Cultivating Teachers’ Social Justice-Forward Adaptive Expertise Through Generative Professional Learning Spaces

• In Event: Reconfiguring teacher learning as joy and connection: Longitudinal pathways for collaboration in justice-centered science instruction

Abstract

Objectives/Purposes
Developing adaptive expertise is central for teachers to deepen their understanding of justice-forward teaching and expand their repertoire of instructional approaches and strategies to draw from, change, or innovate. This qualitative study addresses a need “to understand how particular kinds of learning experiences develop adaptive expertise” (Bransford, et al, 2000), specifically, for learning to teach for social justice: (1) What is the nature of adaptive expertise in teaching for social justice? (2) What teacher learning processes are cultivated in developing justice-forward teaching? (3) How is justice-forward adaptive expertise fostered in collaborative, job-embedded, and inquiry-based professional learning settings?

Perspectives/Theoretical Framework
Adaptive expert teachers are continuous learners who regularly reflect and flexibly adapt knowledge, skills, and dispositions to address novel dilemmas or situations through the creation of new approaches, procedures, or solutions (Schwartz, Bransford, & Sears, 2005). Adaptive expert teachers focus first on understanding a problem, involving “thinking in terms of core concepts or big ideas” as opposed to novices who may first seek “correct formulas and pat answers that fit their everyday intuitions” (Bransford et al, 2000, p. 49). Knowledge and skills developed through adaptive expertise are dependent upon experiences, sociopolitical, and teacher-learning contexts (Carbonell et al, 2014).

Building adaptive expertise is a “developmental process observable through the problem-solving skills of individuals” (Carbonell et al, 2014), which entails a generative praxis of learning to teach for social justice (Ball, 2009, 2012). Adaptive expertise is closely tied to teachers' growing sophistication of practices that integrate science and language learning, asset-based approaches, and connections to social justice and culturally relevant content (Lee et al., 2023; Zeidler, 2014).
Methods
Data come from a multiyear, multisite project that examines teacher learning and student discourse in science, language, and literacy instruction in dual language and multilingual classrooms. Drawing on iterative coding and qualitative data analysis, this study focuses on how adaptive expertise was cultivated across three cohorts of teachers (about 30 in total) across their first five years of teaching and learning.

Data sources
Data sources include: video/audio data from professional learning communities (PLCs) utilizing a critical friends group (CFG) protocol (National School Reform Faculty, 2024), studio days, classroom teaching observations, participant interviews, and relevant documents such as lesson materials and student work.

Results
Findings show that teachers developed justice-forward adaptive expertise through noticing, naming, pedagogical problem solving, and sensemaking guided by their pedagogical commitments and explicit attention to social justice principles (e.g. PASTEL commitments). Teachers continually refined and expanded their instructional repertoire and deepened understandings and enactment of justice-forward teaching. Findings show how generative, inquiry-based professional learning spaces cultivated justice-forward adaptive expertise through collective pedagogical problem solving, teacher agency, and knowledge integration from multiple practical, theoretical, and contextual sources.

Scholarly Significance of the Study
This study challenges static views of teacher learning and shows the critical role of adaptive expertise in justice-forward teaching. The paper shows how adaptive expertise is cultivated in job-embedded learning spaces that grow teachers’ problem-solving skills and repertoire through a generative praxis that integrates theory and practice.

Authors

• Kerry Soo Von Esch, Seattle University

• Hsin-Jung Li, University of Washington

• Matt Stewart, University of Washington

• Sarah Jaewon Lee, University of Washington