摘要
针对大规模在线公开课程(MOOC)注册学员高退出率和低完成率这一现象,通过数据挖掘的聚类手段,对参入MOOC学员的学习状态变化进行了分析,揭示参与MOOC课程的学员在学习目的上的分集,使MOOC课程发起者能够更好地调整授课内容和课程设置来满足不同学生的需求。
The paper focuses on the high dropout rate versus the low completion rate among registered students for Massive Open Online Courses (MOOCs). It employs the clustering technique in data mining to analyze the changing process of participants’ learning motivations and reveal the different types of intentions for taking part in MOOCs, in a bid to help course designers better adjust lectures and curricula to meet the needs of different groups of students.
出处
《石油化工管理干部学院学报》
2015年第1期33-36,共4页
Journal of Sinopec Management Institute
关键词
大规模在线公开课程
学习状态
聚类分析
Massive Open Online Courses,learning motivation,clustering analysis