大数据背景下在线视频点击流行为可视化分析与思考——以香港科技大学VisMOOC项目为例

发布时间：2018-07-12 21:15

本文选题：大数据 + 在线视频　；参考：《现代远距离教育》2017年04期

【摘要】：随着数据科学和学习分析的兴起,教育领域内数据集驱动的研究进入学界视野。视频成为在线学习的主要资源,有必要从微观层面对点击流数据可视化分析,研究学习者在线学习行为,进而优化教学策略、提高学习效率。运用数据思维方式,引入香港科技大学Vis MOOC项目,通过从不同角度对视频点击流行为信息进行可视化分析,解读其功能与实现方式,探索点击流数据产生的学习规律。最后,在数据思维的MOOC学习行为研究、基于大数据的MOOC学习交互分析、学习分析视角下MOOC个性化学习、基于点击流数据公开化的MOOC学习预测、大数据时代MOOC视频注释分析五个方面,提出对MOOC研究实践的思考,解决当前MOOC中存在的高辍学率、交互不足等问题,并利用数据挖掘和学习分析技术分析预测学习者学习行为,为其提供个性化服务支持。
[Abstract]:With the rise of data science and learning analysis, the research of data set driven in the field of education has entered the field of vision. Video has become the main resource of online learning. It is necessary to face the visual analysis of click stream data from the micro level, study the online learning behavior of the learners, and then optimize the teaching strategy and improve the learning efficiency. By introducing the Vis MOOC project of Hong Kong University Science & Technology to visualize the information of video clicks from different angles, interpret its function and realization, explore the learning rules produced by clicking stream data. Finally, the study of MOOC learning behavior in data thinking is based on the MOOC learning interaction analysis of large data, and the learning analysis perspective. The next MOOC personalized learning is based on the MOOC learning prediction of clicking flow data and the five aspects of MOOC video annotation analysis in the big data age. It puts forward thinking about the practice of MOOC research, solves the problems of high dropout rate and lack of interaction in current MOOC, and uses data mining and learning analysis techniques to analyze and predict the learning behavior of the learners. Provide personalized service support for it.
【作者单位】：东北师范大学;湖南第一师范学院;
【基金】：教育部人文社科规划项目“基于知识图谱的开放学习资源自主聚合研究”(编号:14YJA880103) 吉林省高等教育教学改革立项课题“精品慕课课程质量建设规范、路径及教学实践评价研究”(编号:吉教高字[2015]32号);基础教育信息化技术湖南省重点实验室(编号:2015TP1017) 湖南省哲学社会科学基金项目“‘互联网+’促进城乡基础教育均衡发展的创新机制与路径研究”(编号:16YBA094)
【分类号】：G434

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