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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-05-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fnhum.2017.00286/full |
| _version_ | 1818579535899131904 |
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| author | Carina Walter Wolfgang Rosenstiel Martin Bogdan Peter Gerjets Martin Spüler |
| author_facet | Carina Walter Wolfgang Rosenstiel Martin Bogdan Peter Gerjets Martin Spüler |
| author_sort | Carina Walter |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-16T07:03:15Z |
| format | Article |
| id | doaj.art-ea4a347f80d5403b94e97ea0de484218 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-16T07:03:15Z |
| publishDate | 2017-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-ea4a347f80d5403b94e97ea0de4842182022-12-21T22:40:06ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612017-05-011110.3389/fnhum.2017.00286248153Online EEG-Based Workload Adaptation of an Arithmetic Learning EnvironmentCarina Walter0Wolfgang Rosenstiel1Martin Bogdan2Peter Gerjets3Martin Spüler4Department of Computer Engineering, Eberhard-Karls University TübingenTübingen, GermanyDepartment of Computer Engineering, Eberhard-Karls University TübingenTübingen, GermanyDepartment of Computer Engineering, University of LeipzigLeipzig, GermanyKnowledge Media Research CenterTübingen, GermanyDepartment of Computer Engineering, Eberhard-Karls University TübingenTübingen, GermanyIn 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.http://journal.frontiersin.org/article/10.3389/fnhum.2017.00286/fullPassive brain-computer interface (BCI)Cognitive workloadElectroencephalography (EEG)Online AdaptationNeurotutortutoring system |
| spellingShingle | Carina Walter Wolfgang Rosenstiel Martin Bogdan Peter Gerjets Martin Spüler Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment Frontiers in Human Neuroscience Passive brain-computer interface (BCI) Cognitive workload Electroencephalography (EEG) Online Adaptation Neurotutor tutoring system |
| 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 | Passive brain-computer interface (BCI) Cognitive workload Electroencephalography (EEG) Online Adaptation Neurotutor tutoring system |
| url | http://journal.frontiersin.org/article/10.3389/fnhum.2017.00286/full |
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