MOOC网络的行为交互及影响因素研究

大规模开放课程已成为一种在线学习的新途径,探索学习者-课程交互关系网络的拓扑结构,对于提高学习者参与度和MOOC平台的活跃程度具有重要作用。研究随机爬取“中国大学MOOC”的学习者数据,利用复杂网络分析工具探究学习者的度分布演变,基于学习者的学习行为建模,提出学习者学习增益产出模型,并利用Pajek软件将二模网映射至学习者一模关系网;同时结合学习者的属性数据,构建属性数据各分量影响学习者参与课程度的回归模型。

Research on the Behavior Interaction and Influencing Factors of MOOC Network

Large-scale open courses have become a new way of online learning. Exploring the topology of the learner-course interaction network has an important role in improving learner participation and the activity of the MOOC platform. The study randomly crawls the learner data of "China University MOOC", uses complex network analysis tools to explore the evolution of the degree distribution of learners, and based on the learning behavior modeling of the learners, proposes a learner learning gain output model, and uses Pajek software to The two-mode network is mapped to the learner''s one-mode relationship network; at the same time, a regression model is constructed by combining the attribute data of the learners with each component of the attribute data that influences the learner''s participation in the course.