Using Social Network Analysis to Study the Social Structures of Inclusion

Pearson, Nelson; Major, Justin Charles; Godwin, Allison; Kirn, Adam

doi:10.18260/1-2--31211

Cited by 4 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another structurally oriented approach to analysis can be found in network-based approaches like social network analysis. For example, Pearson et al (2018) used Social Network Analysis to understand how students from diverse backgrounds integrate into their large introductory engineering courses. A social network provides a relational description of individuals in the dataset that can be analyzed quantitatively.…”

Section: Secondary Data Analysismentioning

confidence: 99%

Person‐centered analyses in quantitative studies about broadening participation for Black engineering and computer science students

Reeping

Lee

London

2023

J of Engineering Edu

View full text Add to dashboard Cite

Background: There have been calls to shift how engineering education researchers investigate the experiences of engineering students from racially minoritized groups. These conversations have primarily involved qualitative researchers, but an echo of equal magnitude from quantitative inquiry has been largely absent. Purpose: This paper examines the data analysis practices used in quantitative engineering education research related to broadening participation. We highlight practical issues and promising practices focused on "racial difference" during analysis. Scope/Method: We conducted a systematic literature review of methods employed by quantitative studies related to Black students participating in engineering and computer science at the undergraduate level. Person-centered analyses and variable-centered analyses, coined by Jack Block, were used as our categorization framework, backdropped with the principles of QuantCrit. Results: Forty-nine studies qualified for review. Although each article involved some variable-centered analysis, we found strategies authors used that aligned and did not align with person-centered analyses, including forming groups based on participant attitudes and using race as a variable, respectively. We highlight person-centered approaches as a tangible step for authors to engage meaningfully with QuantCrit in their data analysis decision-making.Conclusions: Our findings highlight four areas of consideration for advancing quantitative data analysis in engineering education: operationalizing race and racism, sample sizes and data binning, claims with race as a variable, and promoting descriptive studies. We contend that engaging in deeper thought with these four areas in quantitative inquiry can help researchers engage with the difficult choices inherent to quantitative analyses.

show abstract

Section: Secondary Data Analysismentioning

confidence: 99%

Person‐centered analyses in quantitative studies about broadening participation for Black engineering and computer science students

Reeping

Lee

London

2023

J of Engineering Edu

View full text Add to dashboard Cite

show abstract

“…Previous studies have used SNA to measure community inclusiveness (Garcia et al, 2008; Pearson et al, 2018; Scott, 2000). Scott (2000) proposed the network inclusiveness index (ie, the proportion of network members interacting with other members).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, this global index cannot provide more detailed information on community inclusiveness regarding multiple demographic factors (gender, ethnicity, age, etc.). More recently, Garcia et al (2008) and Pearson et al (2018) addressed this issue by summarizing the degree centrality levels between groups with different demographic factors to analyse the community inclusiveness of gender and ethnicity.…”

Section: Network Analysis and Community Inclusivenessmentioning

confidence: 99%

“…Previous studies on social network analysis have shown centrality metrics are related to learning ability, engagement, and performance (Banati & Arora, 2014; Cadima et al, 2010; Claros et al, 2015; Liu et al, 2017). However, studies on how centrality within a community includes students from diverse demographics are rare (Pearson et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Share and embrace demographic and location diversity: Creating an Instagram‐based inclusive online learning community

Zhu

Hua

Zhu

et al. 2022

Brit J Educational Tech

View full text Add to dashboard Cite

It is critical to create an inclusive online learning environment for students with diverse demographic information studying in different environments, especially during the COVID‐19 pandemic when they are disconnected from peers. Guided to create an inclusive online learning community by situated learning theory and community of practice, both of which advocate learning in context and community, we invited 115 undergraduate students to post photos related to environmental psychology concepts and their surrounding environments and discussed their postings on Instagram over eight weeks. To understand the inclusiveness of the community and students' perception, we collected their posts by searching designated hashtags and interviewed representatives of participants using a stratified sampling strategy. Through network analysis of 272 posts and qualitative analysis of 22 in‐depth interviews, we found that when participants shared and discussed their surroundings and environmental psychology concepts on Instagram, their learning community was inclusive regarding gender, ethnicity, and program. Student participants' centrality and influence were more relevant to whether and how they expressed their identities in the community through posts. We further discuss how our findings could inform to create inclusive and active communities in the future. What is already known about this topic? The definition of inclusive education extends to diversity and accessibility. Social media can support online learning communities. What this paper adds? It explores the inclusiveness of an Instagram‐based learning community using network analysis. It suggests expressing identities in a learning community helps promote inclusiveness. Implications of this study for practice and/or policy It provides information to education practitioners that will help them create inclusive and active communities through social media. It explores the possibility of analysing the inclusiveness of a learning community through social network analysis.

show abstract

“…Rather, our literature searches show prior studies avoid reference ambiguity by focusing on simplified social environments, like single classrooms or online domains, where researchers can consistently collect accurate interaction data [14]- [16]. While these small-scale studies provide valuable insights on engineering students' networks, they neglect a significant portion of students' interactions [17], [18]. To analyze more holistic student networks, our research group is completing a large (i.e., 1000+ students), openresponse network study of all first-and second-year undergraduate engineering students at a large, public land grant university in the U.S. (details in [19]).…”

Section: Introductionmentioning

confidence: 98%

Uncovering Students’ Social Networks: Entity Resolution Methods for Ambiguous Interaction Data

Weaver,

Elliott

2023 ASEE Annual Conference &Amp; Exposition Proceedings

View full text Add to dashboard Cite

show abstract

Using Social Network Analysis to Study the Social Structures of Inclusion

Abstract: is an Ph.D. student at the University of Nevada, Reno. His research interest includes, social networks and the integration of diverse populations, engineering culture as well as engineering pedagogy. His education includes a B.S. and M.

Cited by 4 publications

References 21 publications

Person‐centered analyses in quantitative studies about broadening participation for Black engineering and computer science students

Person‐centered analyses in quantitative studies about broadening participation for Black engineering and computer science students

Share and embrace demographic and location diversity: Creating an Instagram‐based inclusive online learning community

Uncovering Students’ Social Networks: Entity Resolution Methods for Ambiguous Interaction Data

Contact Info

Product

Resources

About