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Technology, Knowledge and Learning

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22-07-2023 | Original research

Multimodal Learning Analytics and Neurofeedback for Optimizing Online Learners’ Self-Regulation

Authors: Insook Han, Iyad Obeid, Devon Greco

Published in: Technology, Knowledge and Learning | Issue 4/2023

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Abstract

This report describes the use of electroencephalography (EEG) to collect online learners’ physiological information. Recent technological advancements allow the unobtrusive collection of live neurosignals while learners are engaged in online activities. In the context of multimodal learning analytics, we discuss the potential use of this new technology for collecting accurate information on learners’ concentration levels. When combined with other learner data, neural data can be used to analyze and predict self-regulated behaviors during online learning. We further suggest the use of machine learning algorithms to provide optimal live neurofeedback to train online learners’ brains to improve their self-regulated learning behaviors. The challenges of EEG and neurofeedback in online educational settings are also discussed.

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Title: Multimodal Learning Analytics and Neurofeedback for Optimizing Online Learners’ Self-Regulation
Authors: Insook Han
Iyad Obeid
Devon Greco
Publication date: 22-07-2023
Publisher: Springer Netherlands
Published in: Technology, Knowledge and Learning / Issue 4/2023
Print ISSN: 2211-1662
Electronic ISSN: 2211-1670
DOI: https://doi.org/10.1007/s10758-023-09675-5

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