Video-based Big Data Analytics in Cyberlearning

Shuangbao Paul Wang
William Kelly


In this paper, we present a novel system, inVideo, for video data analytics, and its use in transforming linear videos into interactive learning objects. InVideo is able to analyze video content automatically without the need for initial viewing by a human. Using a highly efficient video indexing engine we developed, the system is able to analyze both language and video frames. The time-stamped commenting and tagging features make it an effective tool for increasing interactions between students and online learning systems. Our research shows that inVideo presents an efficient tool for learning technology research and increasing interactions in an online learning environment. Data from a cybersecurity program at the University of Maryland show that using inVideo as an adaptive assessment tool, interactions between student–student and student–faculty in online classrooms increased significantly across 24 sections program-wide.


e-learning; video index; big data; learning analytics; assessment

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