Performance and learning differences inside and outside Immersive virtual learning (IVR): Investigating the CAMIL model for chemistry lab skill development in high school students

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Authors: Megan D. Wiedbusch, Daryn A. Dever, Saerok Park, Sarah M. Romero, Romina Jannotti, David Garcia-Gali, Roger Azevedo
Cited by: 1
References: 32

Abstract

Background Immersive virtual reality (IVR) research must (1) consider learning as both an outcome but also a dynamic process and (2) ground IVR design and learning assessment within an empirically tested theoretical model, i.e., the Cognitive Affective Model of Immersive Learning (CAMIL). This model posits that IVR learning may be hindered or supported by several cognitive and affective variables (e.g., agency, cognitive load, situational interest). Aims We examine the learning processes and outcomes (i.e., declarative knowledge, procedural knowledge, and procedural skills) of Chemistry lab skills through a commercially available IVR game, HoloLab Champions, situated in the CAMIL model. Sample(s) Participants were 46 high school students. Methods Participants were randomly distributed between two conditions (IVR versus non-IVR video) to complete five mini-labs for approximately 30 min. Results Participants in the IVR condition had higher self-perceptions of agency and situational interest compared to the non-IVR condition. Several regression models of learning outcomes showed agency was a significant predictor for all outcomes, while situational interest was only a significant predictor for declarative and procedural knowledge. Finally, when examining learners' performance on a practical assessment of Chemistry skills, we found that those in the IVR condition were more accurate than those in the non-IVR condition. Conclusions Our results empirically support certain assumptions of the CAMIL model, propose theoretical expansions of the model, and highlight some of the limitations of IVR-based learning. We discuss implications for designing intelligent, adaptive scaffolds embedded in IVR, including using multimodal process-based data during learning for real-time feedback.

Fields of Study & Subjects

Science Education and Pedagogy Innovative Teaching Methods Innovative Teaching and Learning Methods

Publication details

Journal / source
Learning and Instruction
Publisher
Elsevier BV
Volume
104
Pages
102344–102344
Publication date
5 March 2026
Language
EN