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Fostering locally relevant and community-centered forms of science learning that develop students’ critical science agency problematizes a “one-size-fits-all” model of teacher learning; teachers must examine how community needs and resources, local inequities and justice issues, and curriculum materials can converge to design novel learning opportunities for science learners. This paper presents the core commitments of EMPOWER, a cross-institutional effort that aims to support teachers' sensemaking and adaptations of curriculum materials to promote student ownership, engagement, and relevance at multiple sites across the U.S. 

Designing physics courses that support students' activation and development of expert-like physics epistemologies is a significant goal of Physics Education Research. However, very little research has focused on how physics students' interactions with course structures resonate with different epistemological views. As part of a course redesign effort to increase student success in introductory physics, we interviewed introductory physics students about their experiences with course structures and their learning and belonging beliefs. We present here a case from this broader data corpus in which a student, Robyn, discusses his epistemological views of physics problem solving and his experiences with physics lectures, office hours, and discussion sections. We find that Robyn's physics epistemology manifests consistently across his interactions with each of these different course structures, suggesting a possible resonance between students' beliefs and their experiences with course structures and the value of further investigation into the potential merits of comprehensive course design. 

While classroom video data are detailed sources for mining student learning insights, their complex and unstructured nature makes them less than straightforward for researchers to analyze. In this paper, we compared the differences between the processes of expert- informed manual feature engineering and automated feature engi- neering using positional data for predicting student group interac- tion in four middle school and high school mathematics classroom videos. Our results highlighted notable differences, including im- proved model accuracy for the combined (manual features + au- tomated features) models compared to the only-manual-features models (mean AUC = .778 vs. .706) at the cost of feature interpretabil- ity, increased number of features for automated feature engineering (1523 vs. 178), and engineering approach (domain-agnostic in au- tomated vs. domain-knowledge-informed in manual). We carried out feature importance analyses and discuss the implications of the results for potentially augmenting human perspectives about quali- tatively coding classroom video data by confirming and expanding views on which body areas and characteristics may be relevant to the target interaction behavior. Lastly, we discuss our study’s limitations and future work. 

Abstract Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human‐driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has not yet achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers' use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting‐edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human‐driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.