Conference Agenda

Objective, aims, and theoretical framework

The aim of the study is to investigate how profiles of teacher stress and teacher-student interactions (TSI) at baseline predict the quality of implementation in the INTERACT individual video-based teacher coaching intervention.

TSI can be organized into three domains: teachers’ emotional support, classroom organization, and instructional support (Pianta et al. 2012). Studies of teacher stress and TSI quality indicate a negative association between the two phenomena (Corbine et al. 2019). Further, a meta-analysis documents the effects of video-based teacher coaching (Kraft et al. 2018). Still, the level of perceived stress may affect teachers’ performance in the classroom (Lazarus and Folkman, 1984). Consequently, there are reasons to assume that subgroups of teachers exist with different stress and TSI quality profiles. Little is known about the impact of teacher stress and TSI on the ability to implement a teacher coaching intervention with the quality needed to improve TSI.

Participants, data sources and methodology

The sample consists of 99 teachers (49 intervention group teachers) and 10 coaches participating in a cluster randomized control trial (cRCT). During the academic year, the teachers focus on emotional support, classroom organization, instructional support, and student engagement in seven coaching cycles using a strength-based approach. The intervention group teachers participated in the coaching cycles with an assigned coach about every 3rd week throughout one academic year. Each cycle consists of six steps. The coaching cycles are based on video recordings from the teacher’s own teaching and have a strength-based approach.

The intervention was implemented in the academic year 2022-2023. The current study draws on the baseline teacher reports of TSI (emotional support, classroom organization, and instructional support), as well as stress (due to workload and student behavior) collected prior to randomizing teachers. Further teacher and coach reported on the quality of implementation at the end of each of the seven coaching cycles. Teachers were recruited from 12 upper secondary schools in two Norwegian counties. Teachers at both academic (25%) and vocational tracks were included.

Findings

Exploring which profiles can be identified regarding the teachers’ reports of stress and TSI, four profiles were identified using latent profile analysis (LPA) using Mplus 8.10. The profiles represented groups of teachers with qualitatively and quantitatively different profiles of stress and TSI. It is reasonable to assume that teachers with different profiles may differ in their ability to implement an intervention with high quality (Humphrey et al 2016).

Theoretical and Educational relevance, and connection to conference theme:

Knowledge of for whom and under which conditions an intervention is effective, allows teacher coaching interventions to support TSI interactions to be more targeted and indicate for whom interventions may be particularly useful. Further, findings from the study may contribute to the understanding of the complexity of the association between teacher stress, TSI, and the quality of implementation of video-based teacher coaching interventions. This knowledge may be useful for teachers and school leaders to make data-informed decisions on teachers’ professional development.

Purpose and Research Questions

This year’s ICSEI explores the role and impact of quality professional teaching and learning, which can be supported through the mobilization of knowledge. Knowledge mobilization is the process of moving knowledge (i.e., evidence derived from research, data or practical experience) to where it will be most useful (Ward, 2017). Knowledge brokers, defined as actors that trade knowledge between entities not immediately connected (Weber & Yanovitzky, 2021), are central to this deeply relational process (Rickinson & Edwards, 2021; Ward, 2017). While previous literature illustrates the importance of social networks in the dissemination of knowledge (e.g., Authors, 2022; Farley-Ripple & Yun, 2021; Poortman & Brown, 2017), less is known about how knowledge brokers could harness the power of social networks to mobilize knowledge if presented with curated data and visualizations. Therefore, this study explores the following question: How do knowledge brokers harness social networks for the mobilization of their resources?

Theoretical Perspective

This study employs a social network perspective, which asserts that “relational ties among actors are primary and attributes of actors are secondary” (Wasserman & Faust, 1994, p. 8) in the study of the social environment and processes. A social network consists of actors that are connected through relationships (Wasserman & Faust, 1994).

Methods and Data

We selected six evidence-based and equity-focused organizations that are recognized as experts in their respective fields, and intentionally mobilize their resources to different levels of the education system, from K-12 schools to state-level policy contexts. We analyzed data from 41.5 hours of semi-structured interviews with these knowledge brokers. Some of these interviews were about how the participants would use the Twitter social networks that were visualized and organized around clusters in their work of mobilizing knowledge. We then coded the transcripts with an inductively developed codebook (Merriam & Tisdell, 2016).

Findings

Through the social network analysis and visualization, the knowledge brokers were able to learn more about the content of what their audiences were engaging in, and how they were connected. This understanding led them to strategize and plan several uses of this knowledge for their online and offline knowledge mobilization efforts. Online applications of the presented data included (a) honing social media presence to be more targeted, (b) which users and content to amplify, (c) strategically growing their online networks, (d) understanding the reach of their resources. Offline uses that they discussed included (a) informing their content, (b) updating their editorial calendar, (c) identifying people for collaborations (e.g., conferences, special issues). They were confident that these measures would improve their knowledge mobilization outcomes.

Implications

We demonstrated how knowledge brokers could use curated social network data and visualizations to improve their knowledge mobilization efforts. By highlighting the applications in online and offline spaces, this study provides evidence that knowledge brokers work on a social continuum where the movement of knowledge between online and offline is fluid (Authors, 2019). This understanding provides another perspective and approach to supporting knowledge brokers in their profoundly relational efforts for school effectiveness and improvement.

Educational accountability systems that assess school quality through academic performance metrics are ubiquitous. Recently, systems have deployed data instruments capturing functions of schooling beyond academic achievement: Chile’s DIA measures schools’ capacity to provide for students’ socioemotional needs (Weinstein & Bravo, 2023); the EU’s NESET developed a framework for socioemotional education recommending assessments of student learning (Cefai et al., 2018); the U.S.’s ESSA “school quality” indicators spark a potential shift toward evaluating care and equitable resource distribution (e.g. ISBE 5Essentials Survey). Such data use intends to challenge systems’ sole focus on achievement, which, on an individual level, shortchanges students’ holistic development (Datnow et al., 2022); on a societal level, scholars argue this focus diminishes trust and contributes to inequalities (Au, 2016; Ozga, 2013). Yet, even in systems pushing innovations in measuring climate and curriculum, metrics remain rooted in outcomes-based standardization (Author, 2023; Espeland & Stevens, 2008). Obscuring affective and structural dimensions of schooling, outcomes-based performance metrics assume communities have similar needs, interests, and resources — a myth eliding context-specific characteristics and historically-produced inequities (Espeland & Yung, 2019; Spade, 2015; Leonardo, 2007). The incommensurability of building school cultures of care and equity and collecting data about climate and curricula through performance metrics is troubling given that climate and curricula can themselves reproduce social norms and values (Keenan, 2017; Freire, 1970; Durkheim, 1956).

This paper argues that performance metrics — even those targeting non-academic purposes of schools — are ill-equipped to execute a new, important job: the deinstitutionalization of an exclusive focus on achievement and institutionalization of care and equity (Oliver, 1992). Accordingly, this paper imagines a path forward in school effectiveness and improvement by proposing a theory of “process metrics” — distinct from performance metrics — as a mechanism to disrupt the institutionalized relationship between status quo norms and values, administrative attention, and resource distribution. Integrating queer, critical, and institutional theories, I offer process metrics as “formalized system[s] of abstraction” that can be evaluated in terms of their quality and functionality in capturing both subjectivities and systems (Stinchcombe, 2001; Colyvas, 2012). Process metrics produce qualitative data (e.g. narratives) that reflect affect through evolving, student- & community-centered protocol (Vasudevan et al., 2022; Ghaziani & Brim, 2019; Tuck & Yang, 2013); they center relations over outcomes, assume relations are ever-changing, and are adaptable across organizations.

Upon developing process metrics, we should not expect them to stick. Innovation scholarship shows implementation is fraught, especially when challenging durable systems centered around dominant groups (Klein & Sorra, 1996). This paper shares principles for measuring care-based, equity-oriented, and youth-centered educational processes but also offers propositions for their institutionalization. It holds: institutionalizing process metrics (1) is a multi-level process; it does not happen only at individual schools but is embedded in educational systems’ infrastructure (Spillane et al., 2019); (2) is a process and outcome; it varies not only in its strength (reliability) but in its breadth, reach, and range (Anderson & Colyvas, forthcoming); (3) requires sustained collaboration through coupling, bundling, and nesting modes of reproduction (Anderson & Colyvas, 2021).