Virtual, augmented, and mixed reality-based learning systems: personalisation framework - Dvareckienė, Viktorija; Kurilovas, Eugenijus; Jevsikova, Tatjana (Lietuvos matematikos rinkinys, 2016)
The paper is aimed to analyse the problem of personalisation of Virtual Reality/Augmented Reality/Mixed Reality (VR/AR/MR) based learning systems. Research results are two-fold: first, the results of systematic literature review are presented, and, second, VR/AR/MR-based learning systems personalisation framework is proposed. First of all, systematic literature review on research topic was conducted in Thomson Reuters Web of Science database and applying Semantic Scholar search tool. The review revealed that strides are being made in education using VR/AR/MR, although much needs to be done. The possibilities of VR/AR/MR application in education seem to be endless and bring many advantages to students of all ages. Few are creating content that may be used for educational purposes, with most advances being made in the entertainment industry, but many understand and realise the future and importance of education applying VR/AR/MR. Many studies argue that new VR/AR/MR-based learning systems are more effective in comparison with traditional ones. Teachers and students like learning content and activities provided by VR/AR/MR technologies. On the other hand, although the concept of VR/AR/MR has already been proposed more than 20 years ago, most applications are still limited to simple visualisation of virtual objects onto spatially limited scenes, and the developed systems did not pass the barrier of demonstration prototypes. Many authors agree that personalisation of VR/AR/MR-based learning platforms should be further analysed. Original personalisation framework of VR/AR/MR-based learning systems is also presented in the paper. According to the framework, personalisation of VR/AR/MR learning systems should be based on applying learners models and intelligent technologies e.g. expert evaluation, ontologies, recommender systems, software agents etc. This pedagogically sound personalisation framework is aimed to improve learning quality and effectiveness.