Abstract
In embodied team learning activities, students are expected to learn to collaborate with others while freely moving in a physical learning space to complete a shared goal. Students can thus interact in various team configurations, resulting in increased complexity in their communication dynamics since unrelated dialogue segments can concurrently happen at different locations of the learning space. This can make it difficult to analyse students’ team dialogue solely using audio data. To address this problem, we present a study in a highly dynamic healthcare simulation setting to illustrate how spatial data can be combined with audio data to model embodied team communication. We used ordered network analysis (ONA) to model the co-occurrence and the order of coded co-located dialogue instances and identify key differences in the communication dynamics of high and low performing teams.
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References
Abitino, A., Pugh, S.L., Peacock, C.E., D’Mello, S.K.: Eye to eye: gaze patterns predict remote collaborative problem solving behaviors in triads. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) AIED 2022. LNCS, vol. 13355, pp. 378–389. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11644-5_31
Adrot, A., Bia Figueiredo, M.: “Lost in digitization’’: a spatial journey in emergency response and pragmatic legitimacy. In: de Vaujany, F.-X., Adrot, A., Boxenbaum, E., Leca, B. (eds.) Materiality in Institutions. TWG, pp. 151–181. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-97472-9_6
Bowman, D., et al.: The mathematical foundations of epistemic network analysis. In: Ruis, A.R., Lee, S.B. (eds.) ICQE 2021. CCIS, vol. 1312, pp. 91–105. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67788-6_7
Härgestam, M., Lindkvist, M., Brulin, C., Jacobsson, M., Hultin, M.: Communication in interdisciplinary teams: exploring closed-loop communication during in situ trauma team training. BMJ Open 3(10) (2013)
Ioannou, M., Georgiou, Y., Ioannou, A., Johnson, M.: On the understanding of students’ learning and perceptions of technology integration in low-and high-embodied group learning. 1, 304–311 (2019)
Kelly, S., Olney, A.M., Donnelly, P., Nystrand, M., D’Mello, S.K.: Automatically measuring question authenticity in real-world classrooms. Educ. Res. 47(7), 451–464 (2018)
Liaw, S.Y., Rethans, J.J., Scherpbier, A., Piyanee, K.Y.: Rescuing a patient in deteriorating situations (rapids): a simulation-based educational program on recognizing, responding and reporting of physiological signs of deterioration. Resuscitation 82(9), 1224–1230 (2011)
Lin, J., et al.: Is it a good move? Mining effective tutoring strategies from human-human tutorial dialogues. Futur. Gener. Comput. Syst. 127, 194–207 (2022)
Ludlow, K., Churruca, K., Ellis, L.A., Mumford, V., Braithwaite, J.: Decisions and dilemmas: the context of prioritization dilemmas and influences on staff members’ prioritization decisions in residential aged care. Qual. Health Res. 31(7), 1306–1318 (2021)
Marquart, C., Tan, Y., Cai, Z., Shaffer, D.W.: Ordered network analysis (2022). https://epistemic-analytics.gitlab.io/qe-packages/ona/cran/
Martinez-Maldonado, R., Echeverria, V., Schulte, J., Shibani, A., Mangaroska, K., Buckingham Shum, S.: Moodoo: indoor positioning analytics for characterising classroom teaching. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 360–373. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_29
McHugh, M.L.: Interrater reliability: the kappa statistic. Biochemia medica 22(3), 276–282 (2012)
Miller, K., Riley, W., Davis, S.: Identifying key nursing and team behaviours to achieve high reliability. J. Nurs. Manag. 17(2), 247–255 (2009)
Mitri, D.D., et al. (eds.): Proceedings of the First International Workshop on Multimodal Artificial Intelligence in Education. CEUR Workshop Proceedings (2021)
O’Daniel, M., Rosenstein, A.H.: Professional communication and team collaboration. In: Patient Safety and Quality: An Evidence-Based Handbook for Nurses (2008)
Praharaj, S., Scheffel, M., Schmitz, M., Specht, M., Drachsler, H.: Towards collaborative convergence: quantifying collaboration quality with automated co-located collaboration analytics. In: 12th International Learning Analytics and Knowledge Conference, pp. 358–369 (2022)
Pugh, S.L., Subburaj, S.K., Rao, A.R., Stewart, A.E., Andrews-Todd, J., D’Mello, S.K.: Say what? Automatic modeling of collaborative problem solving skills from student speech in the wild. In: 14th International Conference on Educational Data Mining, pp. 55–67 (2021)
Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. OpenAI Blog (2022)
Riley, W., Hansen, H., Gürses, A.P., Davis, S., Miller, K., Priester, R.: The nature, characteristics and patterns of perinatal critical events teams. In: Advances in Patient Safety: New Directions and Alternative Approaches (2008)
Salas, E., Sims, D.E., Burke, C.S.: Is there a “big five” in teamwork? Small Group Res. 36(5), 555–599 (2005)
Salas, E., Stevens, R., Gorman, J., Cooke, N.J., Guastello, S., von Davier, A.A.: What will quantitative measures of teamwork look like in 10 years? In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 59, no. 1, pp. 235–239 (2015)
Schlotterbeck, D., Uribe, P., Jiménez, A., Araya, R., van der Molen Moris, J., Caballero, D.: TARTA: teacher activity recognizer from transcriptions and audio. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds.) AIED 2021. LNCS (LNAI), vol. 12748, pp. 369–380. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78292-4_30
Setti, F., Lanz, O., Ferrario, R., Murino, V., Cristani, M.: Multi-scale F-formation discovery for group detection. In: 2013 IEEE International Conference on Image Processing, pp. 3547–3551. IEEE (2013)
Shaffer, D.W., Collier, W., Ruis, A.R.: A tutorial on epistemic network analysis: analyzing the structure of connections in cognitive, social, and interaction data. J. Learn. Anal. 3(3), 9–45 (2016). https://doi.org/10.18608/jla.2016.33.3
Shaffer, D.W., et al.: Epistemic network analysis: a prototype for 21st-century assessment of learning. Int. J. Learn. Media 1(2) (2009)
Siebert-Evenstone, A.L., Irgens, G.A., Collier, W., Swiecki, Z., Ruis, A.R., Shaffer, D.W.: In search of conversational grain size: modeling semantic structure using moving stanza windows. J. Learn. Anal. 4(3), 123–139 (2017)
Sorokowska, A., et al.: Preferred interpersonal distances: a global comparison. J. Cross Cult. Psychol. 48(4), 577–592 (2017)
Southwell, R., et al.: Challenges and feasibility of automatic speech recognition for modeling student collaborative discourse in classrooms. In: 15th International Conference on Educational Data Mining, pp. 302–315 (2022)
Takizawa, P.A., Honan, L., Brissette, D., Wu, B.J., Wilkins, K.M.: Teamwork in the time of Covid-19. FASEB BioAdvances 3(3), 175–181 (2021)
Tan, Y., Ruis, A.R., Marquart, C., Cai, Z., Knowles, M.A., Shaffer, D.W.: Ordered network analysis. In: Damşa, C., Barany, A. (eds.) ICQE 2022. CCIS, vol. 1785, pp. 101–116. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-31726-2_8
Wang, Y., Swiecki, Z., Ruis, A.R., Shaffer, D.W.: Simplification of epistemic networks using parsimonious removal with interpretive alignment. In: Ruis, A.R., Lee, S.B. (eds.) ICQE 2021. CCIS, vol. 1312, pp. 137–151. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67788-6_10
Westli, H.K., Johnsen, B.H., Eid, J., Rasten, I., Brattebø, G.: Teamwork skills, shared mental models, and performance in simulated trauma teams: an independent group design. Scand. J. Trauma Resuscitation Emerg. Med. 18(1), 1–8 (2010)
Yan, L., Zhao, L., Gasevic, D., Martinez-Maldonado, R.: Scalability, sustainability, and ethicality of multimodal learning analytics. In: 12th International Learning Analytics and Knowledge Conference, New York, NY, USA, pp. 13–23 (2022)
Zhao, L., et al.: METS: multimodal learning analytics of embodied teamwork learning. In: 13th International Learning Analytics and Knowledge Conference (2023, in press)
Zhao, L., et al.: Modelling co-located team communication from voice detection and positioning data in healthcare simulation. In: 12th International Learning Analytics and Knowledge Conference, New York, NY, USA, pp. 370–380 (2022)
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Zhao, L. et al. (2023). Analysing Verbal Communication in Embodied Team Learning Using Multimodal Data and Ordered Network Analysis. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_20
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