Sequencing immersive virtual reality and physical laboratory work for titration training: effects on learning outcomes

Abstract

Background Higher education institutions face constraints on laboratory space, staffing, and safety, motivating interest in immersive virtual reality (iVR) as a complement to laboratory teaching. Objective This pilot study aimed to compare knowledge‐score changes across two sequences of iVR and physical laboratory titration training in a mandatory first-semester Life Sciences course and to explore relationships within the Cognitive Affective Model of Immersive Learning (CAMIL). Methods Ninety‐one students completed both activities in instructor‐assigned, counterbalanced sequences and took a 13‐item knowledge test at three time points (baseline and after each activity). Questionnaires assessed CAMIL constructs and usability. Knowledge scores were analyzed with a baseline‐adjusted linear mixed‐effects model with participant random intercepts; questionnaire data were analyzed in an exploratory reduced path model with bootstrap confidence intervals. Results Mean knowledge scores increased from 39.80 out of 52 points at baseline (SD = 4.02) to 42.87 after the first activity (SD = 4.47) and 43.18 after the second (SD = 4.35). The first‐period VR‐first minus laboratory-first difference was –1.26 points (95% CI [–2.74, 0.22], p = 0.094). The time‐by‐sequence interaction was also imprecisely estimated (b = 0.76, 95% CI [–0.42, 1.94], p = 0.204). Knowledge-test reliability was limited (Cronbachs α = 0.349, 0.579, and 0.587). In the path model, presence was negatively associated with cognitive load (β = –0.469, bootstrap 95% CI [–0.628, –0.344]); the remaining path intervals included zero. Usability averaged 71.75 (SD = 13.88; n = 90). Conclusions These findings describe knowledge-score increases during a combined training sequence but do not establish equivalent learning outcomes, a causal modality effect, or procedural skill transfer. Limited knowledge-test reliability further constrains interpretation.

The authors' abstract, as published at the source. Frontiers in Virtual Reality, 2026 · DOI ↗