Video-based online learning is becoming commonplace in higher education settings. Prior studies have suggested design principles and instructional strategies to boost video-based learning. However, little research has been done on different learner characteristics, such as how learners behave, what behavioral patterns they exhibit, and how different they are from each other. To fill this research gap in student-video interaction, we employed learning analytics to obtain useful insights into students' learning in the context of video-based online learning. From 11 log behaviors represented by log data from 72 college students, four behavioral patterns were identified while students learned from videos: browsing, social interaction, information seeking, and environment configuration. Based on the behavioral patterns observed, participants were classified into two clusters. Participants in the active learner cluster exhibited frequent use of social interaction, information seeking, and environment configuration, while participants in the passive learner cluster exhibited only frequent browsing. We found that active learners exhibited higher learning achievement than passive learners.

基于视频的在线学习在高等教育环境中正变得司空见惯。先前的研究提出了设计原则和教学策略，以促进基于视频的学习。但是，针对不同学习者特征的研究很少，例如学习者的行为方式，表现出的行为方式以及彼此之间的差异。为了填补学生与视频互动中的这一研究空白，我们采用了学习分析方法，以在基于视频的在线学习的背景下获得对学生学习的有用见解。从72名大学生的日志数据代表的11种日志行为中，学生在从视频中学习时识别了四种行为模式：浏览，社交互动，信息寻求和环境配置。根据观察到的行为模式，参与者分为两类。主动学习者集群中的参与者展示出频繁使用社交互动，信息搜索和环境配置，而被动学习者集群中的参与者展示出仅频繁浏览。我们发现主动学习者比被动学习者表现出更高的学习成就。