Abstract
Mixed reality environments as in-betweens of real-world and virtual-reality (VR) environments are created by combining real world objects with computer generated ones. In general, mixed-reality along with VR technology provides innovative ways of showing relationships and connections in the real world, mainly by complimenting real objects with additional information (text, audio and video overlays). In Science Technology and Engineering (STE) disciplines, collaborative learning is about learners working together in teams or groups on structured learning tasks, that typically have a clearly defined goal and are arranged in such a manner that all members of the team are involved. A collaborative approach builds individual and group accountability in each learner as well as one or more soft skills such as communicating with peers, managing resources and the ability to make decisions. This paper presents the use of mixed-reality environments for personalised learning along with two approaches based on message passing paradigms for implementing collaborative learning in mixed-reality environments. The first approach shows the use of a pair of geographically distant mixed-reality environments for remote collaborative learning, while the second approach show multiple (independent) mixed-reality tools synchronised for co-located group work/learning. The statistical analysis of a research study with over 70 respondents from STE disciplines who were exposed to mixed-reality tools developed using mobile technology is also presented and discussed in the context of collaborative work within mixed-reality environments. The contributions includes unique implementations of mixed-reality based collaborative learning environments capable of providing similar experience to that obtained from traditional real-world environments.
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Onime, C., Uhomoibhi, J., Wang, H. (2018). A Mixed-Reality Environment for Personalised and Collaborative Learning in Science and Engineering. In: Auer, M., Guralnick, D., Simonics, I. (eds) Teaching and Learning in a Digital World. ICL 2017. Advances in Intelligent Systems and Computing, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-73204-6_62
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