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Candace Walkington; Mitchell J. Nathan; Jonathan Hunnicutt; Julianna Washington; Monique Zhou – Grantee Submission, 2024
Dynamic geometry software (DGS) has long been studied in mathematics education as a way for students to explore and interact with geometric objects and figures. Recent advances in Augmented Reality (AR) technologies that allow dynamic three-dimensional mathematical objects to appear in students' environment as holograms have changed the nature of…
Descriptors: Computer Software, Geometry, Computer Simulation, Educational Technology
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Candace Walkington; Mitchell J. Nathan; Jonathan Hunnicutt; Julianna Washington; Kasi Holcomb-Webb – Grantee Submission, 2022
Novel forms of technology, like shared Augmented Reality (AR) holograms, can spur the discovery of new hypotheses about cognition and how it is embodied and distributed. These holograms have affordances for exploration, collaboration, and learning that have never been seen before. In the present study, we examine the multimodal ways that high…
Descriptors: Geometry, Mathematics Instruction, Schemata (Cognition), Teaching Methods
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Kelsey E. Schenck; Doy Kim; Fangli Xia; Michael I. Swart; Candace Walkington; Mitchell J. Nathan – Grantee Submission, 2024
Access to body-based resources has been shown to augment cognitive processes, but not all movements equally aid reasoning. Interactive technologies, like dynamic geometry systems (DGS), potentially amplify the link between movement and geometric representation, thereby deepening students' understanding of geometric properties. This study…
Descriptors: Geometric Concepts, Task Analysis, Thinking Skills, Validity
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Nathan, Mitchell; Walkington, Candace; Swart, Michael – Grantee Submission, 2021
Findings synthesized across five empirical laboratory- and classroom-based studies of high school and college students engaged in geometric reasoning and proof production during single- and multi-session investigations (346 participants overall) are presented. The findings converge on several design principles for computer technologies to support…
Descriptors: Geometry, Mathematics Instruction, High School Students, Undergraduate Students
Steven Moore; John Stamper; Norman Bier; Mary Jean Blink – Grantee Submission, 2020
In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…
Descriptors: Learning Analytics, Educational Improvement, Classification, Learning Processes