Construction of the Interactive Educational Knowledge Graph and Classification of Student Groups

The attributes of the knowledge nodes in interactive educational graph need to cater students’ online learning preferences, so understanding composition and preferences students process is helpful development more targeted paths. Currently, there are few existing research results on embedding methods based interaction with respect points, student group their preferences. To this end, paper studies construction an classification groups. First, a recommendation idea was proposed Through three types behaviors - human-computer, teacher-student, student-student interactions that occur platform, depth points characterized. effects were quantified by achievement mastered weights which represent importance. Then, differences different stability similarity prediction explored, analysis results, classified into experimental verified effectiveness model.