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The paper aims to analyse application of learning analytics (LA) to support learning personalisation in virtual learning environments, namely Moodle. In the paper, first of all, literature review was performed on LA methods and techniques used to personalise students' e-learning activities. Literature review has revealed that LA are known as the measurement, collection, analysis, and reporting of data about learners and their contexts to understand and optimise learning and environments in which it occurs. In the paper, an original methodology to personalise learning is presented. Second, existing Moodle-based learning activities and tools were interlinked with students' learning styles according to Felder-Silverman learning styles model using expert evaluation method. Third, a group of students was analysed to identify their individual learner profiles, and probabilistic suitability indexes were calculated for each analysed student and each Moodle-based learning activity to identify which learning activities or tools are the most suitable for particular student. The higher is suitability index the better learning activity or tool fits particular student's needs. Fourth, using appropriate LA methods and techniques, we could analyse what particular learning activities or tools were practically used by these students in Moodle, and to what extent. Fifth, the data on practical use of Moodle-based learning activities or tools should be compared with students' suitability indexes. In the case of any noticeable discrepancies, students' profiles and accompanied suitability indexes should be identified more precisely, and students' personal leaning paths in Moodle should be corrected according to new identified data. Thus, using LA, we could noticeably enhance students' learning quality and effectiveness. [ABSTRACT FROM AUTHOR]

The aim of the paper is two-fold: first, to perform systematic literature review of Augmented Reality (AR) learning systems/environments and, second, to propose those systems' personalisation framework based on applying learners' profiles/models and intelligent technologies. First of all, systematic literature review on research topic was conducted in Thomson Reuters Web of Science database. The review revealed that strides are being made in education using AR, although much needs to be done. The possibilities of AR 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 AR. Many studies argue that new AR-based learning systems are more effective in comparison with traditional ones. Teachers and students like learning content and activities provided by AR technologies. On the other hand, although the concept of AR 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 AR-based learning platforms should be further analysed. Therefore, original personalisation framework of AR-based learning systems is presented in the paper. According to the framework, personalisation of AR 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. [ABSTRACT FROM AUTHOR]

The paper is aimed to analyse the problem of learning personalisation applying Resource Description Framework (RDF) standard model. Research results are two-fold: first, the results of systematic literature review on RDF application in learning are presented, and, second, RDF-based learning personalisation approach is proposed. First of all, systematic literature review was conducted in Thomson Reuters Web of Science database and using Semantic Scholar search tool. The review has shown that RDF data model is based upon the idea of making statements about web resources in the form of subject-predicate-object expressions. These expressions are known as triples in RDF terminology. The subject denotes the resource, and the predicate denotes traits or aspects of the resource and expresses a relationship between the subject and the object. The review revealed that linked data and triples-based RDF standard model could be successfully used in education. On the other hand, although linked data approach and RDF standard model are already well-known in scientific literature, only few authors have analysed its application to personalise learning process, but many authors agree that linked data and RDF-based learning personalisation trends should be further analysed. Original RDF-based learning personalisation approach is also presented in the paper. According to this approach, RDF-based personalisation of learning should be based on applying students' learning styles and intelligent technologies. The main advantages of this approach are analyses of interconnections between students' learning styles and suitable learning components (i.e. learning resources, learning methods and activities, learning tools and technologies etc.) based on using pedagogically sound vocabularies of learning components, experts' collective intelligence, and intelligent technologies (e.g. expert evaluation, ontologies, recommender systems, software agents etc.). This pedagogically sound RDF-based personalisation approach is aimed at improving learning quality and effectiveness. [ABSTRACT FROM AUTHOR]