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ZDM

, Volume 48, Issue 3, pp 267–278 | Cite as

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

  • Martin Spüler
  • Carina Walter
  • Wolfgang Rosenstiel
  • Peter Gerjets
  • Korbinian Moeller
  • Elise Klein
Original Article

Abstract

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.

Keywords

Electroencephalography (EEG) Brain-computer interface Numerical cognition Arithmetic Cognitive workload 

Notes

Acknowledgments

The authors would like to thank Philipp Wolter for programming the experimental setup and collecting the EEG data. The current research is supported by the Baden-Württemberg Stiftung (GRUENS), the German Research Foundation (DFG; SP 1533/2-1) and the Leibniz ScienceCampus Tübingen “Informational Environments.” Carina Walter is a doctoral student of the LEAD Graduate School [GSC1028], her work is funded by the Excellence Initiative of the German federal and state governments. Elise Klein is supported by the Leibniz-Competition Fund (SAW-2014-IWM-4) as well as by a Margarete-von-Wrangell Fellowship (European Social Fund and the Ministry of Science, Research and the Arts Baden-Württemberg). Korbinian Moeller, Wolfgang Rosenstiel and Peter Gerjets are principle investigators of the LEAD Graduate school at the Eberhard Karls University Tuebingen funded by the Excellence Initiative of the German federal government.

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Copyright information

© FIZ Karlsruhe 2016

Authors and Affiliations

  • Martin Spüler
    • 1
  • Carina Walter
    • 1
  • Wolfgang Rosenstiel
    • 1
  • Peter Gerjets
    • 2
    • 3
  • Korbinian Moeller
    • 2
    • 3
  • Elise Klein
    • 2
    • 4
  1. 1.Department of Computer EngineeringEberhard-Karls University TuebingenTübingenGermany
  2. 2.Leibniz-Institut für WissensmedienTübingenGermany
  3. 3.Department of PsychologyEberhard-Karls University TuebingenTübingenGermany
  4. 4.Department of Neurology, Section NeuropsychologyUniversity Hospital, RWTH Aachen UniversityAachenGermany

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