Multimodal Learning Experience for Deliberate Practice

Book chapter (2022)

Authors

Daniele Di Mitri DIPF - Leibniz Institute for Research and Information in Education
Jan Schneider DIPF - Leibniz Institute for Research and Information in Education
B.H. Limbu Web Information Systems - EEMCS
Roland Klemke TH Köln
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2022 Daniele Di Mitri, Jan Schneider, B.H. Limbu, Khaleel Asyraaf Mat Sanusi, Roland Klemke
DOI: https://doi.org/10.1007/978-3-031-08076-0_8
More Info
expand_more

Published Date

2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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.

Files

978_3_031_08076_0_8.pdf
(pdf | 0.684 Mb)
- Embargo expired in 01-07-2023
Unknown license