MOOC视频学习行为大数据分析  

MOOC Video Learning Behavior Big Data Analysis

作　　者：罗永 李建平[1] 王晓[1] LUO Yong;LI Jian-ping;WANG Xiao(National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学文理学院数学系

出　　处：《大学数学》2019年第6期20-26,共7页College Mathematics

基　　金：湖南省教育科学十三五规划课题(XJK18CXX010);湖南省教学改革课题(湘教通[2018]436);高等学校大学教学研究与发展中心基金(CMC20190305);湖南省研究生教学改革课题(研发[2019]98)

摘　　要：分析了MOOC观看视频行为数据的特征和影响学习者持续观看视频的因素,以及MOOC最受欢迎视频时长的分析方法.研究结果表明MOOC观看视频行为数据具有稀疏性和低秩的特点,在MOOC观看视频行为数据中包含了大量无效的学习行为;影响学习者持续观看视频的因素很多,且相互独立,不存在主导因素.本研究对促进MOOC课程的建设和发展有积极的作用.This paper analyzes the characteristics of MOOC watching video behavior data and the factors that influence learners to continuously watch video,as well as the analysis method of MOOC’s most popular video duration.The research results show that MOOC watching video behavior data has the characteristics of sparsity and low rank.There are a lot of invalid learning behaviors in MOOC watching video behavior data.There are many factors that influence learners to continuously watch videos,and they are independent of each other,and there is no dominant factor.This study has a positive effect on promoting the construction and development of the MOOC curriculum.

关 键 词：MOOC 学习行为分析 观看视频行为 稀疏 低秩 

分 类 号：O177.5[理学—数学]