Analysis of Learning Behavior Characteristics in Wisdom Classrooms Based on Educational Data Mining Technology
The wisdom classroom is the main battlefield for achieving educational intelligence.Analyzing student behavior in the wisdom classroom helps teachers continuously optimize their decisions and improve the effectiveness of wisdom classroom teaching.By collecting log data of students in the wisdom teaching platforms and using educational data mining technology to perform cluster analysis on the learning behavior characteristics of students in the wisdom classroom teaching platforms from 10 different dimensions,learners can be categorized into three types:highly engaged proactive,moderately engaged passive,and lowly engaged negative.The research found that the learning behavior of these three types of students mostly tends to be passive,lacking in the habit of active learning,focusing more on improving academic performance,and paying less attention to the development of innovative and practical abilities.This suggests that teachers should develop individualized self-improvement plans to enhance students'sense of learning efficacy and motivation when using wisdom classroom teaching platforms.They should also conduct process-oriented evaluations to cultivate students'innovative and practical abilities based on their different types,and provide more diverse learning resources and more effective teaching models according to the characteristics of the subject.
wisdom educationwisdom classroomwisdom teaching platformseducational data mininglearning behavior analysis
万方数据
维普
