We find ourselves at the beginning of what might become an exciting new era in the field of Educational Change. Andy Hargreaves and Dennis Shirley anticipated a new era, what they called a Fourth Way, that, “pushes beyond standardization, data-driven decision making, and target-obsessed distractions to forge an equal and interactive partnership among the people, the profession, and their government” (2009, p. 71). Many different stakeholders are now working in partnership to improve schools, including, among others, educators, university-affiliated researchers, parent groups, for-profit and charity organisations, and government agencies. This plurality of participants has helped a more diverse research landscape emerge. A welcomed departure from a technocratic model of evidence-based education, which narrowly assumed that, “the only relevant research questions [were] questions about the effectiveness of educational means and techniques” (Biesta, 2007, p. 5). Instead, researchers are developing new collaborative research strategies (Penuel et al., 2020) and refining working arrangements, such as Research Practice Partnerships (Coburn et al., 2021; Coburn & Penuel, 2016) and Networked Improvement Communities (Bryk et al., 2011; Russell et al., 2019), to support engagement between multiple stakeholders and build collective capacity for sustained improvement across large school systems (Fullan, 2010).

However, as Stephen Ball aptly points out, “policy works by accretion and sedimentation rather than revolution; new policies add to and overlay old ones, with the effect that new principles and innovations are merged and conflated with older rationales and previous practices” (Ball, 2021, p. 63). Festering just beneath the surface of this supposed collaborative landscape is a policy strata where accountability was, and in many areas, remains the cornerstone of education policy (Smith & Benavot, 2019). Dominant school improvement models emanating from this policy context have similar characteristics- they involve mandates for teachers in the form of prescribed practices and specified outcome targets, and involve routine standardized testing to support comparisons of student academic achievement across schools (Afdal & Afdal, 2019; Biesta, 2009; DeLuca & Johnson, 2017; Fuller et al., 2008; Jackson & Temperley, 2007; Taubman & Savona, 2009). Even in national contexts like England where schools appear to be gaining autonomy through networked governance (Goldsmith & Kettl, 2009), organizational replication reigns (Peurach & Glazer, 2012) as government agencies continue to steer education provision, albeit from a distance (Whitty & Wisby, 2016), resulting in “coercive autonomy” (Greany & Higham, 2018) and schools well within the “shadow of hierarchy” (Davies, 2011).

This presentation reports on findings from a Mixed Methods Social Network Analysis project that traced the activities of a teacher innovator as they scaled up their own educational improvement initiative from a single-school pilot into a thirty-school regional program. The research is part of a larger effort to explore how teacher-led educational change can thrive in contexts dominated by networked governance. Specifically, this research asks- How can classroom teachers repurpose School Improvement Networks to replicate their own improvement initiatives in schools?

In England, School Improvement Networks, or educational systems in which a central hub organization works with outlet schools to enact change (Peurach & Glazer, 2012), is becoming the predominant model for large-scale school improvement. For example, the Education Endowment Foundation’s (EEF) Research Schools Network and the Department for Education (DfE) curriculum hubs and Teaching School Hubs are all systems where a lead school connects with outlet schools across a region to deliver common approaches for learning and teaching. Furthermore, by 2030, all schools in England are expected to be members of Multi-academy Trusts (MATs), or networks of schools, sometimes with dozens of members, controlled by lead schools and early joiners. Teachers can repurpose the relational infrastructure of networked governance to lead large-scale school improvement initiatives.

A Mixed Methods Social Network Analysis (MMSNA) design was used for this research (Crossley & Edwards, 2016; Domínguez & Hollstein, 2014; Froehlich et al., 2019). In the initial phase of the research, the frequency of school signups for the teacher-led initiative were recorded. This was done using time-stamped data from a google signup form. Interviews were then conducted with the program creator to understand their recruitment activities in the lead-up to surges in new program enrolments. Qualitative explanations of recruitment activities were then verified by additional data collection methods such as document analysis or further interviews with recruitment collaborators. Visualization tools from Social Network Analysis (SNA) were then used to construct network schemas, which detail the micro-steps of social processes using node and edge diagrams (Stadtfeld & Block, 2017). The aim of the MMSNA design was to identify highly efficient interorganizational recruitment mechanisms for practitioners wanting to scale up their own improvement initiatives. Social mechanisms are constellations of entities and their activities which regularly lead to a social phenomenon of interest (Hedstrom, 2005). The phenomenon of interest was a new program signup. The MMSNA protocol helped identify mechanistic evidence, or the empirical fingerprints (Beach, 2016) of various actors and their activities that led to clusters of new program signups. The MMSNA protocol resulted in several different types of relevant data being collected. To present the resulting recruitment mechanism, interactive network visualization tools were used to generate a joint display. Using interactivity to increase the quantity and diversity of data available to readers of network diagrams is a new development in network visualization. Until now, researchers have primarily deployed interactivity to support exploratory data analysis of large networks at various scales. The use of interactivity in this manner allows researchers to easily zoom in and zoom out of large networks to examine interesting structural configurations like small node clusters and structural holes within whole networks (Pirch et al., 2021). For this research, interactivity was used to demonstrate the explanatory potential of network visualizations by repurposing node and edge labels, along with other components of network diagrams, to display rich qualitative data about the formation of new relationships, which users could call upon by hovering or clicking their cursor, thereby preventing visual clutter and the reduction of perceptual efficiency.

This research resulted in the discovery of new network mechanism, termed here for the first time as Star Coalescence. Star Coalescence is a novel network mechanism that describes a series of activities resulting in two collaborators developing new and enduring relationships with each other’s previously separate alters. At the core of this network mechanism is a tendency for separate contacts of colleagues to become shared associates. However, Star Coalescence represents a more complex social phenomenon because the mechanism can trigger a cascade of triad closures between two sparsely connected professional networks. Causing the formation of many new triads between previously disconnected alters means this network mechanism has the potential to facilitate many interorganizational recruitment events. In this example of Star Coalescence, the program creator managed to solicit the help of a Maths Hub coordinator with their regional recruitment efforts. While the remit of Maths Hubs is expanding, their primary objective is to engage with maths teachers at schools within an assigned region to implement a particular pedagogy known as Teaching for Mastery. The forty regional Maths Hubs, overseen by the National Centre for Excellence in the Teaching of Mathematics (NCETM), have managed to create substantive regional School Improvement Networks. Soliciting the help of a Maths Hub Coordinator meant repurposing the existing relational infrastructure of School Improvement Networks, originally created to support organizational replication of a maths pedagogy scheme, to instead scale up their own school improvement initiative. After successfully running a single-school pilot, the program creator managed to scale their initiative up to a thirty-school regional implementation. Half of their signups were the result of their coordination with the Maths Hub Coordinator.

Ball, S. J. (2021). The Education Debate (Fourth). Bristol University Press. Beach, D. (2016). It’s all about mechanisms – what process-tracing case studies should be tracing. New Political Economy, 21(5), 463–472. https://doi.org/10.1080/13563467.2015.1134466 Biesta, G. (2007). Why ‘What Works’ Won’t Work: Evidence-Based Practice and the Democratic Deficit in Educational Research. Educational Theory, 57(1), 1–22. Biesta, G. (2009). Good education in an age of measurement: On the need to reconnect with the question of purpose in education. Educational Assessment, Evaluation and Accountability, 21(1), 33–46. https://doi.org/10.1007/s11092-008-9064-9 Bryk, A. S., Gomez, L. M., & Grunow, A. (2011). Getting ideas into action: Building networked improvement communities in education. In Frontiers in sociology of education (pp. 127–162). Springer. Crossley, N., & Edwards, G. (2016). Cases, Mechanisms and the Real: The Theory and Methodology of Mixed-Method Social Network Analysis. Sociological Research Online, 21(2), 13. Froehlich, D. E., Rehm, M., & Rienties, B. C. (2019). Mixed Methods Social Network Analysis: Theories and Methodologies in Learning and Education. Routledge. Fullan, M. (2010). All Systems Go: The Change Imperative for Whole System Reform. Corwin Press. http://ebookcentral.proquest.com/lib/gla/detail.action?docID=996270 Goldsmith, S., & Kettl, D. F. (2009). Unlocking the Power of Networks: Keys to High-Performance Government. Brookings Institution Press. Greany, T., & Higham, R. (2018). Hierarchy, Markets and Networks: Analysing the ‘self-improving school-led system’ agenda in England and the implications for schools. UCL Institute of Education Press. Hargreaves, A., & Shirley, D. (2009). The Fourth Way: The Inspiring Future for Educational Change. Corwin Press, Ontario Principals’ Council and the NSDC. Hedstrom, P. (2005). Dissecting the Social: On the Principles of Analytical Sociology. Cambridge University Press. Peurach, D. J., & Glazer, J. L. (2012). Reconsidering replication: New perspectives on large-scale school improvement. Journal of Educational Change, 13(2), 155–190. Pirch, S., Müller, F., Iofinova, E., Pazmandi, J., Hütter, C. V. R., Chiettini, M., Sin, C., Boztug, K., Podkosova, I., Kaufmann, H., & Menche, J. (2021). The VRNetzer platform enables interactive network analysis in Virtual Reality. Nature Communications, 12(1), Article 1. https://doi.org/10.1038/s41467-021-22570-w Stadtfeld, C., & Block, P. (2017). Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events. Sociological Science, 4, 318–352. https://doi.org/10.15195/v4.a14 Whitty, G., & Wisby, E. (2016). Education in England—A testbed for network governance? Oxford Review of Education, 42(3), 316–329. https://doi.org/10.1080/03054985.2016.1184873