Multimodal Learning Experience for Deliberate Practice

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

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

7 Citations (Scopus)
44 Downloads (Pure)


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 languageEnglish
Title of host publicationThe Multimodal Learning Analytics Handbook
EditorsMichail Giannakos, Daniel Spikol, Daniele Di Mitri, Kshitij Sharma, Xavier Ochoa, Rawad Hammad
Place of PublicationCham
Number of pages22
ISBN (Electronic)978-3-031-08076-0
ISBN (Print)978-3-031-08075-3
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
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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