Modelling Co-located Team Communication from Voice Detection and Positioning Data in Healthcare Simulation

The role of indoor positioning analytics in assessment of simulation‐based learning

“…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).…”

“…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).…”

“…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). While all three of these themes are equally important, most of the existing learning analytics research on simulation‐based learning focused on the first theme, analytics for assessment (Echeverria et al, 2018; Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021; Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021; Martinez‐Maldonado, Echeverria, Fernandez Nieto, & Buckingham, 2020; Zhao et al, 2022). The current study explored the role of learning analytics in assessment and feedback provision of simulation‐based learning from all three aspects; further details were elaborated in the research questions presented in the following subsection.…”

“…Milligan (2018) also stressed that the measurement and insight of learning in learning analytics should provide sufficient utility for teachers and are interpretable by them. Additionally, demonstrating results to relevant stakeholders, such as teachers, and incorporating their responses has also been a common approach in prior MMLA research on simulation‐based learning (Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021; Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021; Martinez‐Maldonado, Echeverria, Fernandez Nieto, & Buckingham, 2020; Zhao et al, 2022). Therefore, the validity and interpretability of the aforementioned analytics (RQ1–3) and their potential utility in post‐simulation debrief were evaluated by teachers through the final research question: RQ4 : How can teachers use indoor positioning analytics to support their formative assessment and immediate feedback during simulation‐based learning? …”

“…This study goes beyond prior works that used students' physical positioning traces to model their spatial behaviours. In particular, while previous work on indoor positioning analytics has investigated students' movement within a team (Echeverria et al, 2018; Fernandez‐Nieto, Martinez‐Maldonado, Kitto, & Shum, 2021), presence in spaces of interest (Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021), and interaction with other team members (Fernandez‐Nieto, Martinez‐Maldonado, Echeverria, et al, 2021; Zhao et al, 2022), the current study focused primarily on formative assessment and group/team‐level feedback. This work may be similar to our previous works that used indoor positioning analytics to model students' progression of math and reading abilities in open learning spaces (Yan et al, 2021; Yan, Martinez‐Maldonado, et al, 2022), but we focus specifically on the assessment of task and teamwork performance during simulation‐based learning in this study.…”