Abstract:
The use of immersive technologies in education has transformed the way complicated technical concepts are taught and understood. Immersive Virtual Reality (IVR), in particular, promotes interactive and experiential learning, which improves comprehension and engagement. Despite its shown beneficial effects across multiple disciplines, its use in teaching essential Operating System (OS) concepts is restricted. This study looks into the effect of IVR-based learning on students' understanding of OS process synchronization topics such as race conditions, mutex locks, Peterson's solution, and deadlocks. A custom-built VR application was developed and evaluated among undergraduate computer science students using surveys and comparative assessments. Students who completed the VR module displayed better engagement, improved conceptual understanding, and stronger learning outcomes across various academic performance levels when compared to those who were taught using traditional means. Predictive modeling with Decision Tree and Random Forest classifiers demonstrated the potential of machine learning in identifying learning outcome patterns based on VR experience. Overall, the findings provide empirical evidence that IVR is an effective pedagogical technique for improving the teaching and learning of complex system ideas in computer science education.
