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Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning

Dan Phung; Giuseppe Valetto; Gail E. Kaiser; Tiecheng Liu; John R. Kender

Title:
Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning
Author(s):
Phung, Dan
Valetto, Giuseppe
Kaiser, Gail E.
Liu, Tiecheng
Kender, John R.
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-046-05
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources available to students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. A semantic compression model is developed to provide instructional videos at different level-of-details to accommodate dynamic network conditions and usersäó» system requirements. We take advantage of the semantic compression algorithmäó»s ability to provide different layers of semantically equivalent video by adapting the client to play at the appropriate layer that provides the client with the richest possible viewing experience. 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. 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 invariant.
Subject(s):
Computer science
Item views:
217
Metadata:
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