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 proceedingChapterAcademicpeer-review


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
PublisherSpringer, Cham
Number of pages22
ISBN (Electronic)9783031080760
ISBN (Print)9783031080753, 9783031080784
Publication statusPublished - Oct 2023


  • Deliberate practice
  • Intelligent tutoring systems
  • Multimodal interfaces
  • Psychomotor learning
  • Sensor devices


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