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.