Immersive and Non-immersive Virtual Reality System to Learn Relative Motion Concepts

Michael Kozhevnikov1 and Johannes Gurlitt2
1Norfolk State University, 2University of Freiburg


Abstract

In the area of relative motion, a variety of students’ misconceptions have been documented. The focus of the current study is to understand the strength and limits of immersive virtual environments as a new media for learning and teaching relative motion concepts. In particular, we are interested which unique features of an immersive virtual reality environment have the potential to assist undergraduate students in learning one-dimensional and two-dimensional relative motion concepts. Thirty seven undergraduate students (19 students assigned to Immersive Virtual environment (IVE) condition and 18 students assigned to Desktop (non-immersive) Virtual Environment (DVE) condition) learned relative motion concepts in interactive virtual reality simulation. Our results show that while both IVE and DVE groups exhibited a significant improvement on relative motion problem solving test, and exhibited a significant shift toward a scientific understanding in their conceptual understanding and epistemological beliefs about the nature of relative motion. We also analyzed the one-dimensional and two-dimensional questions in the problem solving test separately, we found that after training in the simulations, the IVE group performed significantly better than the DVE group on solving two-dimensional relative motion problems. This result supports our hypothesis that egocentric encoding of the scene in IVE (where the learner constitutes a part of a scene being immersed in it) as compared to allocentric encoding on a computer screen in DVI (where the earner is looking on the scene from “outside”) is beneficial for studying two-dimensional problems. The results of this study suggest that such aspects of virtual realities as immersivity, first-hand experience and possibility to change different frame of references can facilitate understanding abstract science phenomena and help in displacing intuitive misconceptions with more accurate mental models.