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Using learning analytics to identify engagement trajectories associated with clinical performance gains in virtual simulation

Year: 2026

Type: article

Abstract: Background Although virtual simulation is widely used in nursing education, how students engage with repeated practice remains poorly understood. Traditional evaluations based on pre- and post-test scores may overlook important behavioural learning patterns. This study aims to identify distinct engagement trajectories in virtual simulation and examine their association with performance improvement. Methods A retrospective observational study analysed log data from 330 nursing students completing a severe limb trauma simulation. Trajectory analysis included 148 students with two or more attempts. Behavioural indicators included attempts, error reduction, completion time change and total learning time. K -means clustering identified engagement trajectories and analysis of variance (ANOVA) compared clinical performance gains across groups. Results Three engagement trajectories emerged: High Engagement (n = 39), Moderate Engagement (n = 49) and Low Engagement (n = 60). Clinical performance gains differed significantly across trajectories (F = 414.83, p < .001), with the High-Engagement group showing the greatest improvement. Students who repeated practice initially scored lower but ultimately achieved higher simulation performance scores than single-attempt peers. Conclusions Engagement trajectories within virtual simulation are strongly associated with learning outcomes. Learning analytics may help identify students needing targeted instructional support.

Source: Clinical Simulation in Nursing

DOI: 10.1016/j.ecns.2026.102081