Article Version of Record

Cognitive state monitoring and the design of adaptive instruction in digital environments: Lessons learned from cognitive workload assessment using a passive brain-computer interface approach.

Author(s) / Creator(s)

Gerjets, P.
Walter, C.
Rosenstiel, W.
Bogdan, M.
Zander, T. O.

Other kind(s) of contributor

Leibniz-Institut für Wissensmedien

Abstract / Description

According to Cognitive Load Theory, one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners’ current working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners’ WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing EEG data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work.

Persistent Identifier

Date of first publication

2014

Journal title

Frontiers in Neuroscience.

Volume

8:385

Publication status

publishedVersion

Review status

peerReviewed

Is version of

10.3389/fnins.2014.00385

Citation

  • Author(s) / Creator(s)
    Gerjets, P.
  • Author(s) / Creator(s)
    Walter, C.
  • Author(s) / Creator(s)
    Rosenstiel, W.
  • Author(s) / Creator(s)
    Bogdan, M.
  • Author(s) / Creator(s)
    Zander, T. O.
  • Other kind(s) of contributor
    Leibniz-Institut für Wissensmedien
  • PsychArchives acquisition timestamp
    2017-08-28T11:11:04Z
  • Made available on
    2017-08-28T11:11:04Z
  • Date of first publication
    2014
  • Abstract / Description
    According to Cognitive Load Theory, one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners’ current working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners’ WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing EEG data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work.
  • Publication status
    publishedVersion
  • Review status
    peerReviewed
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/484
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.692
  • Is version of
    10.3389/fnins.2014.00385
  • Title
    Cognitive state monitoring and the design of adaptive instruction in digital environments: Lessons learned from cognitive workload assessment using a passive brain-computer interface approach.
  • DRO type
    article
  • Leibniz institute name(s) / abbreviation(s)
    IWM
  • Leibniz subject classification
    Psychologie
  • Journal title
    Frontiers in Neuroscience.
  • Volume
    8:385
  • Visible tag(s)
    Version of Record