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Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held i...

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Autores principales: Walter, Carina, Rosenstiel, Wolfgang, Bogdan, Martin, Gerjets, Peter, Spüler, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5448161/
https://www.ncbi.nlm.nih.gov/pubmed/28611615
http://dx.doi.org/10.3389/fnhum.2017.00286
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author Walter, Carina
Rosenstiel, Wolfgang
Bogdan, Martin
Gerjets, Peter
Spüler, Martin
author_facet Walter, Carina
Rosenstiel, Wolfgang
Bogdan, Martin
Gerjets, Peter
Spüler, Martin
author_sort Walter, Carina
collection PubMed
description In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.
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spelling pubmed-54481612017-06-13 Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment Walter, Carina Rosenstiel, Wolfgang Bogdan, Martin Gerjets, Peter Spüler, Martin Front Hum Neurosci Neuroscience In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration. Frontiers Media S.A. 2017-05-30 /pmc/articles/PMC5448161/ /pubmed/28611615 http://dx.doi.org/10.3389/fnhum.2017.00286 Text en Copyright © 2017 Walter, Rosenstiel, Bogdan, Gerjets and Spüler. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Walter, Carina
Rosenstiel, Wolfgang
Bogdan, Martin
Gerjets, Peter
Spüler, Martin
Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment
title Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment
title_full Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment
title_fullStr Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment
title_full_unstemmed Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment
title_short Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment
title_sort online eeg-based workload adaptation of an arithmetic learning environment
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5448161/
https://www.ncbi.nlm.nih.gov/pubmed/28611615
http://dx.doi.org/10.3389/fnhum.2017.00286
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