Learning Analytics

Optimizing learning environments to help support and improve the learning of students.

Learning Analytics to Support Student Success

The purpose of Learning Analytics is to support and improve the learning of students. This includes evaluating, diagnosing, and providing appropriate scaffolds to help students reach their learning goals. Ensuring students become reflective, self-motivated, and life-long learners is another important aspect of Learning Analytics. 

The definition of Learning Analytics varies, but one of the most cited definitions comes from SOLAR, The Society for Learning Analytics Research:

Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs (https://solaresearch.org/).

This site provides background, literature, and examples on how higher education institutions can use data in education and learning analytics as you begin to consider its role within your classroom, departments, programs, and schools at Rutgers University.  

OTEAR collaborates with various units to offer workshops every semester focused on how instructors can utilize learning analytics to improve their courses and support students. The following is a list of workshops descriptions with links to recordings and materials when available.

Introduction to Learning Analytics: Getting Started
Learning Analytics can be a powerful tool to support the students in your courses, but are you considering where to begin? This session is intended for faculty new to, but interested in learning analytics. The workshop will provide the general scope of the field, provide some examples of questions learning analytics can help answer, and how to ensure your course is designed in such a way to construct useful data. Participants will be engaged in discussion around how to use learning analytics responsibly and have the opportunity to look at the Canvas New Analytics within their course. Recording available on Canvas site (log into Canvas with netID)

Easy Data From Online Tools to Support Student Success
Users of Canvas, Kaltura, PlayPosit, Zoom, and Gradescope might not be aware of available data that could assist in understanding student engagement, learning, and success. We will present an overview of the data available, best practices, and limitations, and will give you an opportunity to look at and interpret the analytics in one of the tools you use. Recording available on Canvas site (log into Canvas with netID)

Understanding Your Students’ Engagement to Plan for the Fall
The level of student engagement in courses has been a hot topic for many since Spring 2020 and continues to be of interest for instructors, departments, programs, and schools. In this work session, you will have the opportunity to look at student engagement indicators within your own course(s) from this past year. We will help you consider what this information means and start to consider changes to your course ahead of the Fall semester.

Workshops are also available on request. Please email otear@rutgers.edu if you would like to schedule one for your unit.

We also have invited guest Dr. Ryan Baker’s talk on “Learning Analytics: Promises and Limitations” from October 15th, 2018 publically available below.

This Educause article draws upon the expertise of learning analytics experts who provide suggestions to avoid failure when utilizing learning analytics in higher education.

Goldie Blumenstyk from the Chronicle considers one of the main reasons for hesitance when discussions on the use of learning analytics begins.

This Chronicle article explains how Dayna Weintraub and Kevin Pitt utilized student-conduct data at Rutgers University.

This review of the literature summarizes how learning analytics is being used in higher education including the promise, weaknesses, and barriers.

  • Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2), 13-29. Retrieved from https://eric.ed.gov/?id=EJ1105911

Researchers detail a case study which demonstrate the benefits of using learning analytics throughout a course to adjust course design.

  • Ifenthaler, D., Gibson, D., & Dobozy, E. (2018). Informing learning design through analytics: Applying network graph analysis. Australasian Journal of Educational Technology, 34(2). doi:10.14742/ajet.3767

An in-depth look at past, present and future of learning analytics in higher education.

  • Lang, C., Siemens, G., Wise, A., & Gašević, D. The Handbook of Learning Analytics. Society for Learning Analytics Research. doi:10.18608/hla17

This report provides a thorough overview of much of the work being done in the realm of learning analytics. It includes several case studies from around the world and the lessons gained from these experiences.

Institutions have been creating various tools intended for different audiences. This provides a sample of those.

Audience: Instructors

Instructional Analytics Dashboard

Dashboard to allow instructors to view and utilize information on students’ backgrounds and engagement in their course to make data informed decisions regarding their course and instruction. Information on University of Iowa website.Yellowdig Visualization Tool

Provided for both instructors and students to visualize of network graph of class interactions in Yellowdig discussions. Information on Northwestern website.Course Specific Nudging

Student early warning system that would send students a “nudge” to let them know they have not utilized a certain key resource in the class yet. Australian university focused on online classes. Found did increase student engagement with those resources. Student feedback was generally positive and while dependent on using personalized language relevant to the course, it was found that students felt more connected to their faculty (avenue to support inclusion and equity). Full article is available online.

Audience: Chairs, Deans and other Administrators

Understanding Student Pathways Through Curriculum

Authors created a tool that generates Sankey diagrams to show student pathways through curriculum and groups on student preparedness (AP credits), course difficulty, initial first credit-bearing college-level course in a program. Used by departments to consider how to support students through their curriculum or advisors when advising students on the best pathway through a curriculum. Full article is available online.Disaggregating Canvas Student Learning Outcome Data to Identify Equity Gaps

While Student Learning Outcomes Assessment is a central component of accreditation, it can often be seen as a tedious process for faculty. However, the advent of Learning Management Systems (LMS) like Canvas have made the process more efficient and provide access to a trove of data. The National Institute for Learning Outcomes Assessment has stated on several occasions that LMS data should be used in the service of equitable assessment (Rorrer & Richards, 2020). Despite the potential of learning analytics to expose barriers to achievement, there is little information about how campuses might integrate Canvas data into their SLO assessment process. This session highlights how Irvine Valley College created a dashboard with disaggregated SLO mastery rates. Participants of the session will gain insight into the SLO assessment process at Irvine Valley College from data collection in Canvas to creating a disaggregated dashboard and monitoring trends. Full presentation is available online.

Audience: Students

“Elements of Success” Student Dashboard – Iowa

Student facing dashboard to provide feedback to students about their progress. Instructors can add this into their course if the course has a variety of assignments in their course and at least 25 students. Created by the Office of Teaching, Learning & Technology and Administrative Information Systems, in close collaboration with faculty members at Iowa. Information on University of Iowa website.“My Learning Analytics” Student Dashboard – University of Michigan

Student-facing dashboard developed as a collaborative project between researchers from the School of Information and School of Education partnering with ITS Teaching & Learning. Included visualizations are resources accessed, assignment planning, and grade distribution. Faculty can add this to their course through Canvas. Information on University of Michigan website.

Audience: Advisors

“Elevate” Advisor Dashboard – Penn State

Elevate Advising produces a student profile of the entire semester, all courses plus info from SIS, & Advising (Starfish) platforms. Information on Penn State website.“Learner Activity View” Advisor Dashboard – University of Wisconsin-Madison

Allows advisors to see a summary of course level behavior, across a student’s enrollment for the semester. Focuses on presence, engagement and performance. Information on University of Wisconsin-Madison website.


Do you have examples of how you are using learning analytics at Rutgers that you would like to share? Please send a note to otear@rutgers.edu with the details to include on this page and share with the Rutgers community.