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With the urgent call for supporting science teachers to promote equity and justice through their daily work of teaching, there is a growing need for better understanding how science teachers come to engage in transformative teaching and learning that is equitably consequential. In this participatory design research project (Bang & Vossoughi, 2016), we created a professional learning context in which high school chemistry teachers engaged in a pedagogical imagining (Gutiérrez & Calabese Barton, 2015) by leveraging their teaching experiences, knowledge about students and communities, values, and concerns to create powerful learning contexts for Latinx and multilingual students from immigrant, low-income families. Drawing upon the perspective of learning as making and sharing of the world interwoven with making and sharing of selves (Warren et al, 2020), we analyzed teachers’ participations and discourses to examine teachers’ making and sharing that were equitably consequential. The findings illustrated three critical moments of teachers’ making and sharing where: (a) the teachers collectively developed shared pedagogical goals toward transformative learning while formulating agency, (b) the teachers and the researchers came to design a creative stoichiometry unit where students use chemistry to make their community better, and (c) the teachers came to be committed to being ‘intentional’ in their relational work to create a welcoming and safe learning environment using concrete pedagogical strategies. The analyses point out three design features of the professional learning context that were associated with the teachers’ consequential makings: (a) the use of a conceptual tool (i.e., ‘design principles’), (b) the power of “what if” discourses, and (c) creating a space for collective learning. Recommendations for designing professional learning context toward transformative teaching and learning are discussed. 

Despite increasing awareness about the role of classroom assessments in perpetuating educational inequities, the research community continues to struggle with how to support teachers to design and use classroom assessments for achieving equity. In response to recent calls to better connect learning theory to the design of classroom assessments, we explore the links among contemporary learning theories, classroom assessments, equity, and teachers’ professional learning. Building a conceptual argument that we should shift our attention from assessment tasks to a classroom activity system to better support minoritized students’ learning via classroom assessment, we examine how teachers participate in assessment codesign activities in two research-practice partnerships (RPPs), and then identify emerging tensions in relation to promoting equity. Each RPP drew upon contemporary learning theories—sociocognitive and sociocultural learning theories, respectively—to create a coherent system of curriculum, instruction, and assessment. The examples show that the tensions emerging from each project are at least partially related to the learning theory that led the researchers to set up professional learning settings in a particular way. Our findings suggest that managing these tensions is an inherent part of the work as researchers seek to support equitable student learning. We discuss specific implications for the assessment community. 

Agent-based modeling (ABM) assumes that behavioral rules affecting an agent's states and actions are known. However, discovering these rules is often challenging and requires deep insight about an agent's behaviors. Inverse reinforcement learning (IRL) can complement ABM by providing a systematic way to find behavioral rules from data. IRL frames learning behavioral rules as a problem of recovering motivations from observed behavior and generating rules consistent with these motivations. In this paper, we propose a method to construct an agent-based model directly from data using IRL. We explain each step of the proposed method and describe challenges that may occur during implementation. Our experimental results show that the proposed method can extract rules and construct an agent-based model with rich but concise behavioral rules for agents while still maintaining aggregate-level properties.