Methods of data mining and educational analytics

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