基于教育数据挖掘的学情数据分析研究

Research on the Analysis of Learning Data Based on Educational Data Mining

大数据时代,教育数据的价值正在被广大的教育研究者重新认识和评估。因此,采用教育数据挖掘方法进行数据分析,让数据发声,指导教学。研究通过数据特征分析、聚类分析、相关性分析和社会网络分析四个角度展开。结果表明:该高校网络课程中,大部分学生能够保证课堂任务的完成;课程群体划分为优秀学习者、普通学习者和风险学习者三类;课堂内相关学习因素之间呈现一定正相关性,但是网络课堂行为与线下考试之间并没有直接相关性;讨论区中呈现教师为中心的“问题式”讨论。

In the era of Big Data,the value of educational data is being re-understood and re-evaluated by a wide range of educational researchers.Therefore,data analysis through using educational data mining methods is used to give voice to data and guide teaching and learning.The study was conducted through four perspectives:data feature analysis,cluster analysis,correlation analysis and social network analysis.The results show that:most students in this university's online course are able to ensure the completion of classroom tasks;the course community is divided into three categories:excellent learners,average learners and at-risk learners;there is a positive correlation between relevant learning factors within the classroom,but there is no direct correlation between internet classroom behaviors and offline exams;the discussion forum showed a teacher-centered"question-based"discussion.