EEG-based prediction of cognitive workload induced by arithmetic: a step towards online adaptation in numerical learning

Numeracy is a key competency for living in our modern knowledge society. Therefore, it is essential to support numerical learning from basic to more advanced competency levels. From educational psychology it is known that learning is most effective when the respective content is neither too easy nor...

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Veröffentlicht in:ZDM Jg. 48; H. 3; S. 267 - 278
Hauptverfasser: Spüler, Martin, Walter, Carina, Rosenstiel, Wolfgang, Gerjets, Peter, Moeller, Korbinian, Klein, Elise
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
Springer
Springer Nature B.V
Schlagworte:
Adaptation
Addition
Algorithms
Arithmetic
Brain
Cognition & reasoning
Cognitive Processes
Cognitive Style
Competence
Computer Interfaces
Computers
Difficulty Level
Education
Educational Environment
Educational Technology
Electrodes
Electroencephalography
Electronic Learning
Learning
Learning Problems
Mathematics
Mathematics Education
Mathematics Instruction
Mathematics Skills
Medicine
Meta Analysis
Neurological Impairments
Numbers
Numeracy
Original Article
Prediction
School environment
Secondary Schools
Short Term Memory
Student Centered Learning
Student Evaluation
Teaching Methods
Word Problems (Mathematics)
Workload
Workloads
Brain-computer interface
Electroencephalography (EEG)
Numerical cognition
Cognitive workload
Arithmetic
ISSN:1863-9690, 1863-9704
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Zusammenfassung:Numeracy is a key competency for living in our modern knowledge society. Therefore, it is essential to support numerical learning from basic to more advanced competency levels. From educational psychology it is known that learning is most effective when the respective content is neither too easy nor too demanding in relation to learners’ prerequisites. However, so far it is difficult to assess individual’s cognitive workload independently from performance to adapt learning environments accordingly. In the present study, we aim at identifying learners’ cognitive workload induced by addition tasks of varying difficulty using electroencephalography (EEG). To this end, a classifier using specific features in the EEG-signal is trained to differentiate between different levels of task difficulty significantly above chance level and with high consistency over all participants. Importantly, our model even allows for the prediction of cognitive demands induced by the addition tasks in a cross-participant approach. Closer inspection of the crucial EEG features indicates that oscillations in the theta and alpha band recorded from parietal electrodes are most reflective of current task difficulty. In summary, we are able to differentiate cognitive workload of participants independently from performance based on data of only a small number of electrodes. This suggests that a reduced EEG-setup combined with cross-participant classification may be a feasible approach to assess learners’ cognitive workload.
Bibliographie:ObjectType-Article-1
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ISSN:1863-9690
1863-9704
DOI:10.1007/s11858-015-0754-8