As outright discrimination has largely transformed into covert inequities, “discrimination [has] moved underground” (Massey, 2007). Institutions are increasingly interested in fostering diversity and inclusion [D&I] to counteract these inequities (Brimhall et al., 2017). Although frequently linked, diversity and inclusion are distinct (Mor Barak et al., 2015). Measuring the impact of D&I interventions on diversity involves monitoring workplace demographics. Measuring inclusion is less straightforward (Sherbin & Rashid, 2017). Researchers fail to agree on a single construct of inclusion and lack evidence to do so (Shore et al., 2011). Yet, defining and measuring inclusion is critical in the studying of how social stratification is localised in different contexts, such as in education. 

Researchers usually define inclusion as perceptions of uniqueness and belongingness, or alternatively, as participation and contribution. Theories borrowed from social psychology explain how individuals will unconsciously sort themselves into groups based on commonalities, such as gender or race, while group membership influences how they perceive others (Tajfel & Turner, 1986). Individuals must feel a sense of belongingness in their groups, while also being sufficiently recognised for their unique characteristics (Shore et al., 2011). These social psychology theories could explain how individuals’ framing of themselves and others manifest into group dynamics. Therefore, social psychology could provide an explanation for patterns of student interaction within a given classroom.

Yet, a purely social psychological framework of inclusion overlooks how inclusion exists beyond individuals’ sentiments. Instead, Roberson (2006) defines inclusion as “the removal of obstacles to the full participation and contribution of [people] in organisations”. This version of inclusion highlights how in- and out-group dynamics dictate who is trusted, who is communicated with, and ultimately, who is embedded in social and collaboration networks (Ridgeway, 2011). Conventional inclusion surveys effectively measure inclusion as uniqueness and belongingness as Shore et al. (2011) defines it, but fail to appropriately measure inclusion in terms of participation and contribution.

To solve this methodological gap in the literature, this research borrows methodologies from the field of data science to revitalise inclusion research, using one university as a case study. The participating university was an elite public higher education institution. It prides itself on the usual interests of most historically white, high-ranking, and well-funded 21st century educational institutions: being research-driven, global, future-oriented, and a leader in higher education and learning. As such, in September 2022, one school at the institution implemented mandatory inclusion training for all tutors. The training had three main goals: to increase awareness and knowledge of what constitutes equality, diversity, and inclusion; to teach tutors tangible ways to alter behaviours to foster inclusion; and to provide a space for tutors to learn from each other to further develop inclusive teaching practices. The study and this resulting paper are a showcase of how one computational social science method, social network analysis, can help measure and analyse inclusion, as participation and contribution, in educational settings. Only a few studies to date have used social network analysis to research patterns of inclusion, such as Karimi & Matous (2018), Collins & Steffen-Fluhr (2019), and Hardcastle et al. (2019). None have employed mixed-methods of social network analysis, survey analysis, and demographic analysis. This innovative mixed-methods, but largely computational, approach is the focus of this paper as it allows educational researchers to monitor changes in students’ experiences of inclusion. A secondary benefit of this method is that it provides evidence, or lack thereof, for D&I interventions as they are implemented. Therefore, using social network analysis to measure inclusion helps ensure all students, regardless of identity, succeed in their education.

This research occurred at the participating higher education institution from September through December 2022. All tutors part of the one school completed a mandatory “Fostering Inclusion in Tutorials” training, which was co-developed by this proposal’s researcher and a working group of three other postgraduate tutors. The working group was supervised by the school's teaching and student development officer to ensure the training had subject matter expertise and relevant institutional knowledge. The training was facilitated during the first week of the term, but prior to the commencement of courses. After training implementation, data collection occurred in tutorials for one introductory first-year course. Two tutorials were recorded three times throughout the term to capture classroom dialogue. Students and tutors were also asked to complete an inclusion survey twice during the term. The inclusion survey was an adaptation of the Mor Barak Inclusion-Exclusion Scale survey to measure perceptions of inclusion constructed as perceptions of uniqueness and belongingness. It also included demographic questions such as race, disability, gender, etc. The same data collection occurred for one similar first-year course in another school where tutors underwent another inclusion training to provide a comparative lens. One tutorial from this course was therefore also recorded three times throughout the term to capture classroom dialogue. For data analysis, social network analysis with demographic information was used to capture the changes in contribution and participation of students, thus analysing tutorial discussion dynamics. Organisational network analysis’s ability to monitor the ebbs and flows of communication to and from marginalised groups is crucial in understanding how inclusion shifts to redistribute power (Helgesen, 1995). With that, data focused on the quantity of communication and who spoke to who. The igraph R package created visualisations of communication reflecting how the communication network changed throughout the term. Other metrics investigated included items such as the number of interruptions that occured from majority to historically marginalised students, and speaking time of students. Together with the inclusion surveys, social network analysis reveals how inclusion shifted throughout the term. Furthermore, the inclusion surveys administered allowed for the nuances in inclusion and exclusion processes to be explored.

This presentation will discuss some findings from the student inclusion surveys, but its focus will be on the study’s observational network visualisation data. This session will thus demonstrate how social network analysis may be used to measure inclusion in the classroom in terms of participation and contribution. By the end of this session, educational researchers will understand how social network analysis presents an innovative methodological addition to equality, diversity, and inclusion discourse. At a high-level, the results indicate that the training interventions did not universally lead to students experiencing high perceived levels of inclusion and high levels of contribution and participation. While the interventions hoped to prompt behavioural changes in tutors to propel their students with marginalised identities to become more deeply embedded in class discussions, this was not the case. Even so, the results show how social network analysis with inclusion survey and demographic data can reveal otherwise covert patterns of inclusion and exclusion. In particular, patterns of racial exclusion at the higher education institution will be discussed. Therefore, this study sets the groundwork for further implementing social network analysis to investigate inclusion levels in other areas of student life. It will also allow universities to understand if certain identity groups beyond historically marginalised racial groups have lower levels of inclusion in the classroom. Ultimately, the hope is that these methods will help education researchers better understand what students need to be successful.

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