基于大数据的“国图公开课”用户学习行为研究

Research on User Learning Behavior of the National Library Open Course Based on Big Data

“国图公开课”自上线以来,在拓宽国家图书馆社会教育职能中发挥着重要的作用。随着数万名学生加入并在“国图公开课”平台上学习课程,用户与平台之间产生了丰富的用户数据以及学习行为记录。面向“国图公开课”繁复的用户数据资源,利用关联数据挖掘方法和可视化技术可实现关联算法及结果可视化的系统结合,优化学生学习过程模式。建立基于大数据的用户学习行为分析系统,设计无交叉的网格优化映射,实现事件相关性、事件排名、网络社交三类行为要素的可视化。并利用“国图公开课”数据资源中的User数据集、Session数据集和Event数据集进行数据挖掘分析测试,结果表明,该系统实现了对用户学习行为数据的有效关联聚类,获得了良好的可视化效果。

Since the launch of the National Library Open Course (NLOC), it has played an important role in broadening the social education function of the National Library. With tens of thousands of students joining and learning on the NLOC platform, there is a wealth of user data and learning behavior records between users and platforms. Facing the complicated user data resources of NLOC, through the associated data mining method and the visualization realization technology, the system combination of the association algorithm and the result visualization is realized;the search for the optimization of the student learning process mode is sought. By establishing a user-learning behavior analysis model system based on big data, the grid-free optimization mapping is designed, and the visual analysis of event correlation, event ranking and network social behavior elements is realized. The data mining analysis test is carried out by using the User dataset, Session dataset and Event dataset in the NLOC data resources. The results show that the system achieves effective association clustering of student learning behavior data and obtains a good visualization effect.