Learning analytics uses data to uncover ways to 1) increase student learning and 2) improve institutional contexts and practices associated with student success. Library engagement in learning analytics requires librarians to identify available library data points, investigate the potential utility of each, determine how to access the data that are deemed relevant for understanding library contributions, and discuss any data that raise security or ethical concerns. This process can enable librarians to determine, in a logical and methodical way, whether each library data point should be included or excluded from analysis as well as whether the data is likely to be useful in achieving the goals of learning analytics.(i)
For example, librarians seeking to determine whether students who interact with library reference services attain more learning outcomes (i.e., learn more), earn better assignment or course grades, are more engaged, are retained, transfer successfully, graduate/complete on time, get jobs, and/or earn more money. Librarians then can advocate for more (or more appropriate) reference resources, encourage more faculty and students to interact with reference librarians, and/or improve reference services might wish to review data including physical desk transactions, student/faculty-librarian consultations, peer research consultations, reference chat/IM/text/SMS participation, reference email participation, referrals to librarians from other systems (advising/IPAS/early warning), archives and special collections consultations, etc.(ii).
A detailed list of data for consideration is included in section 3.7 of the LIILA report. The Library Learning Analytics Project (LLAP) also has an example of a data collection case study chart on their website.
Please see the tabs in this section for data related to specific areas of library interactions.
i. Oakleaf, M. “Library Integration in Institutional Learning Analytics” (LIILA, Nov. 15, 2018), available online at https://library.educause.edu/~/media/files/library/2018/11/liila.pdf
ii. Oakleaf, M. “Library Integration in Institutional Learning Analytics” (LIILA, Nov. 15, 2018), available online at https://library.educause.edu/~/media/files/library/2018/11/liila.pdf
Circulation data sources that might be relevant for learning analytics efforts include:
physical circulation (books, technology, etc.)
electronic resources usage (databases, ebooks, digital videos and audios, etc.)
special collections usage
institutional repository usage
discovery/search system usage
reserves usage
interlibrary loan usage
Source: LIILA report, section 3.7
Instruction data sources that might be relevant for learning analytics efforts include:
library instruction participation
workshop registration/participation
special event participation
Source: LIILA report, section 3.7
Reference data sources that might be relevant for learning analytics efforts include:
physical desk transactions
student/faculty-librarian consultations
peer research consultations
chat/IM/text/SMS transactions
email reference transactions
referrals to librarians from other systems (advising/IPAS/early warning)
special collections and archives consultations
Source: LIILA report, section 3.7
Facilities data sources that might be relevant for learning analytics efforts include:
space usage (learning commons, makerspace, study rooms, etc.)
technology/device usage
computer/software usage
printer/copier usage
Source: LIILA report, section 3.7
Caliper Analytics® Library Use Profile
In 2018 the Institute of Museum and Library Services funded the e Connecting Libraries and Learning Analytics for Student Success (CLLASS) project. A significant outcome of this project has been the development of a a "library profile for Caliper, an interoperability standard used to label learning data and provide the means for capturing, presenting, and conveying learning activities to centralized data stores in order to facilitate the analysis, visualization, and increased awareness of student learning behaviors." (i) The standard is currently under review by IMS Global.
Connecting Libraries and Learning Analytics for Student Success (CLLASS)
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.
Libraries Learning Analytics Project (LLAP) - Resources Page
The Library Learning Analytics Project (LLAP) was awarded a 2018 National Leadership Grant for Libraries by the Institute of Museum and Library Services. LLAP is led by the University of Michigan. This is a collection of resources that includes key readings, charts, and guides.
GitHub repository for software and documentation related to LLAP
i. Oakleaf, Megan, Kenneth Varnum, Jan Fransen, Shane A. Nackerud, Cary Brown, Bracken Mosbacker, and Steve McCann. "Connecting Libraries and Learning Analytics for Student Success." (2020), available online at https://library.educause.edu/resources/2020/12/connecting-libraries-and-learning-analytics-for-student-success
