Great Stuff from My Team in 2017

One of the great things about coming up in this profession is seeing all the great work my students (current and former) produce. As you may note, their work varies yet addresses some central themes. I hope you will read any papers that sound interesting.

Without further ado, here are some of the highlights from 2017:

Simpson, A., Bannister, N., & Matthews, G. (2017). Cracking her codes: understanding shared technology resources as positioning artifacts for power and status in CSCL environments. International Journal of Computer-Supported Collaborative Learning, 12(3), 221-249.
There is a positive relationship between student participation in computer-supported collaborative learning (CSCL) environments and improved complex problem-solving strategies, increased learning gains, higher engagement in the thinking of their peers, and an enthusiastic disposition toward groupwork. However, student participation varies from group to group, even in contexts where students and teachers have had extensive training in working together. In this study, we use positioning theory and interaction analysis to conceptualize and investigate relationships between student interactions across two partner pairs working with technology in an all-female cryptography summer camp and their negotiated positions of power and status. The analysis resulted in uneven participation patterns, unequal status orderings, and an imbalance of power in both comparison cases. We found a reflexive relationship between partner interactions around shared technology resources and negotiated positions of power and status, which leads us to conclude that interactions around technology function as an important indicator of negotiated positionings of power and status in CSCL settings, and vice-versa. With that said, we found qualitative differences in the ways emergent status problems impacted each team’s productivity with the cryptography challenge, which has important implications for future research on CSCL settings and classroom practice.
Chen, G. A. & Buell, J. Y. (2017). Of models and myths: Asian (Americans) in STEM and the neoliberal racial project. Race Ethnicity and Education, 1-19.
This paper examines historical and contemporary racializations of Asian(Americans) within the STEM system. The prevailing perception of Asian(Americans) as model minorities masks how their multiple and contradictory positionings in the STEM system perpetuate the neoliberal racial project and reproduce systems of racism and oppression. Through a multidisciplinary analysis of STEM education and industry, we demonstrate that the shifting racialization of Asian(Americans) secures advantages for White Americans by promoting meritocracy and producerism and justifies White supremacy. By serving these functions, the racialization of Asian(Americans) within the STEM system is central to the neoliberal racial project. This paper also suggests how STEM education researchers can reveal and resist, rather than veil and support, the neoliberal racial project in STEM.
Horn, I. S., Garner, B., Kane, B. D., & Brasel, J. (2017). A Taxonomy of Instructional Learning Opportunities in Teachers’ Workgroup Conversations. Journal of Teacher Education, 68(1), 41-54.
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.
Garner, Brette,  Jennifer Kahn Thorne, and Ilana Seidel Horn. “Teachers interpreting data for instructional decisions: where does equity come in?.” Journal of Educational Administration 55, no. 4 (2017): 407-426.

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.

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.

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.

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.

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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