E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Kona, Hawaii, United States Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
In this paper, we present a novel system - inVideo for video data analytics, and its use in learning analytics as the field strives towards new cultures of learning in the digital realm involving large quantities of video data in online learning systems and MOOCs.
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 e-learning systems.
Experiments show that inVideo presents an efficient tool that could be used in multiple applied and empirical settings for learning technology research and increase interactions in online learning environment. Using inVideo as an adaptive assessment tool, interactions in online classrooms at University of Maryland increased 7 folds program-wide.
Wang, S., Kelly, W. & Zhang, J. (2015). Using Novel Video Indexing and Data Analytics Tool to Enhance Interactions in e-Learning. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1919-1927). Kona, Hawaii, United States: Association for the Advancement of Computing in Education (AACE).
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