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Behavioral Coding for Predicting Perceptions of Interactions in Dyads

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Learning and Collaboration Technologies (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14040))

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Abstract

Verbal and non-verbal interpersonal interactions, if quantified, can be important factors for analyzing the performance of globalized team members. While using a survey is a common way to measure the interpersonal interactions, behavioral observation is a method that can effectively save time and avoid individual bias in interpretation. This between-subjects experiment collected data from 101 participants in a face-to-face (F2F), video conference (VC), and virtual reality (VR) training communication between the participant and a confederate trainer. The study explored whether the three conditions affected facial and interpersonal behavior differently. Secondly, the study examined whether future researchers could use the behavior markers to predict social and task variables. The dependent behavioral marker variables were Affirmation, Head Nod, Socializing, Question, Leaning Forward, Common Point of Reference, Mirroring, and Hand Gesture. Results showed that Affirmation and Question occurred significantly more often in F2F and VR than in VC. Participants also scored significantly higher in Head Nod in F2F than in VR and VC conditions, and VR socializing scores were also significantly higher than VC condition. Finally, correlation analysis showed that some self-report survey responses could possibly be replaced with behavioral observation methods. Future researchers who can automate the behavioral coding could thus use this approach rather than survey questions for those constructs.

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Acknowledgments

This research has been partially supported by an award from the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy associated with Award Number DE-AR0001097.

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Authors and Affiliations

  1. Iowa State University, Ames, IA, 50010, USA

    Mohammadamin Sanaei, Marielle Machacek, Stephen B. Gilbert & James Oliver

  2. Raytheon Technology, Arlington, USA

    Coleman Eubanks & Peggy Wu

Authors
  1. Mohammadamin Sanaei
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  2. Marielle Machacek
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  3. Stephen B. Gilbert
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  4. Coleman Eubanks
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  5. Peggy Wu
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  6. James Oliver
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Corresponding author

Correspondence to Mohammadamin Sanaei .

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Editors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    Panayiotis Zaphiris

  2. Cyprus University of Technology, Limassol, Cyprus

    Andri Ioannou

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Sanaei, M., Machacek, M., Gilbert, S.B., Eubanks, C., Wu, P., Oliver, J. (2023). Behavioral Coding for Predicting Perceptions of Interactions in Dyads. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. HCII 2023. Lecture Notes in Computer Science, vol 14040. Springer, Cham. https://doi.org/10.1007/978-3-031-34411-4_7

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  • DOI: https://doi.org/10.1007/978-3-031-34411-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34410-7

  • Online ISBN: 978-3-031-34411-4

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