Using Motion Capture Technologies to Provide Advanced Feedback and Scaffolds for Learning

Published on Jan 1, 2019
· DOI :10.1007/978-3-030-02631-8_7
Andreja Istenič Starčič8
Estimated H-index: 8
(University of Primorska),
William Mark Lipsmeyer1
Estimated H-index: 1
Lin Lin18
Estimated H-index: 18
(UNT: University of North Texas)
In this chapter, we take the stand that cognition and learning are embodied in psychomotor activities and socio-cultural contexts, and they are mediated by technologies on the enactive, iconic, and symbolic representational levels. We discuss motion or body movements as an integral part of cognition and learning. The particular focus is on the role of motion capture technologies in integrating body, sensorimotor engagement, and feedback in learning. Motion capture technologies may help assist learning in several ways: (1) fascilitating seamless human–computer interaction; (2) connecting the enactive learning to observation and to model-based learning; (3) linking body motion to psychological reactions and states. Traditionally, computer-based learning has supported visual and symbolic representations. Advanced motion capture technologies connect physical and virtual environments, support enactive representations, connect different types of representations, and provide smart and sophisticated feedback to improve learning.
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  • Citations (1)
#1Andreja Istenic Starcic (University of Primorska)H-Index: 1
#2William Mark Lipsmeyer (UNT: University of North Texas)H-Index: 1
Last.Lin Lin (UNT: University of North Texas)H-Index: 18
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#1Mina C. Johnson-Glenberg (ASU: Arizona State University)H-Index: 15
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