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This study applied Transmodal Analysis (TMA), a newly developed quantitative ethnographic approach, to examine whether and how virtual patient simulations can aid in educating undergraduate nursing students with competencies that exemplify practice-ready nurses. Multimodal transcripts capturing patient interactions, exam actions, and documentation were obtained from two students who used Elsevier’s Shadow Health® Digital Clinical Experiences (DCE) in Fall 2022 and Spring 2023. Patient scenarios were situated in three content areas (Gerontology, Mental Health, and Community Health) and two assignment types (focused exam and contact tracing). In each scenario, similar patterns of engagement were observed for both students as they completed learning activities such as collecting patient data and establishing a caring relationship. These activities—guided by the instructional design of DCE—indicated how students practiced recognizing and analyzing cues, subjective assessment, diagnosing and prioritizing hypotheses, generating solutions, evaluating outcomes, therapeutic communication, and care coordination and management in relation to each patient’s needs and conditions. A statistical difference was observed between competencies practiced while completing focused exam and contact tracing assignments. This study provides evidence for using simulations to facilitate competency-based education in nursing. Additionally, it provides motivation for using Transmodal Analysis combined with Ordered Network Analysis (T/ONA) to advance quantitative ethnography research in health care and health professions education. 

Wearable positioning sensors are enabling unprecedented opportunities to model students’ procedural and social behaviours during collaborative learning tasks in physical learning spaces. Emerging work in this area has mainly focused on modelling group-level interactions from low-level x-y positioning data. Yet, little work has utilised such data to automatically identify individual-level differences among students working in co-located groups in terms of procedural and social aspects such as task prioritisation and collaboration dynamics, respectively. To address this gap, this study characterised key differences among 124 students’ procedural and social behaviours according to their perceived stress, collaboration, and task satisfaction during a complex group task using wearable positioning sensors and ordered networked analysis. The results revealed that students who demonstrated more collaborative behaviours were associated with lower stress and higher collaboration satisfaction. Interestingly, students who worked individually on the primary and secondary learning tasks reported lower and higher task satisfaction, respectively. These findings can deepen our understanding of students’ individual-level behaviours and experiences while learning in groups.