A Mixed Reality-Based Framework for Blended Learning Environment

The process of bridging the gap between reality and virtual data has been possible through exponential technologies such as Mixed Reality (MR), Augmented Reality (AR) and Virtual Reality (VR). MR has so far proffered numerous advantages ranging from communication, navigation, medical aid, data modelling, visualization, and education to remote accessibility across diverse platforms. This paper proposes a systematic framework for knowledge acquisition and synthesis through MR based constructivism learning theory. The methodology uses a bottom-up approach and is based upon the Dryfus modelling. The existing drawbacks of poor learning retention, interactive knowledge acquisition and synthesis are addressed based upon MR technology. The framework provides the basis for embedding MR technology into analyzing and enabling human practical knowledge, as well as extending the scope for knowledge acquisition to rediscover the learning process in educational environments. Furthermore, existing MR based solutions for learning theories are reviewed. The validation of the proposed framework is carried out using technology acceptance model. The real-time knowledge acquisition device embedded with MR technology is explored and enhanced to demonstrate the accuracy and efficacy of the proposed framework.