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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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
Publisher	Springer, Cham
Pages	183-204
Number of pages	22
Edition	1
ISBN (Electronic)	9783031080760
ISBN (Print)	9783031080753, 9783031080784
DOIs	https://doi.org/10.1007/978-3-031-08076-0_8
Publication status	Published - Oct 2023

Keywords

Deliberate practice
Intelligent tutoring systems
Multimodal interfaces
Psychomotor learning
Sensor devices

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

Cite this

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title = "Multimodal Learning Experience for Deliberate Practice",

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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note = "Publisher Copyright: {\textcopyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.",

year = "2023",

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language = "English",

isbn = "9783031080753",

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editor = "Michail Giannakos and Daniel Spikol and \{Di Mitri\}, Daniele and Kshitij Sharma and Xavier Ochoa and Rawad Hammad",

booktitle = "The Multimodal Learning Analytics Handbook",

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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. 1. ed. Springer, Cham, 2023. p. 183-204.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Multimodal Learning Experience for Deliberate Practice

AU - Di Mitri, Daniele

AU - Schneider, Jan

AU - Limbu, Bibeg

AU - Mat Sanusi, Khaleel Asyraaf

AU - Klemke, Roland

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

PY - 2023/10

Y1 - 2023/10

N2 - 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.

AB - 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.

KW - Deliberate practice

KW - Intelligent tutoring systems

KW - Multimodal interfaces

KW - Psychomotor learning

KW - Sensor devices

U2 - 10.1007/978-3-031-08076-0_8

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SN - 9783031080784

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EP - 204

BT - The Multimodal Learning Analytics Handbook

A2 - Giannakos, Michail

A2 - Spikol, Daniel

A2 - Di Mitri, Daniele

A2 - Sharma, Kshitij

A2 - Ochoa, Xavier

A2 - Hammad, Rawad

PB - Springer, Cham

ER -

Multimodal Learning Experience for Deliberate Practice

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Keywords

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