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1. Can discussion boards disrupt gendered and racialized discussion patterns in math classes.

   Kim, M (January 2022, Proceedings of the Annual Conference on Research in Undergraduate Mathematics Education)

   Karunakaran, S.; Higgins, A. (Ed.)

   Social Network Analysis is a method to analyze individuals’ social accessibility and power. We adapt it to change inequitable issues in STEM postsecondary education. Equity issues in mathematics education, such as underrepresented women and racial disparities, are prevalent. With the social capital perspective, we investigate the demographic characteristics of influential students and their social networks. Seventeen participants are undergraduate students in an inquiry-oriented linear algebra course. The number of nominations on discussion boards as “Shout-out” is data to measure influence and map the social network. By analyzing data with UCINET, we found that (1) the most influential students are non-White males and the principal components of the network are male-dominant, and (2) there is a female-dominant small cluster and female students have reciprocal networks. This study suggests further discussions of (1) how discussion boards position students with the social capital perspective and (2) intersectionality, especially for women of color. 

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2. Can discussion boards disrupt gendered and racialized discussion patterns in math classes?

   Kim, M (January 2022, Proceedings of the 24th conference on research in undergraduate mathematics education)

   S. S. Karunakaran; A. Higgins (Ed.)

   Social Network Analysis is a method to analyze individuals’ social accessibility and power. We adapt it to change inequitable issues in STEM postsecondary education. Equity issues in mathematics education, such as underrepresented women and racial disparities, are prevalent. With the social capital perspective, we investigate the demographic characteristics of influential students and their social networks. Seventeen participants are undergraduate students in an inquiry-oriented linear algebra course. The number of nominations on discussion boards as “Shout-out” is data to measure influence and map the social network. By analyzing data with UCINET, we found that (1) the most influential students are non-White males and the principal components of the network are male-dominant, and (2) there is a female-dominant small cluster and female students have reciprocal networks. This study suggests further discussions of (1) how discussion boards position students with the social capital perspective and (2) intersectionality, especially for women of color. 

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3. Leveraging Social Capital to Broaden Participation in STEM

   https://doi.org/10.1177/2372732219895997  

   Saw, Guan K. (March 2020, Policy Insights from the Behavioral and Brain Sciences)

   Broadening participation in science, technology, engineering, and mathematics (STEM) is critical to the nation’s economic growth and national security. In K–12 and higher education, researchers and educators increasingly employ the concept of social capital to develop programs for improving STEM learning, motivation, and participation of young students. STEM social capital in education comprises STEM-oriented resources—whether instrumental, informational, or emotional—that students access through their social networks. Major theoretical perspectives, research evidence, and promising practices are associated with the concepts of social capital in STEM education. Students’ social capital in STEM education (derived from families, peers, teachers, and professional networks) demonstrably promotes their STEM educational outcomes and career paths. Inclusive STEM schools, mentoring, and after-school programs are some promising approaches that can enhance STEM social capital and outcomes of underrepresented students, particularly women, Blacks/Hispanics/Native Americans, youth with low socioeconomic status, and persons with disabilities. 

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4. What You Need to Succeed: Examining Culture and Capital in Biomedical Engineering Undergraduate Education

   Corple, D.; Zoltowski, C. B.; Eddington, S. M.; Brightman, A. O.; Buzzanell, P. M (June 2019, ASEE annual conference & exposition proceedings)

   The low numbers of women and underrepresented minorities in engineering has often been characterized as a ‘pipeline problem,’ wherein few members of these groups choose engineering majors or ‘leak out’ of the engineering education pipeline before graduating [1]. Within this view, the difficulty of diversifying the engineering workforce can be addressed by stocking the pipeline with more diverse applicants. However, the assumption that adding more underrepresented applicants will solve the complex and persistent issues of diversity and inclusion within engineering has been challenged by recent research. Studies of engineering culture highlight how the persistence of women and minorities is linked to norms and assumptions of engineering cultures (e.g., [2], [3]). For example, some engineering cultures have been characterized as masculine, leading women to feel that they must become ‘one of the guys’ to fit in and be successful (e.g., [4]). In the U.S., engineering cultures are also predominantly white, which can make people of color feel unwelcome or isolated [5]. When individuals feel unwelcome in engineering cultures, they are likely to leave. Thus, engineering culture plays an important role in shaping who participates and successfully persists in engineering education and practice. Likewise, disciplinary cultures in engineering education also carry assumptions about what resources students should possess and utilize throughout their professional development. For example, educational cultures may assume students possess certain forms of ‘academic capital,’ such as rigorous training in STEM subjects prior to college. They might also assume students possess ‘navigational capital,’ or the ability to locate and access resources in the university system. However, these cultural assumptions have implications for the diversity and inclusivity of educational environments, as they shape what kinds of students are likely to succeed. For instance, first generation college (FGC) students may not possess the same navigational capital as continuing generation students [5]. Under-represented minority (URM) students often receive less pre-college training in STEM than their white counterparts [6]. However, FGC and URM students possess many forms of capital that often are unrecognized by education systems, for example, linguistic capital, or the ability to speak in multiple languages and styles) [7], [8]. Educational cultures that assume everyone possesses the same kinds of capital (i.e. that of white, American, high SES, and continuing generation students) construct barriers for students from diverse backgrounds. Thus, we propose that examining culture is essential for understanding the underlying assumptions and beliefs that give rise to the challenging issues surrounding the lack of diversity and inclusion in engineering. This case study examines the culture of a biomedical engineering (BME) program at a large Midwestern university and identifies underlying assumptions regarding what sources of cultural and social capital undergraduate students need to be successful. By tracing when and how students draw upon these forms of capital during their professional development, we examine the implications for students from diverse backgrounds, particularly FGC and URM students. 

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5. AI Literacy for Underserved Students: Leveraging Cultural Capital from Underserved Communities for AI Education Research

   https://doi.org/10.1145/3706598.3713173  

   Cao, Huajie Jay; Choi, Kahyun; Park, Claire; Lee, Hee Rin (April 2025, ACM)

   As Artificial Intelligence (AI) continues to influence various aspects of society, the need for AI literacy education for K-12 students has grown. An increasing number of AI literacy studies aim to enhance students’ competencies in understanding, using, and critically evaluating AI systems. However, despite the vulnerabilities faced by students from underserved communities—due to factors such as socioeconomic status, gender, and race—these students remain underrepresented in existing research. To address this gap, this study focuses on leveraging the cultural capital that students acquire from their communities’ unique history and culture for AI literacy education. Education researchers have demonstrated that identifying and mobilizing cultural capital is an effective strategy for educating these populations. Through collaboration with 26 students from underserved communities—including those who are socioeconomically disadvantaged, female, or people of color—this paper identifies three types of cultural capital relevant to AI literacy education: 1) resistant capital, 2) communal capital, and 3) creative capital. The study also emphasizes that collaborative relationships between researchers and students are crucial for mobilizing cultural capital in AI literacy education research. 

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