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)
DOI: 10.4018/978-1-60566-342-5.ch006
OnDemand PDF Download:
No Current Special Offers


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.
Chapter Preview

Motivation And Background

Correspondence courses have been available for over a century, for example, the University of Wyoming began offering extension courses in 1892 (Miller, Ditzler, & Lamb, 2003) correspondence courses have traditionally been designed for individual students with a self-motivated learning style, studying primarily from text materials.

A National Science Foundation report (NSF, 2002) discusses how technology, from radio to television; to audio and video cassettes; and to audio and video conferencing has affected distance education. The report states that the recent use of Internet technologies, especially the Web, has “allowed both synchronous and asynchronous communication among students and between faculty and students” (p. 1) and has “stimulated renewed interest in distance education.” (p. 4) It also mentions that “stimulating interaction among students” (p. 4) can help reduce dropout rates, which it says may be higher in distance education than in traditional courses. Finally, it cites some studies that “suggest the Web is superior to earlier distance education technologies because it allows teachers to build collaborative and team-oriented communities.”

Complete Chapter List

Search this Book: