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This paper's focus is on the "middle" of partnerships for equity in science education. Middle is used in a temporal sense, meaning the time after the general purposes and terms of working together have been set and before outcomes have been achieved. The middle of the partnership also represents people interacting, bounded around the edges, by their institutional roles, norms, resources, and priorities. As co-authors who had not previously collaborated (or even met in person), we approached the construction of this manuscript as a dialogue where we learn by sharing narrations of experiences and values and principles. We were inspired by the conversational book between Horton and Freire (1990) and specifically their discussion of "Is it possible to just teach biology?" (p. 102). In our conversation, we illustrate the ways in which partnerships may make justice-oriented science education possible. Our focus is on complementary and contradictory knowledges, and ways of knowing, institutional resources and constraints, and strategies for making transformative change. We explore the middle of partnerships as a series of opportunities for learning and growing, caring for one another, and building solidarity spaces together.

As an immigrant, born to formerly undocumented parents who left Hong Kong for the United States with barely a middle school education and a pair of tourist visas, Tina Cheuk, was separated from her parents for four years. She later joined them at age seven. Upon arriving in the United States, she was one of three English Learners, (ELs) in her entire elementary school. She writes that she did not know what this label would signify until she became a classroom teacher in New York City 15 years later. In her current role as an assistant professor at California Polytechnic State University, part of a network of state universities that prepares approximately 6,000 California teachers annually for public school classrooms (California State University, 2019), Cheuk's work centers on historical and contemporary perspectives of culturally, racially, and linguistically marginalized peoples and environments. As a teacher educator, she prepares future teachers so that they can create more socially just classrooms through deepening their knowledge and practices toward greater civic engagement and advocacy for the most underserved students in their classrooms and schools. Cheuk reflects upon her own personal, professional, and political story because she has seen families torn apart by U.S. Immigration and Customs Enforcement, and that has strengthened her zeal to work more closely with advocacy organizations in pressuring lawmakers to protect and serve the needs of all students in public school classrooms. Because she believes the work educators do rests on layers upon layers of complex decision making, Cheuk has joined a new generation of activist leaders organized by Californians Together, a statewide advocacy coalition that brings together voices from all segments of the education community, including teachers, administrators, board members, parents, and civil rights non-profit leaders. This article provides a brief history of California Proposition 227, commonly known as the English-only bill, which severely limited instruction in students' home languages, and outlines the formation of Californians Together, which was a reactionary response to Proposition 227. More recently, Californians Together formed the EL Leadership and Legacy Initiative (ELLLI), of which Cheuk is a member. Cheuk closes the article by saying that even though her professional role sits within the community of teacher educators in the academy, she also has the opportunity to join forces with others to advance movement toward shared goals of creating a more socially just educational system. The work of reform then can be more tightly coupled and targeted in achieving the coalition's goals. Individual voices in professional educational learning spaces are weak, but by banding together in support of advancing educational and life outcomes for ELs, the impact of organized advocacy becomes far more coherent and powerful.

Assessment developers are increasingly using the developing technology of machine learning in transforming how to assess students in their science learning. I argue that these algorithmic models further embed the structures of inequality that are pervasive in the development of science assessments in how they legitimize certain language practices that protect the hierarchical standing of status quo interests. My argument is situated within the broader emerging ethical challenges around this new technology. I apply a raciolinguistic equity analysis framework in critiquing the "new black box" that reinforces structural forms of discrimination against the linguistic repertoires of racially marginalized student populations. The article ends with me sharing a set of tactical shifts that can be deployed to form a more equitable and socially-just field of machine learning enhanced science assessments.

In this paper, I argue that the practice of argumentation will have implications for students not only in science learning, but also, potential transfer in other disciplines. More importantly, the shift toward a shared discursive language practice of argumentation provides an opportunity for both content-area and language development teachers to build on each other's pedagogical knowledge around argumentation, design tasks that support both content and language development, and create a more coherent learning experience for students. For English language learners, this convergence in the discourse practice of argumentation from evidence is particularly salient because content area teachers will be expected to create a more communicative classroom that supports both content and language development.

The definition of public education is changing rapidly and radically in our current political landscape. Nowhere is this more apparent than in the debate over publicly funded school voucher systems, which direct taxpayer funds to parents to offset the cost of tuition at the private schools of their choice. Tina Cheuk and Rand Quinn address how three spheres of influence -- the courts, the executive branch, and public opinion -- are contributing to a slow dismantlement of the wall between the church and the state.

Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance tasks requiring argumentation must capture the many ways students can construct and evaluate arguments in science, yet such tasks are both expensive and resource‐intensive to score. In this study we explore how machine learning text classification techniques can be applied to develop efficient, valid, and accurate constructed‐response measures of students' competency with written scientific argumentation that are aligned with a validated argumentation learning progression. Data come from 933 middle school students in the San Francisco Bay Area and are based on three sets of argumentation items in three different science contexts. The findings demonstrate that we have been able to develop computer scoring models that can achieve substantial to almost perfect agreement between human‐assigned and computer‐predicted scores. Model performance was slightly weaker for harder items targeting higher levels of the learning progression, largely due to the linguistic complexity of these responses and the sparsity of higher‐level responses in the training data set. Comparing the efficacy of different scoring approaches revealed that breaking down students' arguments into multiple components (e.g., the presence of an accurate claim or providing sufficient evidence), developing computer models for each component, and combining scores from these analytic components into a holistic score produced better results than holistic scoring approaches. However, this analytical approach was found to be differentially biased when scoring responses from English learners (EL) students as compared to responses from non‐EL students on some items. Differences in the severity between human and computer scores for EL between these approaches are explored, and potential sources of bias in automated scoring are discussed. [ABSTRACT FROM AUTHOR]

In this paper, I argue that the practice of argumentation will have implications for students not only in science learning, but also, potential transfer in other disciplines. More importantly, the shift toward a shared discursive language practice of argumentation provides an opportunity for both content-area and language development teachers to build on each other's pedagogical knowledge around argumentation, design tasks that support both content and language development, and create a more coherent learning experience for students. For English language learners, this convergence in the discourse practice of argumentation from evidence is particularly salient because content area teachers will be expected to create a more communicative classroom that supports both content and language development. [ABSTRACT FROM AUTHOR]