Using Social Network Analysis to Investigate Teacher Induction and Retention

Focus of the workshop and relevance to the science teacher education: The demand for a qualified, competent, and stable K12 science teaching workforce is being emphasized now more strongly than ever. Reports from the National Academies (National Research Council, 2005, 2010), the President’s Council of Advisors on Science and Technology (Lander & Gates Jr, 2010; President’s Council of Advisors on Science Technology, 2012), and even directly from the White House (2012) attest to the need to prepare and retain highly qualified and skilled science teachers – and science teacher leaders. One rationale underlying this common focus has been the goal of having highly qualified STEM teachers who can inspire students to choose STEM careers and maintain our country’s global competitiveness. In recent years, multiple programs funded by federal (e.g., Race to the Top, NSF’s Noyce Scholarship Program) and private entities (e.g., Teach for America, Woodrow Wilson Scholars, PhysTEC, and UTeach) alike have been working to prepare candidates for the K12 teaching community, especially in the highest need schools. Too few of these teacher preparation programs, though, focus on understanding the high turnover rate among early-career science teachers. Devising effective, scalable, and sustainable frameworks for retaining these educators for the first five years of employment and helping them grow as teacher leaders is essential if we are to meet our collective goals. However, we are not aware of reports in the literature of how projects like the National Science Foundation’s Robert Noyce Program successfully prepared and inducted STEM teachers into high-needs K12 schools so that similar teacher preparation programs and future PIs could tailor their approaches based on successful outcomes from Noyce Programs. In the extant STEM induction literature, most studies detail qualitative experiences within individual contexts that make comparisons or generalizable inferences about key design features difficult to ascertain. The larger research project in which this workshop proposal is embedded is designed to investigate the successes (and failures) of five federally-funded teacher preparation projects across diverse geographical, disciplinary, and school contexts. Findings from that larger project will be based on data collected from the Social Network Analysis Survey that is the focus of this workshop.

Previous studies regarding teacher retention and attrition investigated relationships between personal, professional, and workplace characteristics and dispositions towards leaving the career (Borman & Dowling, 2008; Macdonald, 1999). Those studies that disaggregated science teachers from their peers generally did not show substantive differences between these groups in the reasons for choosing to persist or resign their positions. Our research maintains the integrity of communities of practice investigated through Social Network Analysis. Ingersoll and May’s (2012) analysis of the National Center for Education Statistics’ Teacher Follow-Up Study indicated that nearly two-thirds of US public science teachers who voluntarily left teaching in the 2003-2004 school year cited a desire to obtain a better job/career or because they were dissatisfied with their current position as their reasons. As the demographics of those teaching in public schools have shifted towards being both younger and less experienced over the past two decades (Ingersoll & Merrill, 2010; Polizzi, Jaggernauth, Ray, Callahan, & Rushton, 2015; Rushton et al., 2014), the loss of potential teacher leaders has become more relevant to the issue of turnover (Kelly & Northrop, 2015). In light of these outcomes, induction programs with goals that include retention have incorporated particular design features to address these concerns. Recently, large-scale empirical studies and review articles have sought to identify the most critical aspects of induction programs to improve retention. Whisnant, Elliott, and Pynchon (2005) and Howe (2006) assert that intensive, ongoing social supports that leverages the expertise of discipline-specific mentors, common planning, and collaboration is particularly important. However, much of the literature has viewed professional support communities during induction programs monolithically, not accounting for much of the possible (but measurable) variance in relational supports that characterize communities of practice (Cross, Laseter, Parker, & Velasquez, 2006; Penuel, Riel, Krause, & Frank, 2009). Given that successful induction programs can facilitate a pathway for new teachers into existing teacher communities, and induction programs improve retention (Ingersoll & Strong, 2011), empirical studies and the instruments like our Social Network Analysis Survey are needed to understand how interactions are mediating the trajectory from novice teachers to experienced teachers with the confidence to remain in the profession (i.e., they identify themselves as teachers).

In this project, with the aid of the social network perspective (Borgatti & Ofem, 2010), we take a more holistic approach by directly measuring the professional support structures in the communities of practice where new STEM teachers become embedded, the antecedents and consequences of those structures, and how geographic or broader teacher program features impact those phenomena. As a result, social network analysis will allow us to capture data specific to the contexts in which teachers operate, but through a quantitative design that can be compared across multiple teacher preparation programs. The goal is to identify the key drivers—whether structural, geographic, or programmatic—that lead to a stronger sense of efficacy and identity within the teaching profession, and in turn, lead to greater retention during the induction years. Ultimately, our purpose is aligned to the principles of design-based research (IES & NSF, 2013; Zheng, 2015). The data gathered on program features and communities of practice, and their effects on teacher retention, will be used to feed back into our evolving understanding of what makes the most effective induction programs. The outcomes from this iterative, design-based process will not only contribute to the research base in the area of strengthening induction programs, but it will also provide the development of instruments like our Social Network Analysis Survey needed to inform the development of similar teacher preparation programs in the future.

ASTE membership who would be most interested in this workshop and why: This ASTE workshop would attract members interested in factors influencing teacher induction and the development of social networks by new math and science teachers that contribute to and establish leadership capabilities. Educational researchers with interests in collecting data on social networks would also be interested in learning how to design, construct, display, and interpret data using the social network mapping techniques presented in this workshop.

Expertise/experience of the workshop presenters to present in the topic area: The presenters for this workshop were all Principal Investigators on funded math and/or science project that attracted and prepared highly qualified individuals into careers as teachers. Each individual teacher preparation program was successful by any measures in preparing significant numbers of prospective teachers. The presenters have now come together, with several additional colleagues from similar teacher preparation projects across the country, to investigate key structural, programmatic, and geographic features within and across their individual programs. The main goal of the larger research study is to contribute new knowledge about our collective ability to successfully prepare and retain highly qualified secondary STEM teachers for positions in public schools. The goals for this workshop are to provide participants with a unique opportunity to learn about the underpinnings of our larger study and how we constructed a survey to investigate the social networks of teachers from our projects. Participants in this workshop will see actual data for individuals and projects in our study and engage in the analysis of data displayed on those maps.

Learning objectives and assessment of those objectives: Workshop participants will:
• learn to read Social Network maps
• identify different densities of social networks on Social Network maps
• learn how items on the Social Network Survey can capture structural, programmatic, and/or geographic factors related to teacher preparation programs
• identify features on a social network map and how those features are interpreted
• see Social Network Analysis as a tool that has practical implications for understanding key features in teacher preparation programs
At the conclusion of the workshop participants will complete a Likert-scaled evaluation with 2-3 questions for each learning objective above.

Workshop activities/instructional strategies you will be using to meet the objectives: A handout describing significant information regarding each activity below will be provided to all participants.
• Communities of Practice and Social Networks Analysis – our theoretical grounding (5 minutes)
• Participants share experiences with new math or science teachers during induction (10 minutes, participant interactions)
• Development of our Social Network Analysis survey tool – seeking data on the density of structural, programmatic, and/or geographic links that contribute to greater self-efficacy (10 minutes)
• Sharing of exemplary Social Network Analysis maps – how data are represented and can be interpreted (20 minutes, participant interactions)
• Participant discussion and feedback on the utility of Social Network Analysis maps for 1) investigating social networks during induction, 2) possible implications of research findings for teacher preparation programs, and 3) potential for new math and science teachers to benefit from understanding key factors contributing to the development of their social networks (10 minutes)
• Workshop evaluation (5 minutes)

Describe how you will make yourself available/offer support to the participants for continuing their learning and collaboration after they return to their home institutions: Presenters will share contact information so participants can seek out support or responses to follow-up questions after the workshop. Presenters will also share data from this workshop evaluation with all participants following the workshop.