Examining Students’ Behavior in a Digital Simulation Game for Nurse Training

Digital simulation games serve as a critical pedagogical tool for nursing education, specifically in fostering decision-making, task delegation, and patient care management. By analyzing 353 sessions within the CLONE simulation platform, researchers have established a clear correlation between specific behavioral patterns and successful learning outcomes. The primary objective of this analysis is to leverage learning analytics and data mining techniques to identify the strategies that distinguish high-performing students from those who struggle to meet educational objectives.

Successful performance in these simulations is consistently linked to a higher intensity of planning, thorough information inspection, and the frequent use of task delegation. Students who achieve positive outcomes demonstrate a methodical approach to patient care, whereas outlier sessions often reveal a lack of structured planning or a tendency to prioritize game mechanics over clinical requirements. These atypical sessions frequently indicate premature care delivery or inconsistent strategies, highlighting areas where students may be experiencing difficulty in translating theoretical knowledge into practical, simulated clinical action.

The methodology employs n-gram sequence analysis, agglomerative clustering, and k-nearest neighbor outlier detection to map student behavior across the simulation. These findings suggest that integrating dynamic feedback loops and structured learning analytics into serious games can significantly enhance educational effectiveness. By identifying and addressing these behavioral deviations in real-time, educators can better support students in developing the clinical competencies necessary for professional nursing practice. This data-driven approach provides a scalable framework for improving simulation-based training, ensuring that digital tools effectively bridge the gap between academic instruction and real-world medical demands.