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Multichannel data for understanding cognitive affordances during complex problem solving

Authors:

Charlotte Larmuseau,

Pieter Vanneste,

Fien DepaepeAuthors Info & Claims

LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge

Pages 61 - 70

https://doi.org/10.1145/3303772.3303778

Published: 04 March 2019 Publication History

Abstract

This exploratory study challenges the current practices in cognitive load measurement by using multichannel data to investigate cognitive load affordances during online complex problem solving. Moreover, it is an attempt to investigate how cognitive load is related to strategy use. Accordingly, in the current study a well- and an ill-structured problem were developed in a virtual learning environment. Online support was provided. Participants were 15 students from the teacher training program. This study incorporated subjective measurements of students' cognitive load (i.e., intrinsic, extraneous, germane load and their mental effort) combined with physiological data containing galvanic skin response (GSR) and skin temperature (ST). A first aim was to investigate whether there was a significant difference for the subjective measurements, physiological data and consultation of support between the well-and ill-structured problem. Secondly this study investigated how individual differences of subjective measurements are related to individual differences of physiological data and consultation of support. Results reveal significant differences for intrinsic load, mental effort between a well- and ill-structured problem. Moreover, when investigating individual differences, findings reveal that GSR might be related to mental effort. Additionally, results indicate that cognitive load influences strategy use. Future research with larger sample sizes should verify these findings in order to have more insight into how we can measure cognitive load and how its related to self-directed learning. These insights should allow us to provide adaptive support in virtual learning environments.

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Zammouri AMoussa AChevallier S(2024)Use of cognitive load measurements to design a new architecture of intelligent learning systemsExpert Systems with Applications10.1016/j.eswa.2023.121253237(121253)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121253
Patzak AVytasek J(2023)Operationalizing Learning Processes Through Learning AnalyticsApplied Data Science10.1007/978-3-031-29937-7_6(69-81)Online publication date: 10-May-2023
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Index Terms

Multichannel data for understanding cognitive affordances during complex problem solving
1. Applied computing
  1. Education
    1. Computer-assisted instruction
    2. E-learning

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cover image ACM Other conferences

LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge

March 2019

565 pages

ISBN:9781450362566

DOI:10.1145/3303772

General Chairs:
Sharon Hsiao
Arizona State University, USA
,
Jim Cunningham
Arizona State University, USA
,
Katie McCarthy
Georgia State University, USA
,
Grace Lynch
Society for Learning Analytics Research, Australia
,
Program Chairs:
Christopher Brooks
University of Michigan, USA
,
Rebecca Ferguson
The Open University, UK
,
Ulrich Hoppe
University of Duisburg-Essen, Germany

Copyright © 2019 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SoLAR: The Society for Learning Analytics Research
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 04 March 2019

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LAK19: The 9th International Learning Analytics & Knowledge Conference

March 4 - 8, 2019

AZ, Tempe, USA

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Cited By

Casella MBorghesi FCipresso PMarocco D(2024)Analyzing the Impact of Questionnaire Length on Respondent Burden Through Biosensors2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)10.1109/MetroXRAINE62247.2024.10796344(1045-1049)Online publication date: 21-Oct-2024
https://doi.org/10.1109/MetroXRAINE62247.2024.10796344
Zammouri AMoussa AChevallier S(2024)Use of cognitive load measurements to design a new architecture of intelligent learning systemsExpert Systems with Applications10.1016/j.eswa.2023.121253237(121253)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121253
Patzak AVytasek J(2023)Operationalizing Learning Processes Through Learning AnalyticsApplied Data Science10.1007/978-3-031-29937-7_6(69-81)Online publication date: 10-May-2023
https://doi.org/10.1007/978-3-031-29937-7_6
Wise ASarmiento JBoothe Jr. MScheffel MDowell NJoksimovic SSiemens G(2021)Subversive Learning AnalyticsLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448210(639-645)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448210
Mu SCui MHuang X(2020)Multimodal Data Fusion in Learning Analytics: A Systematic ReviewSensors10.3390/s2023685620:23(6856)Online publication date: 30-Nov-2020
https://doi.org/10.3390/s20236856
Srivastava NNawaz SLodge JVelloso EErfani SBailey JRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)Exploring the usage of thermal imaging for understanding video lecture designs and students' experiencesProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375514(250-259)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375514
Vrzakova HAmon MStewart ADuran ND'Mello SRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)Focused or stuck togetherProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375467(295-304)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375467

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