libraries impact learning by 17 institutions led by the University of Michigan. LLAP had two primary goals. The first included identifying how the library impacts learning, especially in the areas of course instruction, research (including funding), and publication. The second entailed developing sharable tools, scripts and protocols on the basis of principled engagement and professional agency. The LLAP project provided guidance on how to best design and implement empirical, holistic analysis of the links between library usage and learning outcomes. The project produced a set of tools, scripts, and protocols that are freely available to all libraries via the project website https://libraryanalytics.org/.
Principal Investigator: Felichism "Felix" Kabo, M.Arch, Ph.D., University of Michigan
Timeline: 2018 -2021
Project Website: https://libraryanalytics.org/
Grant Information: IMLS LG-96-18-0040-18
The CLLASS project brought together a diverse group of library and higher education leaders and experts to:
develop models for library inclusion in institutional learning analytics,
explore strategies for bringing the models to fruition,
design technologies to support library-enabled learning analytics, and
anticipate ways in which this work will increase library impact on student learning and success.
To support dialogue both among librarians and between librarians and their educational partners, this document includes a series of discussion questions throughout the text and closes with suggested reading lists, including relevant privacy resources. It is hoped that this report will stimulate discussions about the role of libraries in institutional learning analytics, enable the technical capabilities necessary for libraries to contribute to institutional understanding of student learning, and ultimately enable libraries to employ a new approach to supporting student learning and success.
Principal Investigator: Megan Oakleaf, PhD, Syracuse University
Timeline: 2018 - 2019
Project Materials: EDUCAUSE
Grant Information: https://www.imls.gov/grants/awarded/lg-97-18-0209-18
Data Doubles was a student-centered research agenda focused on student perspectives of privacy issues associated with university and academic library participation in learning analytics (LA) initiatives. The collaborative team consisted of scholars and practitioner experts at eight American higher education institutions. Three phases structured this project. The first used a semi-structured interview method, the second a multi-institutional survey, and the third and final phase used scenario-based focus groups. This project was funded by the Institute of Museum and Library Services.
Principal Investigator: Kyle M. L. Jones, PhD, Indiana University - Indianapolis
Timeline: May 2018 - August 2022
Project Website: https://thecorkboard.org/projects/data-doubles/
Grant Information: IMLS LG-96-18-0044-18
Prioritizing Privacy was a three-year continuing education program that trained academic library practitioners to comprehensively address privacy and other related ethical implications of learning analytics projects (e.g., autonomy, agency, and trust). The training program guided participants to explore learning analytics, privacy theory, privacy-by-design principles, and research ethics and then present participants with case studies. Participants develop plans for a learning analytics project prioritizing privacy protections.
Principal Investigator: Lisa Janicke Hinchliffe, MLS, EdM, University of Illinois
Co- Principal Investigator: Kyle M. L. Jones, PhD, Indiana University - Indianapolis
Timeline: 2019 - 2022
Project Website: https://prioritizingprivacy.org/
Grant Information: IMLS RE-18-19-0014-19
The University of Illinois Chicago Library is conducting research to answer questions relating to how student demographics play a role in how they engage in academic activities, including their use of the library; how students’ psychological factors relate to their academic engagement; how students define their academic success and how they view the library’s contribution to their success; what factors correlate with students’ GPA and their own definition of academic success; and how libraries can utilize assessment tools to examine academic engagement and success and apply the findings. The research project team is creating tools and research that will be available for dissemination to academic libraries to assess the impact of their library on student success and to further support and address students’ needs and/or academic libraries. The assessment tools and datasets developed by the research team will be publicly available in summer 2024 via the Project Website.
Project Director and Principal Investigator: Jung Mi Scoulas, PhD, University of Illinois Chicago
Co- Principal Investigators:
Sandra L. De Groote, University of Illinois Chicago
Nestor Osorio, Northern Illinois University
Kimberly Shotick, Northern Illinois University
Timeline: 2022 - 2024
Project Website: [TBD]
Grant Information: IMLS LG-252338-OLS-22
