Methods of data mining and educational analytics

Abstract

The relevance of the study is due to the fact that there are currently more questions than specific answers on the topic in the context of intellectual analysis of educational data: how it is done, for what and how we can use it, what metrics to include in the sample and how to make forecasts. Undoubtedly, in the coming years there will be a transition from discussions to the practical implementation of educational analytics in educational processes. The purpose of the study is to systematize the methods of intellectual analysis of educational data in the context of the difference between educational analytics and pedagogical diagnostics and other methods of data collection. The results of the study will help to build a learning strategy and combine the objectives of the training program with the effectiveness of the educational process and the expected results from the students. In this regard, the author considers the types of educational analytics. The scientific novelty of the research lies in the systematization of the areas of research interests related to data mining in education and educational analytics. It is proved that educational analytics in combination with intellectual analysis of educational data makes it possible to develop accurate models that characterize the behavior of students, their properties, weaknesses and strengths of content and interaction with it, team and group dynamics. The practical significance of the study lies in the fact that the methods considered will allow to assess the current state of the training system or program, predict the desired results and draw up a roadmap of planned changes. For pedagogical designers and methodologists, the presented methods will become the foundation for optimizing the program. Thanks to the presented methods, students receive the most relevant, engaging and meaningful educational experience.