Multimodal Learning Experience for Deliberate Practice

Daniele Di Mitri; Jan Schneider; Bibeg Limbu; Khaleel Asyraaf Mat Sanusi; Roland Klemke

doi:10.1007/978-3-031-08076-0_8

Multimodal Learning Experience for Deliberate Practice

Daniele Di Mitri, Jan Schneider, Bibeg Limbu, Khaleel Asyraaf Mat Sanusi, Roland Klemke

Web Information Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Chapter › Scientific › peer-review

7 Citations (Scopus)

31 Downloads (Pure)

Abstract

While digital education technologies have improved to make educational resources more available, the modes of interaction they implement remain largely unnatural for the learner. Modern sensor-enabled computer systems allow extending human-computer interfaces for multimodal communication. Advances in Artificial Intelligence allow interpreting the data collected from multimodal and multi-sensor devices. These insights can be used to support deliberate practice with personalised feedback and adaptation through Multimodal Learning Experiences (MLX). This chapter elaborates on the approaches, architectures, and methodologies in five different use cases that use multimodal learning analytics applications for deliberate practice.

Original language	English
Title of host publication	The Multimodal Learning Analytics Handbook
Editors	Michail Giannakos, Daniel Spikol, Daniele Di Mitri, Kshitij Sharma, Xavier Ochoa, Rawad Hammad
Place of Publication	Cham
Publisher	Springer
Pages	183-204
Number of pages	22
ISBN (Electronic)	978-3-031-08076-0
ISBN (Print)	978-3-031-08075-3
DOIs	https://doi.org/10.1007/978-3-031-08076-0_8
Publication status	Published - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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978-3-031-08076-0_8Final published version, 533 KB

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abstract = "While digital education technologies have improved to make educational resources more available, the modes of interaction they implement remain largely unnatural for the learner. Modern sensor-enabled computer systems allow extending human-computer interfaces for multimodal communication. Advances in Artificial Intelligence allow interpreting the data collected from multimodal and multi-sensor devices. These insights can be used to support deliberate practice with personalised feedback and adaptation through Multimodal Learning Experiences (MLX). This chapter elaborates on the approaches, architectures, and methodologies in five different use cases that use multimodal learning analytics applications for deliberate practice.",

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Multimodal Learning Experience for Deliberate Practice. / Di Mitri, Daniele; Schneider, Jan; Limbu, Bibeg et al.
The Multimodal Learning Analytics Handbook. ed. / Michail Giannakos; Daniel Spikol; Daniele Di Mitri; Kshitij Sharma; Xavier Ochoa; Rawad Hammad. Cham: Springer, 2022. p. 183-204.

Research output: Chapter in Book/Conference proceedings/Edited volume › Chapter › Scientific › peer-review

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AU - Schneider, Jan

AU - Limbu, Bibeg

AU - Mat Sanusi, Khaleel Asyraaf

AU - Klemke, Roland

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PB - Springer

CY - Cham

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Multimodal Learning Experience for Deliberate Practice

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