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

Simulation-based learning provides students with unique opportunities to develop key procedural and teamwork skills in close-to-authentic physical learning and training environments. Yet, assessing students' performance in such situations can be challenging and mentally exhausting for teachers. Multimodal learning analytics can support the assessment of simulation-based learning by making salient aspects of students' activities visible for evaluation. Although descriptive analytics have been used to study students' motor behaviours in simulation-based learning, their validity and utility for assessing performance remain unclear. This study aims at addressing this knowledge gap by investigating how indoor positioning analytics can be used to generate meaningful insights about students' tasks and collaboration performance in simulation-based learning. We collected and analysed the positioning data of 304 healthcare students, organised in 76 teams, through correlation, predictive and epistemic network analyses. The primary findings were (1) large correlations between students' spatial-procedural behaviours and their group performances; (2) predictive learning analytics that achieved an acceptable level (0.74 AUC) in distinguishing between low-performing and high-performing teams regarding collaboration performance; and (3) epistemic networks that can be used for assessing the behavioural differences across multiple teams. We also present the teachers' qualitative evaluation of the utility of these analytics and implications for supporting formative assessment in simulation-based learning. Practitioner notes What is currently known about this topic Assessing students' performance in simulation-based learning is often challenging and mentally exhausting. The combination of learning analytics and sensing technologies has the potential to uncover meaningful behavioural insights in physical learning spaces. Observational studies have suggested the potential value of analytics extracted from positioning data as indicators of highly-effective behaviour in simulation-based learning. What this paper adds Indoor positioning analytics for supporting teachers' formative assessment and timely feedback on students' group/team-level performance in simulation-based learning. Empirical evidence supported the potential use of epistemic networks for assessing the behavioural differences between low-performing and high-performing teams. Teachers' positively validated the utility of indoor positioning analytics in supporting reflective practices and formative assessment in simulation-based learning. Implications for practitioners Indoor positioning tracking and spatial analysis can be used to investigate students' teamwork and task performance in simulation-based learning. Predictive learning analytics should be developed based on features that have direct relevance to teachers' learning design. Epistemic networks analysis and comparison plots can be useful in identifying and assessing behavioural differences across multiple teams.