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Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

Dan Phung (Columbia University, USA), Giuseppe Valetto (IBM T.J. Watson Research Center, USA), Gail E. Kaiser (Columbia University, USA), Tiecheng Liu (University of South Carolina, USA), and John R. Kender (Columbia University, USA)
Copyright: © 2007 |Pages: 18
DOI: 10.4018/jdet.2007040105
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Abstract

The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In this article, we present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view instructional videos in synchrony. Video player actions, like play, pause and stop, can be initiated by any group member and and the results of those actions are synchronized with all the other students. These features allow students to review a lecture video in tandem, facilitating the learning process. AI2TV upholds the invariant that each student will receive semantically equivalent content at all times. Experimental trials show that AI2TV successfully synchronizes instructional videos for distributed students while concurrently optimizing the video quality, even under conditions of fluctuating bandwidth, by adaptively adjusting the quality level for each student while still maintaining the semantic content of the instructional videos.

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