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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

Overview of attention for article published in Frontiers in Neuroscience, December 2014
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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
Published in
Frontiers in Neuroscience, December 2014
DOI 10.3389/fnins.2014.00385
Pubmed ID
Authors

Peter Gerjets, Carina Walter, Wolfgang Rosenstiel, Martin Bogdan, Thorsten O. Zander

Abstract

According to Cognitive Load Theory (CLT), 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' 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 electroencephalography (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.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 202 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 <1%
Germany 1 <1%
Pakistan 1 <1%
Israel 1 <1%
Denmark 1 <1%
Unknown 197 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 21%
Student > Master 30 15%
Researcher 25 12%
Student > Doctoral Student 10 5%
Student > Bachelor 10 5%
Other 42 21%
Unknown 42 21%
Readers by discipline Count As %
Psychology 35 17%
Engineering 28 14%
Computer Science 26 13%
Neuroscience 20 10%
Medicine and Dentistry 12 6%
Other 34 17%
Unknown 47 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 November 2014.
All research outputs
#20,653,708
of 25,368,786 outputs
Outputs from Frontiers in Neuroscience
#9,456
of 11,537 outputs
Outputs of similar age
#273,553
of 368,328 outputs
Outputs of similar age from Frontiers in Neuroscience
#114
of 126 outputs
Altmetric has tracked 25,368,786 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,537 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.