The application of datamining methods to personalised learning environments

The main research area of the dissertation is an investigation of data mining methods and its application in the distance education. Information about the learning process activities, progress and the final evaluations is accumulated in Virtual Learning Environments (VLE). It is possible to observe various learning patterns or repetitions while processing this data with data mining methods. The main research object of the dissertation is application of data mining methods in VLE for the accumulated data of learners’ activities. This object is actual for the data mining because it is possible to develop modules after the data interpretation and, based on the following, to improve the quality of distance education. The following tasks are related to this object: formation of the original data set, data clustering, Case-Based Reasoning, visualisation and classification. The research made in this dissertation proved that it is possible to improve study process after adding computer agents to the VLE and thus to facilitate all users’ groups activities and interactions. The dissertation consists of six chapters and reference list. The total size of the dissertation is 118 pages, 46 pictures and 8 tables. The results of the research are published in 6 scientific publications. 1 article is published in foreign scientific publication, included in the main journals’ list of the Institute for Scientific Information, 5 articles in the peer reviewed Lithuanian and foreign publications. The results of the research have been presented and discussed in 11 national and international conferences