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Small group interactions and interactions with near‐peer instructors such as learning assistants serve as fertile opportunities for student learning in undergraduate active learning classrooms. To understand what students take away from these interactions, we need to understand how and what they learn during the moment of their interaction. This study builds on practical epistemology analysis to develop a framework to study this in‐the‐moment learning during interactions by operationalizing it through the lens of discourse change and continuity toward three ends. Using video recordings of students and learning assistants interacting in a variety of contexts including remote, in‐person, and hybrid classrooms in introductory chemistry and physics at two universities, we developed an analytical framework that can characterize learning in the moment of interaction, is sensitive to different kinds of learning, and can be used to compare interactions. The framework and its theoretical underpinnings are described in detail. In‐depth examples demonstrate how the framework can be applied to classroom data to identify and differentiate different ways in which in‐the‐moment learning occurs. 

In the 21st century with the rise of computing power, it has become increasingly important to create opportunities for students to learn to work with large, authentic, complex (LAC) data across multiple disciplines. DataFest, a hackathon style undergraduate event, creates a space for such inquiry due to the collaborative, data-driven, open-problem, real-world relevant nature of the challenge it presents. We present preliminary findings from research that explores how teams at DataFest leverage and integrate multidisciplinary tools and domain knowledge to engage productively with the data investigation process. Implications for statistics and data science education are discussed.