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Good Vibrations: Tuning a Systems Dynamics Model of Affect and Cognition in Learning to the Appropriate Frequency Bands of Fine-Grained Temporal Sequences of Data: Frequency Bands of Affect and Cognition

Published: 09 June 2021 Publication History

Abstract

Process-oriented studies of cooperative learning from an educational neuroscience perspective has not been firmly quantified experimentally. Within a modeling approach aimed at the development of a systems dynamics model of affect and cognition, the goal of this exploratory study is to identify typical timescales of variation for continuous metrics of affect (Frontal Alpha Asymmetry (FAA): valence) and cognition (Cognitive Load (CL); Index of Cognitive Engagement (ICE); Frontal Midline Theta (FMT): attention). These metrics were obtained from 72 participants paired in dyads (player and watcher) from whom electroencephalography (EEG) was recorded for 2 hours while one participant was playing a serious game to learn Physics, and the other one was watching passively. The results show rather slow cyclical variation for every metric tested, accompanied in certain cases by short bursts of faster variations. This result converges with [Newell 1990] cognitive architecture assuming that psychophysiological measures capture activity at higher levels such as operation tasks and operations. Theoretical, methodological and applied implications are discussed. Also, the need for further fine-grained analyses of the context and other atypical analyses are expressed.

References

[1]
John R. Anderson. 2002. Spanning seven orders of magnitude: A challenge for cognitive modeling. Cognitive Science 26 (January 2002), 85-112. https://doi.org/10.1207/s15516709cog2601_3
[2]
Roger Azevedo. 2015. Defining and Measuring Engagement and Learning in Science: Conceptual, Theoretical, Methodological, and Analytical Issues. Educational Psychologist 50, 1 (January 2015), 84–94. https://doi.org/10.1080/00461520.2015.1004069
[3]
François Boucher-Genesse, Martin Riopel, and Patrice Potvin. 2011. Research results for Mecanika: a game to learn Newtonian concepts. In Proceedings of the 7 th international conference on Games, Learning and Society. Madison, Wisconsin, 31-38.
[4]
Gian Emilio Chatrian, Ettore Lettich, and Paula L. Nelson. 1985. Ten percent electrode system for topographic studies of spontaneous and evoked EEG activity. American Journal of EEG Technology 25 (June 1985), 83-92. https://doi.org/10.1080/00029238.1985.11080163
[5]
Gian Emilio Chatrian, Ettore Lettich, and Paula L. Nelson. 1988. Modified nomenclature for the “10%” electrode system. Journal of Clinical Neurophysiology 5 (April 1988), 183-186. https://doi.org/10.1097/00004691-198804000-00005
[6]
Andy Clark. 2013. Expecting the World: Perception, Prediction, and the Origins of Human Knowledge. Journal of Philosophy 110,9 (September 2013), 469-496. https://doi.org/10.5840/jphil2013110913
[7]
David Hestenes, Malcolm Wells, and Gregg Swackhamer. 1992. Force Concept Inventory. The Physics Teacher 30 (February 1992), 141-158. https://doi.org/10.1119/1.2343497
[8]
Anu Holm, Kristian Lukander, Jussi Korpela, Mikael Sallinen, Kiti M. I. Müller. 2009. Estimating brain load from the EEG. The Scientific World Journal 9 (July 2009), 639–651. https://doi.org/10.1100/tsw.2009.83
[9]
Julien Mercier, Ivan Luciano Avaca, Kathleen Whissell-Turner, and Ariane Paradis. Submitted. Towards Modeling the Psychophysiology of Learning Interactions: The Effect of Agency on Arousal in Dyads Learning Physics with a Serious Computer Game. Proceedings of the 2020 International Conference on Technology and Innovation in Learning, Teaching and Education.
[10]
Julien Mercier, Ivan Luciano Avaca, Kathleen Whissell-Turner, Ariane Paradis, and Tassos A. Mikropoulos. 2020. Agency affects learning outcomes with a serious game. In Panayiotis Zaphiris and Andri Ioannou (Eds.) Proceedings of the 2020 22nd International Conference on Human-Computer Interaction. Springer Nature, Switzerland, 267-278. https://doi.org/10.1007/978-3-030-50506-6_20
[11]
Damon J. Mitchell, Neil McNaughton, Danny Flanagan, and & Ian J. Kirk. 2008. Frontal-midline theta from the perspective of hippocampal “theta”. Progress in Neurobiology 86, 3 (November 2008), 156–185. https://doi.org/10.1016/j.pneurobio.2008.09.005
[12]
Allen Newell. (1990). Unified Theories of Cognition. University Press. Harvard, USA.
[13]
Alan T. Pope, Edward H. Bogart, Debbie S. Bartolome. 1996. Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology 40 (May 1996), 187–195. https://doi.org/10.1016/0301-0511(95)05116-3
[14]
Ezra E. Smith, Samantha J. Reznik, Jennifer L. Stewart, and John J. B. Allen. 2017. Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. International Journal of Psychophysiology 111 (January 2017), 98–114. https://doi.org/10.1016/j.ijpsycho.2016.11.005
[15]
Kurt VanLehn. 2011. The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist 46, 4 (September 2011), 197–221. https://doi.org/10.1080/00461520.2011.611369
[16]
Wim Westera. 2018. Simulating serious games: a discrete-time computational model based on cognitive flow theory. Interactive Learning Environments 26, 4 (June 2018), 539-552. https://doi.org/10.1080/10494820.2017.1371196

Cited By

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  • (2024)A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load AssessmentIEEE Access10.1109/ACCESS.2024.336032812(23466-23489)Online publication date: 2024
  • (2023)Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic ReviewSensors10.3390/s2313596823:13(5968)Online publication date: 27-Jun-2023

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cover image ACM Other conferences
DSAI '20: Proceedings of the 9th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
December 2020
245 pages
ISBN:9781450389372
DOI:10.1145/3439231
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 June 2021

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  1. educational neuroscience
  2. game-based learning
  3. online measures of learning

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Cited By

View all
  • (2024)A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load AssessmentIEEE Access10.1109/ACCESS.2024.336032812(23466-23489)Online publication date: 2024
  • (2023)Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic ReviewSensors10.3390/s2313596823:13(5968)Online publication date: 27-Jun-2023

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