The Multimodal Learning Analytics Handbook

Book


Giannakos, M., Spikol, D., Di Mitri, D., Sharma, K., Ochoa, X. and Hammad, R. (ed.) 2022. The Multimodal Learning Analytics Handbook. Springer, Cham.
EditorsGiannakos, M., Spikol, D., Di Mitri, D., Sharma, K., Ochoa, X. and Hammad, R.
Abstract

This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA). The field of MMLA is an emerging domain of Learning Analytics and plays an important role in expanding the Learning Analytics goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice in this field is how to develop theories about the analysis of human behaviors during diverse learning processes and to create useful tools that could augment the capabilities of learners and instructors in a way that is ethical and sustainable. Behind this area, the CrossMMLA research community exchanges ideas on how we can analyze evidence from multimodal and multisystem data and how we can extract meaning from this increasingly fluid and complex data coming from different kinds of transformative learning situations and how to best feed back the results of these analyses to achieve positive transformative actions on those learning processes. This handbook also describes how MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities. The book describes how this “virtual nature” allows MMLA to provide new insights into learning processes that happen across multiple contexts between stakeholders, devices and resources. Using such technologies in combination with machine learning, Learning Analytics researchers can now perform text, speech, handwriting, sketches, gesture, affective, or eye-gaze analysis, improve the accuracy of their predictions and learned models and provide automated …

KeywordsMMLA; Learning Analytics
Year2022
PublisherSpringer, Cham
Publication dates
Online08 Oct 2022
Print09 Oct 2022
Publication process dates
Deposited04 Jun 2024
Edition1
ISBN9783031080760
9783031080753
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-031-08076-0
Web address (URL)https://link.springer.com/book/10.1007/978-3-031-08076-0
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