Scaffolding tacit knowledge transfer via ontology-driven non-immersive virtual reality: an empirical analysis of skill acquisition in vocational training

Traditional vocational training struggles to transfer “tacit knowledge” – intuitive, context-dependent expertise required for complex spatial planning, motor coordination, and environmental hazard detection. To address this bottleneck in solar photovoltaic (PV) installation training, this study proposes an ontology-based non-immersive virtual reality (VR) system that externalizes expert cues into a digital learning environment. Grounded in the Solar PV Knowledge Ontology, the system uses a desktop-based “Magic Window” interface designed to reduce technology-related barriers. A quasi-experimental design was implemented with 65 engineering students assigned by intact instructional conditions to an experimental group using the interactive VR system or a control group receiving conventional instruction. Assessment instruments were mapped to the ontology dimensions and measured explicit theoretical knowledge, practical procedural skill performance, and blueprint interpretation. The experimental group significantly outperformed the control group across assessed dimensions. These findings suggest that combining formal knowledge modeling with non-immersive VR may support the novice-to-expert transition by preserving spatial context while avoiding interface burdens associated with highly immersive systems. However, cognitive load was used as an instructional design framework rather than as a directly measured outcome in this study. The study provides a scalable framework for digital vocational training and highlights the pedagogical value of aligning interactive learning environments with explicit knowledge structures.