Social network analysis is an analytical approach – including theoretical perspectives and methodological techniques – that focuses on the relationships among actors in a system using quantitative or qualitative data. Researchers using social network analysis aim to explore and understand patterns and structural properties of relationships and their implications for social action (Borgatti, Everett, & Johnson, 2013; Scott, 2017). This method allows researchers to explore an array of topics at multiple levels of the education system relevant to STEM education, including student discourse, teacher professional learning, and large-scale education reform.
The first webinar in a 2-part series, this presentation will provide an introduction to social network analysis. Researchers Kyle Fagan, PhD, and Ben Kalina from the American Institutes for Research will provide an introduction to social network analysis, including an overview of key considerations for data collection, analysis, and network visualization. They will review the kinds of research questions that can be answered with social network analysis, present the merits and limitations of this method, and share tools and resources to help the DRK-12 community apply this method to their projects.
The second webinar, held on April 20 at 1pm ET, will feature a panel of researchers. They will share how they used social network analysis in their work and discuss challenges they faced when using this method and the strategies they applied to overcome these challenges. Both webinars in the series are designed to be interactive. There will be time for audience participation and questions. Participants are encouraged to attend both sessions but may elect to attend just one of the two webinars in this series.
Who should attend?
The webinars are designed for past, current, and aspiring DRK-12 principal investigators and project teams interested in learning more and applying social network analysis. The webinar swill be geared toward those who do not have much experience with the research method, though participants more familiar with social network analysis may also benefit from joining.
- Kyle Fagan, PhD, is a researcher at the American Institutes for Research who works with state, regional, and local agencies on projects at the intersection of educational and community change. His work is primarily focused on addressing issues of equity through collaborative research, evaluation, and technical support, with a concentration in collaboration between education, health, and social services. Dr. Fagan is the technical lead for multiple projects using social network analysis, leading the design, data collection, analysis, and communication of findings. He also leads AIR’s efforts to build capacity for social network analysis through staff training, resource development, and thought partnership. He holds a MA in Applied Developmental and Educational Psychology and a PhD in Leadership, Policy, and Educational Change from Boston College.
- Ben Kalina is a Senior Researcher at the American Institutes for Research. His research experiences include developing and implementing qualitative research approaches and study designs, including social network analysis. Mr. Kalina studies the health of networks in education systems and uses network analysis to study interventions in education systems and how networks affect intended outcomes. Mr. Kalina served as a project director for studies to analyze social networks of Teach For America alumni and Education Pioneers alumni and how these networks may impact local education policies. Mr. Kalina holds a Master’s degree in Education and Politics from Teachers College, Columbia University.
Following participation, audience members will:
- Understand social network analysis terminology
- Identify the importance and benefits of social network analysis
- Understand key considerations for data collection, analysis, and network visualization
- Consider ways social network analysis might be useful in future research in STEM education
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These webinars are supported by a grant from the National Science Foundation (DRL-1813777). Opinions, findings, and conclusions or recommendations expressed do not necessarily reflect the views of the National Science Foundation.