Needed now: Belonging@Scale
Simon Buckingham Shum, Peter Felten, Lisa-Angelique Lim and Jennifer Uno
A sense of belonging is a critical enabler for learning. But belonging to what? Does ‘fitting in’ risk ironing out distinctiveness? And could technology help us assess and build belonging at the scale now needed in so many universities?
As with every purposeful human endeavour, motivation for learning and becoming a professional within a discipline are enhanced when individuals feel a sense of belonging. In the context of education, belonging refers to a student’s subjective feeling of being valued, accepted, needed and fitting in. Moreover, students may feel a sense of belonging at different scales, from their institution, or faculty, to their discipline or a specific class, or just with a small social group. The importance of belonging has been underscored by the recent COVID-19 pandemic and subsequent increase in online remote learning, when millions of students found themselves learning in isolation. The issue is further compounded for students from equity or disadvantaged groups, who may well start their studies feeling a lower sense of belonging. For instance, analyses from the Australian and US contexts report that such groups were disproportionately impacted during the stresses of the pandemic.
Research into this complex concept documents positive links to transition, retention, academic success and wellbeing. But critical debate in the field also questions when ‘fitting in’ can take an insidious turn into assimilation. As Karen Gravett and Rola Ajjawi observe, there are “students who may not wish to, or cannot, belong”, while Alison Cook-Sather and colleagues revive “mattering” as an alternative concept to belonging, “disentangling the two parts of belonging by separating “fit” from “value”.”
Can belonging be assessed, beyond directly observing or talking with students? There are validated survey instruments such as the Psychological Sense of School Membership and University Belonging Questionnaire and, as you’d expect, interviews/focus groups are widely used to enable students to tell their stories of belonging and alienation. But we also know that experiences of belonging are dynamic and contextual, so such methods present challenges for tracking and supporting students in a timely manner, at scale. It’s here that Learning Analytics may have key contributions to make.
A network is now forming to explore the possibilities of using learning analytics techniques to track students’ sense of belonging, and thereby enable interventions that can cultivate it. In an introduction to “Belonging Analytics” we make the following proposal:
“We argue that learning analytics (LA) offers the potential to address these challenges, providing novel and dynamic insights into belonging, helping students better understand and develop a sense of agency in their own journeys through higher education, and allowing institutions to be more responsive to students’ evolving experiences and needs. This will require the integration of rich qualitative data with the power of statistically meaningful quantitative data. If it is possible to track valid indicators of students’ sense of belonging longitudinally, at scale, in a timely manner, what we term “Belonging Analytics” could contribute significantly to learning, well-being, and equity in higher education.”
We present examples of platforms that can be used to gather diverse forms of student data, both self-reported and from online activity traces, in order to provide insights to teaching teams, and personalise feedback to students in ways that build belonging.
We conclude by noting the intriguing questions which now present themselves:
Definitional questions:
To what extent are students’ and staff perspectives of belonging aligned, and how do we bring students’ and staff voices into dialogue for participatory design?
If the recent critiques of “belonging” transition into more focus on “mattering” without a student having to assimilate into the university culture, what are the implications for LA?
Technical questions:
In trying to quantify belonging, what value is provided by data at different levels of belonging: classroom, discipline, institution/campus?
What are key ethical considerations when trying to track belonging with data
Research and practice questions:
How do students’ “belonging analytics trajectories” vary with demographics or disciplines?
How does the impact on students’ belonging differ with the use of different belonging analytics approaches?
Clearly, much care is needed to ensure that any approach drawing on learning data to inform belonging is firmly grounded in the best scholarship, does not do violence to its subject matter, and does not foster inequity (e.g., through algorithmic or other biases). The design of Belonging Analytics should be contextually sensitive and empowering to students. Ongoing work on human-centred learning analytics, and culturally-aware learning analytics, offer foundations on which to build.
We’ve presented these ideas in a recent webinar, and invite you to join the new LinkedIn group. We look forward to building this community to wrestle with these new possibilities and questions together.
Simon Buckingham Shum is Professor of Learning Informatics, and Director of the Connected Intelligence Centre, University of Technology Sydney (AUS)
Peter Felten is Professor of History, Executive Director of the Center for Engaged Learning, and Assistant Provost for Teaching and Learning, Elon University (USA)
Lisa-Angelique Lim is a Postdoctoral Research Fellow at the Connected Intelligence Centre, University of Technology Sydney (AUS)
Jennifer Uno is an Associate Professor of Biology and an Associate Director of the Center for the Advancement of Teaching and Learning, Elon University (USA)