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The paper aims to analyse application trends of intelligent multi-agent systems to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying multi-agent systems to personalise learning were identified. Second, main requirements and components for an educational multi-agent system were formulated. Third, based on these components a model of intelligent personalized system is proposed. The system employs five intelligent agents: (1) learning styles identification software agent, (2) learner profile creation software agent, (3) pedagogical suitability software agent, (4) optimal learning units/scenarios creation software agent, and (5) learning analytics/educational data mining software agent.

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.

The paper aims to analyse Educational Data Mining/Learning Analytics application trends to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying educational data mining methods to personalise learning were identified. Second, three main tendencies on educational data mining/learning analytics application in education were formulated. They are: (a) Educational Data Mining/Learning Analytics support self-directed autonomous learning; (b) Educational Data Mining/Learning Analytics systems become essential tools of educational management; and (c) most teaching is delegated to computers, and Educational Data Mining/Learning Analytics based recommendations become better and more reliable than those that can be produced by even the best-trained teachers.