Innovation in Team Interaction: New Methods for Assessing Collaboration Between Brains and Bodies Using a Multi-level Framework

Fiore, Stephen M.; Kapalo, Katelynn A.

doi:10.1007/978-3-319-33261-1_4

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…The temporal range over which NOs occur is between ~5 s to 3+ min, which is greater than the ~1.5 s required for reciprocal implicit social coordination events (Menoret et al, 2014) and less time over which performance evidence is usually accumulated by raters (Baker, Amodeo, Krokos, Slonim, & Herrea, 2010). It is a representational time frame that spans micro and macro levels of teamwork (Fiore & Kapalo, 2017;Salas et al, 2015;.…”

Section: Introductionmentioning

confidence: 99%

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information

Stevens

Galloway²,

Willemsen‐Dunlap³

2018

Hum Factors

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Section: Introductionmentioning

confidence: 99%

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information

Stevens

Galloway²,

Willemsen‐Dunlap³

2018

Hum Factors

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“…The interrelations among dynamic processes are critical for successful collaboration (Gašević, Joksimović, Eagan, & Shaffer, 2019). So far, researchers have studied collaboration processes and models, alongside factors influencing the processes and outcomes (e.g., Fiore & Kapalo, 2017;Gašević et al, 2019;Hesse, Care, Buder, Sassenberg, & Griffin, 2015;OECD, 2017;Scoular & Care, 2020).…”

Section: Relationship Between Students and Contentmentioning

confidence: 99%

Collaboration Analytics Need More Comprehensive Models and Methods: An Opinion Paper

Han

Krieger

Greiff

2021

JLA

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As technology advances, learning analytics is expanding to include students’ collaboration settings. Despite their increasing application in practice, some types of analytics might not fully capture the comprehensive educational contexts in which students’ collaboration takes place (e.g., when data is collected and processed without predefined models, which forces users to make conclusions without sufficient contextual information). Furthermore, existing definitions and perspectives on collaboration analytics are incongruent. In light of these circumstances, this opinion paper takes a collaborative classroom setting as context and explores relevant comprehensive models for collaboration analytics. Specifically, this paper is based on Pei-Ling Tan and Koh’s ecological lens (2017, Situating learning analytics pedagogically: Towards an ecological lens. Learning: Research and Practice, 3(1), 1–11. https://doi.org/10.1080/23735082.2017.1305661), which illustrates the co-emergence of three interactions among students, teachers, and content interwoven with time. Moreover, this paper suggests several factors to consider in each interaction when executing collaboration analytics. Agendas and recommendations for future research are also presented.

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“…The study of teams now transcends disciplines with important research coming not only out of the social and organizational sciences, but also neuroscience and computer science (Fiore & Kapalo, 2017). Generally, the focus is on a blend of attitudinal, cognitive, and behavioral processes emerging during complex and dynamic interaction between team members and their technology.…”

Section: Introductionmentioning

confidence: 99%

Transdisciplinary Team Research to Develop Theory of Mind in Human-AI Teams Panelists

Fiore

Bracken

Demir

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

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This panel will provide a transdisciplinary perspective on developing artificial social intelligence for teams. A panel with representatives from the cognitive, computational, and neural sciences will discuss theoretical, methodological, and technological insights derived from their respective disciplines. These perspectives will be integrated via a set of questions meant to guide synthesis across disciplines in support of a transdisciplinary team research approach. Through discussion across the panel and audience, our goal is to contribute to research and development in the area of Human-AI-Robot Teaming effectiveness.

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Innovation in Team Interaction: New Methods for Assessing Collaboration Between Brains and Bodies Using a Multi-level Framework

Cited by 6 publications

References 27 publications

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information

Collaboration Analytics Need More Comprehensive Models and Methods: An Opinion Paper

Transdisciplinary Team Research to Develop Theory of Mind in Human-AI Teams Panelists

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