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EEG-Related Changes in Cognitive Workload, Engagement and Distraction as Students Acquire Problem Solving Skills

  • Ronald H. Stevens
  • Trysha Galloway
  • Chris Berka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

We have begun to model changes in electroencephalography (EEG)-derived measures of cognitive workload, engagement and distraction as individuals developed and refined their problem solving skills in science. For the same problem solving scenario(s) there were significant differences in the levels and dynamics of these three metrics. As expected, workload increased when students were presented with problem sets of greater difficulty. Less expected, however, was the finding that as skills increased, the levels of workload did not decrease accordingly. When these indices were measured across the navigation, decision, and display events within the simulations significant differences in workload and engagement were often observed. Similarly, event-related differences in these categories across a series of the tasks were also often observed, but were highly variable across individuals.

Keywords

Work Memory Capacity Skill Acquisition Discriminant Function Analysis Main Menu Cognitive Workload 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ronald H. Stevens
    • 1
  • Trysha Galloway
    • 1
  • Chris Berka
    • 2
  1. 1.UCLA IMMEX Project, 5601 W. Slauson Ave. #255, Culver City, CA 90230 
  2. 2.Advanced Brain Monitoring, Inc, Carlsbad, CA 90045 

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