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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

verfasst von: Insook Han, Iyad Obeid, Devon Greco

Erschienen in: Technology, Knowledge and Learning | Ausgabe 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.

Vorheriger Artikel Undergraduate Game-Based Student Response Systems (SRSs): A Systematic Review

Nächster Artikel Exploring the Potential of ChatGPT as an Educational Technology: An Emerging Technology Report

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Titel: Multimodal Learning Analytics and Neurofeedback for Optimizing Online Learners’ Self-Regulation
verfasst von: Insook Han
Iyad Obeid
Devon Greco
Publikationsdatum: 22.07.2023
Verlag: Springer Netherlands
Erschienen in: Technology, Knowledge and Learning / Ausgabe 4/2023
Print ISSN: 2211-1662
Elektronische ISSN: 2211-1670
DOI: https://doi.org/10.1007/s10758-023-09675-5

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