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Seamless LearningSensors for Seamless Learning

Seamless Learning: Sensors for Seamless Learning [The chapter highlights the role of sensors for supporting seamless learning experiences. In the first part, the relation between sensor tracking of learning activities and research around real-time feedback in educational situations is introduced. The authors present an overview of the kinds of sensor data that have been used for educational purposes in the literature. Secondly, the authors introduce the link between sensor data and educational interventions, and especially the role of building expert models from real-world expert tracking. The third part of the paper illustrates how educational AR applications have used sensor data for different forms of learning support. The authors present 15 design patterns that have been implemented in different educational AR applications that build on our analysis of sensor tracking. For future AR applications, the authors propose that the use of sensors for building expert performance models is essential for a variety of educational interventions.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Seamless LearningSensors for Seamless Learning

Part of the Lecture Notes in Educational Technology Book Series
Editors: Looi, Chee-Kit; Wong, Lung-Hsiang; Glahn, Christian; Cai, Su
Specht, Marcus; Hang, Limbu Bibeg; Barnes, Jan Schneider
Seamless Learning — Jan 31, 2019
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References (36)

Publisher
Springer Singapore
Copyright
© Springer Nature Singapore Pte Ltd. 2019
ISBN
978-981-13-3070-4
Pages
141 –152
DOI
10.1007/978-981-13-3071-1_7
Publisher site
See Chapter on Publisher Site

Abstract

[The chapter highlights the role of sensors for supporting seamless learning experiences. In the first part, the relation between sensor tracking of learning activities and research around real-time feedback in educational situations is introduced. The authors present an overview of the kinds of sensor data that have been used for educational purposes in the literature. Secondly, the authors introduce the link between sensor data and educational interventions, and especially the role of building expert models from real-world expert tracking. The third part of the paper illustrates how educational AR applications have used sensor data for different forms of learning support. The authors present 15 design patterns that have been implemented in different educational AR applications that build on our analysis of sensor tracking. For future AR applications, the authors propose that the use of sensors for building expert performance models is essential for a variety of educational interventions.]

Published: Jan 31, 2019

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