EDUCATIONAL DATA MINING AND LEARNING ANALYTICS

Abstract

The relevance of the study is due to the fact that on the topic in the context of the intellectual analysis of educational data there are currently more questions than specific answers: how it is done, why 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 learning 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 goals 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 areas of research interests related to data mining in education and educational analytics. It is substantiated that educational analytics in conjunction with the intellectual analysis of educational data makes it possible to develop accurate models that characterize the behavior of students, their properties, weak and strong points of content and interaction with it, team and group dynamics. The practical significance of the study lies in the fact that the considered methods will allow us to assess the current state of the training system or program, predict the desired results and draw up a roadmap for planned changes. For pedagogical designers and methodologists, the presented methods will become the foundation for optimizing the program.