LAK22: 12th International Learning Analytics and Knowledge Conference 2022
DOI: 10.1145/3506860.3506935
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Modelling Co-located Team Communication from Voice Detection and Positioning Data in Healthcare Simulation

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Cited by 13 publications
(16 citation statements)
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“…Multimodal learning analytics (MMLA) can potentially support the assessment and feedback provision process in simulation‐based learning by making salient aspects of students' learning behaviours visible for objective evaluation and for provoking reflection (Crescenzi‐Lanna, 2020; Sharma & Giannakos, 2020; Yan, Zhao, et al, 2022). Recent small‐scale studies in MMLA have used wearable positioning sensors to automatically capture students' positioning traces in simulation‐based learning (Echeverria et al, 2018; Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021; Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021; Zhao et al, 2022). These fine‐grained x‐y positioning traces have been modelled to extract insights about students' spatial and collaborative behaviours, including students' movement within a team (Echeverria et al, 2018; Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021); an individual's presence in spaces of interest (Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021); and students' interaction with other team members (Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021; Zhao et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
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“…Multimodal learning analytics (MMLA) can potentially support the assessment and feedback provision process in simulation‐based learning by making salient aspects of students' learning behaviours visible for objective evaluation and for provoking reflection (Crescenzi‐Lanna, 2020; Sharma & Giannakos, 2020; Yan, Zhao, et al, 2022). Recent small‐scale studies in MMLA have used wearable positioning sensors to automatically capture students' positioning traces in simulation‐based learning (Echeverria et al, 2018; Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021; Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021; Zhao et al, 2022). These fine‐grained x‐y positioning traces have been modelled to extract insights about students' spatial and collaborative behaviours, including students' movement within a team (Echeverria et al, 2018; Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021); an individual's presence in spaces of interest (Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021); and students' interaction with other team members (Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021; Zhao et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…In the context of simulation‐based learning, analytics for assessment would be related to research on learning analytics approaches that can support evaluating students' procedural and collaboration skills and generating evidence for constructive feedback (eg, Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021; Martinez‐Maldonado, Echeverria, Fernandez Nieto, & Buckingham, 2020). Analytics of assessment would involve research that aims to understand the reliability of teachers' assessment and scoring criteria across multiple simulations (eg, Zhao et al, 2022). Finally, studies investigating the validity of measurement would consider the external assessment validity of theory‐based trace data and behavioural features in capturing students' external performance measures (eg, Milligan & Griffin, 2016).…”
Section: Introductionmentioning
confidence: 99%
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