Many school-improvement efforts include time for teacher collaboration, with the assumption that teachers’ collective work supports instructional improvement. However, not all collaboration equally supports learning that would support improvement. As a part of a 5-year study in two urban school districts, we collected video records of more than 100 mathematics teacher workgroup meetings in 16 different middle schools, selected as “best cases” of teacher collaboration. Building off of earlier discursive analyses of teachers’ collegial learning, we developed a taxonomy to describe how conversational processes differentially support teachers’ professional learning. We used the taxonomy to code our corpus, with each category signaling different learning opportunities. In this article, we present the taxonomy, illustrate the categories, and report the overall dearth of meetings with rich learning opportunities, even in this purposively sampled data set. This taxonomy provides a coding scheme for other researchers, as well as a map for workgroup facilitators aiming to deepen collaborative conversations.

Purpose Though test-based accountability policies seek to redress educational inequities, their underlying theories of action treat inequality as a technical problem rather than a political one: data point educators toward ameliorative actions without forcing them to confront systemic inequities that contribute to achievement disparities. To highlight the problematic nature of this tension, the purpose of this paper is to identify key problems with the techno-rational logic of accountability policies and reflect on the ways in which they influence teachers’ data-use practices. Design/methodology/approach This paper illustrates the data use practices of a workgroup of sixth-grade math educators. Their meeting represents a “best case” of commonplace practice: during a full-day professional development session, they used data from a standardized district benchmark assessment with support from an expert instructional leader. This sociolinguistic analysis examines episodes of data reasoning to understand the links between the educators’ interpretations and instructional decisions. Findings This paper identifies three primary issues with test-based accountability policies: reducing complex constructs to quantitative variables, valuing remediation over instructional improvement, and enacting faith in instrument validity. At the same time, possibilities for equitable instruction were foreclosed, as teachers analyzed data in ways that gave little consideration of students’ cultural identities or funds of knowledge. Social implications Test-based accountability policies do not compel educators to use data to address the deeper issues of equity, thereby inadvertently reinforcing biased systems and positioning students from marginalized backgrounds at an educational disadvantage. Originality/value This paper fulfills a need to critically examine the ways in which test-based accountability policies influence educators’ data-use practices.

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Teacher collaboration is often assumed to support school’s ongoing improvement, but it is unclear how formal learning opportunities in teacher workgroups shape informal ones. In this mixed methods study, we examined 77 teacher collaborative meetings from 24 schools representing 116 teacher pairs. We coupled qualitative analysis of the learning opportunities in formal meetings with quantitative analysis of teachers’ advice-seeking ties in informal social networks. We found that teachers’ coparticipation in learning-rich, high-depth meetings strongly predicted the formation of new advice-seeking ties. What is more, these new informal ties were linked to growth in teachers’ expertise, pointing to added value of teachers’ participation in high-depth teacher collaboration.

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