Adaptive Internet Interactive Team Video

Dan Phung, Giuseppe Valetto, Gail Kaiser

Proceedings of the 4th International Conference on Web-based Learning (ICWL 2005), Hong Kong SAR, China, July 31-August 3, 2005. (Best Paper Award)


The increasing popularity of online courses has highlighted the lack of collaborative tools for student groups. In addition, the intro- duction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources used by students. We present an e-Learning architecture and adaptation model called AI2 TV (Adaptive Internet Interactive Team Video), which allows virtual students, possi- bly some or all disadvantaged in network resources, to collaboratively view a video in synchrony. AI2 TV upholds the invariant that each stu- dent will view semantically equivalent content at all times. Video player actions, like play, pause and stop, can be initiated by any student and their results are seen by all the other students. These features allow group members to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2 TV can successfully synchro- nize video for distributed students while, at the same time, optimizing the video quality, given fluctuating bandwidth, by adaptively adjusting the quality level for each student.



Columbia University Department of Computer Science