Methods of teaching educational data mining for pedagogical students - E. A. Terbusheva (Открытое образование (Москва), 2019)
The aim of the article is to discuss and argue teaching educational data mining for pedagogical stu-dents and to describe the methodical system of educational data mining teaching for students with a middle level of mathematical and IT disciplines, that contributes to the development of student’s research competence. The relevance of the study is determined by the requirements for the ability of higher education graduates to analyze information and perform research using modern methods and technologies that are mentioned in the educational standards and the government order. They are associated with an increasing amount of accumulated data in various fields and the cost of the knowledge extracted from data. Materials and methods. The article describes the author’s methodical system of educational data mining teaching, which was developed rely on: analysis of requirements and expectations to the re-search competence level, data analysis skills and modern education in general; comparison and analysis of the content of educational programs, books and courses on data mining and related dis-ciplines, generalization of pedagogical experience. The main aspects underlying the methodology: a form of flipped learning, a concentric (iterative) content structure, research teaching methods, a set of practical tasks for developing research competencies and Weka software for data mining as the main technical training tool for practical tasks implementation. The effectiveness of the developed methodological system was tested by the educational process monitoring, students questioning and statistical processing of questionnaires data. Results. The study shows the relevance of educational data mining teaching for students of peda-gogical universities, studying in mathematical and informational specialization. The use of the de-scribed methodic system for senior pedagogical students allows increasing the level of research competence of students and significantly developing the competence of data analysis. Conclusion. The described methodical system can used be partially or completely by teachers and methodologists for teaching data analysis at the modern level and development of research compe-tence of students with an average level of knowledge in mathematical and IT disciplines.