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Using Physiological Responses To Capture Unique Idea Creation In Team Collaborations

Authors:

Kira Furuichi,

Marcelo WorsleyAuthors Info & Claims

CSCW '18 Companion: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing

Pages 369 - 372

https://doi.org/10.1145/3272973.3274099

Published: 30 October 2018 Publication History

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Abstract

The rise of multimodal learning analytics (MMLA) gives opportunity to learn about teamwork and collaboration through detailed physiological responses, with the aid of multimodal tools. The primary goal of this study is to determine if unique idea creation, or secondary agreement to unique ideas, in group collaboration can be distinguished through one's physiological responses. In this pilot study participants who presented new ideas demonstrated higher levels of galvanic skin response, indicative of engagement, emotional arousal or cognitive load.

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References

[1]

Ahonen, L. 2018. Biosignals reflect pair-dynamics in collaborative work: EDA and ECG study of pair-programming in a classroom environment. Scientific Reports, 3138, 8.

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[2]

Benedek, M. & Kaernbach, C. 2010. A continuous measure of phasic electodermal activity. Journal of Neuroscience Methods, 190, 80--91.

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[3]

Hernandez, J. 2014. ACM. Using electrodermal activity to recognize ease of engagement in children during social interactions. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 307--17. Retrieved June 11, 2018 from: https://dl.acm.org/citation.cfm?id=2636065.

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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
Olsen JSharma KRummel NAleven V(2020)Temporal analysis of multimodal data to predict collaborative learning outcomesBritish Journal of Educational Technology10.1111/bjet.1298251:5(1527-1547)Online publication date: 20-Jul-2020
https://doi.org/10.1111/bjet.12982
Dokic KLauc T(2020)Opportunity for Video-on-Demand Services – Collecting Consumer’s Neurophysiology Data for Recommendation Systems ImprovementDigital Economy. Emerging Technologies and Business Innovation10.1007/978-3-030-64642-4_8(91-104)Online publication date: 3-Dec-2020
https://doi.org/10.1007/978-3-030-64642-4_8
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Index Terms

Using Physiological Responses To Capture Unique Idea Creation In Team Collaborations
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI design and evaluation methods
      1. Field studies

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Published In

CSCW '18 Companion: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing

October 2018

518 pages

ISBN:9781450360180

DOI:10.1145/3272973

General Chairs:
Vanessa Evers
University of Twente
,
Mor Naaman
Cornell Tech
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Karrie Karahalios
University of Illinois
,
Airi Lampinen
Stockholm University
,
Andrés Monroy-Hernández
Snap, Inc.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 30 October 2018

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CSCW '18

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SIGCHI

CSCW '18: Computer Supported Cooperative Work and Social Computing

November 3 - 7, 2018

NJ, Jersey City, USA

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CSCW '18 Companion Paper Acceptance Rate 105 of 385 submissions, 27%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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CSCW '25

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sigchi

Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

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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
Olsen JSharma KRummel NAleven V(2020)Temporal analysis of multimodal data to predict collaborative learning outcomesBritish Journal of Educational Technology10.1111/bjet.1298251:5(1527-1547)Online publication date: 20-Jul-2020
https://doi.org/10.1111/bjet.12982
Dokic KLauc T(2020)Opportunity for Video-on-Demand Services – Collecting Consumer’s Neurophysiology Data for Recommendation Systems ImprovementDigital Economy. Emerging Technologies and Business Innovation10.1007/978-3-030-64642-4_8(91-104)Online publication date: 3-Dec-2020
https://doi.org/10.1007/978-3-030-64642-4_8
Sharma KPapamitsiou ZGiannakos M(2019)Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approachBritish Journal of Educational Technology10.1111/bjet.1285450:6(3004-3031)Online publication date: 21-Jul-2019
https://doi.org/10.1111/bjet.12854

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Idea Creation of Creative Computing Based on Embedding Features and Ontology

Tool and Evaluation Method for Idea Creation Support

A team collaboration space supporting capture and access of virtual meetings

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