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Main 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
Author(s): Gerjets, Peter
Walter, Carina
Rosenstiel, Wolfgang
Bogdan, Martin
Zander, Thorsten O.
Type: Article
Language Code: en
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.
Issue Date: 9-Dec-2014
Date Available: 7-Nov-2019
DDC Class: 610 Medizin und Gesundheit
Subject(s): passive brain-computer interface
cross-task classification
working-memory load
adaptive learning environments
cognitive load theory
Journal Title: Frontiers in Neuroscience
Publisher: Frontiers Media S.A.
Publisher Place: Lausanne
Volume: 8
Article Number: 385
Publisher DOI: 10.3389/fnins.2014.00385
EISSN: 1662-453X
Appears in Collections:FG Biopsychologie und Neuroergonomie » Publications

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