LEARNING PERSONALISATION METHODS AND TECHNOLOGIES IN VIRTUAL LEARNING ENVIRONMENTS

The paper aims to analyse different methods and technologies to personalise learning in virtual learning environments (VLEs). Special attention is paid to application of educational data mining (EDM) to support learning personalisation in VLEs, namely Moodle. In the paper, first of all, literature review was performed on EDM methods and techniques used to personalise students' e-learning activities. Literature review has revealed that EDM is 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 (e.g. chat, choice, database, feedback, forum, glossary, lesson, quiz, survey, wiki, workshop) 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 and 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 EDM methods and techniques (e.g. classification, clustering, association rules, prediction, decision tree, case-based reasoning), 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 EDM, we could noticeably enhance students' learning quality and effectiveness.