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Students’ Success Predictive Models Based on Selected Input Parameters Set

Modern LMS systems offer many possibilities for adapting e-learning courses’ teaching materials. In order to make the adjustment more efficient, many educational data should be analyzed. This paper describes one possible solution for evaluation of students’ parameters available at the very beginning of the courses. Based on detected significant input parameters sets, students’ success predictive models are created and key factors that can help prevention of dropping out and failing the course are identified.