IEEE International conference on Computional Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2013

Abstract: Virtual environments are more and more used for educational and training purposes. In order to design virtual environments for these applications in particular, it is very important to get a deep understanding of the relevant design features supporting the user's process of learning and comprehension. Relevance and implementation of these features as well as the benefits of virtual learning environments over traditional educational approaches in general are rarely explored. Focusing on modes of interaction in this work, we examined the effect of different motion types on the knowledge acquisition of users in various virtual environments. For our study we chose a simple memorization task as approximation of low cognitive knowledge acquirement. We hypothesized motion types and immersion levels influence memorization performance in virtual environments. The memorization task was conducted in two virtual environments with different levels of immersion: A high-immersive Cave Automatic Virtual Environment (CAVE) and a low-immersive desktop virtual environment. Two motion types in virtual environments were explored: Physical and virtual walking. In the CAVE physical walking was implemented by using motion capturing and virtual walking was realized using a joystick-like input device. The results indicate neither motion types nor immersion levels in virtual environments affect memorization performance significantly.