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Session Type: Paper Session
This session explores the use of embodied pedagogies—gesture, drawing, and model manipulation—to improve learning in mathematics. Four studies investigate these strategies across contexts: (1) comparing gestures and drawing in online statistics instruction; (2) examining gestures as scaffolds for learning vectors in physics; (3) assessing how prior knowledge influences the impact of drawing and gesture in data science; and (4) exploring how manipulating concrete and virtual models affects three-view drawing skills. Findings offer insights into optimizing instructional design to support diverse learners in complex STEM subjects.
Direction, Magnitude, and Torque: Mixed Methods Analysis of Embodied Scaffolds for Transfer of Learning - Mitchell J. Nathan, University of Wisconsin - Madison; Michael I. Swart, University of Wisconsin - Madison; Rosanne Lily Luu, University of Wisconsin - Madison; Matthew M. Grondin, University of Wisconsin - Madison; Henry McGinn Smith, North Carolina State University
Comparing Embodied Teaching Strategies in Online Statistics - Luke Rabelhofer, University of California - Riverside; Kinnari Atit, University of California - Riverside; Catherine Lussier, University of California - Riverside; Annie Stanfield Ditta, University of California - Riverside; Felipe Cruz, University of California - Riverside
Manipulating Instructional Models to Improve Three-View Drawing Learning - ziyi kuang, Shaanxi Normal University; Fuxing Wang, Central China Normal University; Xiangen Hu, University of Memphis
The Impact of Prior Knowledge on Learning Through Reenacted Drawing and Gesture - Icy Yunyi Zhang, University of Wisconsin - Madison; Ji Son, California State University - Los Angeles; Zoey Yi Zhao, University of California - Los Angeles