Balancing Web 2.0 and Grid Approaches in Distributed Learning Framework

By Alexander Vengerov.

Published by The Technology Collection

Format Price
Article: Print $US10.00
Article: Electronic $US5.00

The explosive growth of Web 2.0 sites and their users often leads to the perception of a paradigm switch and coming revolution of old versus new. Although the importance of the social computing and enthusiasm regarding its use are difficult to ignore, some analysts perceive this phenomenon as hype and a bubble, which will implode into something simple, basic, and not-so-influential. On the other hand the actual fight is perceived as a bigger phenomenon contrasting between rather large clusters of methods often representing the polar sides of the offered scales some of which could be described as Web approach versus Grid approach, resource-centered administration/design versus communicative evolving and self-organization, sensitivity/creativity versus connectivity/control, controlled knowledge management versus communities of practice, application as a product versus being a mode of communication. The paper states that the main phenomenon in these contrasting developments is a growing importance of adaptive learning and the need in its integration with traditional means of information resource management.

Keywords: Social Computing, Distributed Learning, Web 2.0, Grid, Autonomy, Aadaptivity, Change Management

The International Journal of Technology, Knowledge and Society, Volume 3, Issue 3, pp.1-10. Article: Print (Spiral Bound). Article: Electronic (PDF File; 560.267KB).

Dr. Alexander Vengerov

professor, Anisfield School of Business, Ramapo College of NJ, Mahwah, New Jersey, USA

Dr Alexander Vengerov is an associate professor of Information Systems at Ramapo College of NJ. He chaired an All-College Teaching and Learning with Technology Committee, and is the president of Syslearn, Inc. developing learning systems and intelligent learning processes, as well as consulting in the area of business and academic organizational learning.


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