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A unified framework for multi-level analysis of distributed learning

A unified framework for multi-level analysis of distributed learning A Unified Framework for Multi-Level Analysis of Distributed Learning Daniel Suthers Dept. of Information and Computer Sciences University of Hawaii, 1680 East-West Road, POST 309 Honolulu, HI 96822, USA Devan Rosen School of Communications Ithaca College 953 Danby Road, Ithaca, NY 14850, USA suthers@hawaii.edu ABSTRACT Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A unified framework for multi-level analysis of distributed learning

Association for Computing Machinery — Feb 27, 2011

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References (56)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISBN
978-1-4503-0944-8
doi
10.1145/2090116.2090124
Publisher site
See Article on Publisher Site

Abstract

A Unified Framework for Multi-Level Analysis of Distributed Learning Daniel Suthers Dept. of Information and Computer Sciences University of Hawaii, 1680 East-West Road, POST 309 Honolulu, HI 96822, USA Devan Rosen School of Communications Ithaca College 953 Danby Road, Ithaca, NY 14850, USA suthers@hawaii.edu ABSTRACT Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential

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