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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.

 

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. 

This symposium explores the empirical relationship between two theoretically distinct uses of the construct of positioning in the learning sciences. To do so, it brings together different studies that examine teaching and learning in STEM classrooms that incorporate both embodied and social aspects of positioning. These examples contribute to answering the question: How does simultaneously considering students’ and teachers’ embodied movements and social positioning offer new insights into studies of STEM classroom learning? Together, these studies show how different types of positioning are tightly related to one another, suggesting that more research is needed to understand the complex relationships between the physical, social, and epistemic positions in research and design of learning environments.