"Belonging analytics"

A proposal





belonging analytics, engagement, higher education, learning analytics, sense of belonging


Students’ sense of belonging is associated with successful transition into higher education and a range of positive outcomes including enhanced learning, well-being, and demonstrated achievement. The COVID-19 pandemic underscored the importance of belonging as the shift to online learning highlighted the challenges of supporting and monitoring student belonging. Attending to belonging is not simple, however; students’ experiences with belonging are complex, dynamic, and contextual. In creating a new agenda connecting the fields of belonging and learning analytics, we propose the idea of “belonging analytics” to address the challenge of supporting and tracking students’ belonging. In this paper, we discuss how the understanding of belonging may be enhanced through learning analytics. Advancements in learning analytics, such as digital trace data, narratives, textual data, or a combination, may be harnessed to gain insights into ongoing experience of belonging, and consequently to support belonging. We conclude with a set of open questions to interested researchers and practitioners, to advance the field of belonging analytics.

LIFT Learning: The authors recently presented on the topic of Belonging Analytics at Indiana University’s 5th International Learning Analytics Summit. Engage with part of this discussion through this article’s companion LIFT Learning site where the authors describe their proposition and lay out the key concepts and challenges associated with belonging. As part of this, the authors discuss the case study presented in the article in greater detail, and provide additional contextual information that enhances the reader’s understanding of the proposal. The LIFT Learning site is available at https://lift.c3l.ai/courses/course-v1:LEARNINGLETTERS+0104+2023


Download data is not yet available.

Author Biographies

Lisa-Angelique Lim, Connected Intelligence Centre, University of Technology Sydney

Lisa is a postdoctoral research fellow with the Connected Intelligence Centre at the University of Technology Sydney. 

Simon Buckingham Shum, Connected Intelligence Centre, University of Technology Sydney

Simon Buckingham Shum is the Director of the Connected Intelligence Centre at the University of Sydney. Simon has a career-long fascination with the potential of software to make thinking visible. His work sits at the intersection of the multidisciplinary fields of Human-Computer Interaction, Educational Technology, Hypertext, Computer-Supported Collaboration and Educational Data Science (also known as Learning Analytics).

Peter Felten, Center for Engaged Learning, Elon University

Peter Felten is professor of history, executive director of the Center for Engaged Learning, and assistant provost for teaching and learning at Elon University.  

Jennifer Uno, Center for the Advancement of Teaching and Learning, Elon University

Jennifer is Associate Professor of Biology and Associate Director of The Center for the Advancement of Teaching and Learning (CATL) at Elon University. 




How to Cite

Lim, L.-A., Buckingham Shum, S., Felten, P., & Uno, J. (2023). "Belonging analytics": A proposal. Learning Letters, 1, 4. https://doi.org/10.59453/EAXA8005