大规模私有在线课程微观交互行为模式探究

Exploration of Micro Interaction Behavior in Massive Private Online Courses

随着在线学习的发展,在线学习平台积累了大量的学习者微观学习行为的数据。越来越多的研究人员提出利用这类由技术支持的数据来研究学习过程中交互行为的序列和时间模式,也就是行为序列模式的挖掘与研究。该文聚焦于大规模私有在线课程中的学习者微观交互行为,尝试运用序列挖掘的方法来探讨行为模式的演变,并对不同成就群体之间的模式进行了比较,揭示了高、低成就学习者群体的主要交互模式及其演变趋势,并且就两个群体的模式进行了比较。该研究结果为细粒度层面的交互行为模式的研究提供了一个新的视角,有助于教师进一步了解学习者的在线行为,并针对不同学习者设计专门的干预措施。对于学习平台开发者来说,该研究提供了关于微观在线行为模式的挖掘、呈现和解释的有益尝试,为平台功能的设计和实现提供帮助,此外,还可以帮助教学设计者构建以学习者为中心的课程来促进在线学习。

As online learning grows rapidly all over the world,the online learning platforms have accumulated a large amount of data about learners’micro-level learning behaviors.More and more researchers propose to use the learning behaviors data for studying the sequence and time patterns of interactive behaviors in the learning process.This paper focuses on the micro-interaction behavior of learners in large-scale private online courses,attempts to explore the evolution of behavioral patterns by using the method of sequence mining,and compares the patterns among different achievement groups.The study reveals the main interaction patterns and evolving tendency of high and low achievement learners,and compares the patterns of the two groups.The findings of this study provide a new perspective to investigate interaction patterns at the fine-grained level,which can help instructors to further understand students’online behaviors and design specialized interventions for different learners.For learning platform developers,this study provides useful insights regarding extraction,presentation,and interpretation of online learning analytics,which can inform the realization of platform functions,such as pattern visualization,learning diagnosis,and learner profiles.For instructional designers,it can also help to develop learner-centered courses and platforms to enhance online learning.