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Automated discovery of social networ...

Gruzd, Anatoliy Anatoliyovych.

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Automated discovery of social networks in online learning communities.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automated discovery of social networks in online learning communities./
Author:	Gruzd, Anatoliy Anatoliyovych.
Description:	119 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-05, Section: A, page: 1479.
Contained By:	Dissertation Abstracts International71-05A.
Subject:	Library Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3406147
ISBN:	9781109730067

Automated discovery of social networks in online learning communities.
Gruzd, Anatoliy Anatoliyovych.

Automated discovery of social networks in online learning communities. - 119 p.

Source: Dissertation Abstracts International, Volume: 71-05, Section: A, page: 1479.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as 'name network'. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed 'name network' method for collecting social network data is a viable alternative to costly and time-consuming collection of users' data using surveys. The study also demonstrates how social networks produced by the 'name network' method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the 'name network' method in other types of online communities.

ISBN: 9781109730067Subjects--Topical Terms:

881164
Library Science.

Automated discovery of social networks in online learning communities.
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500 $a Source: Dissertation Abstracts International, Volume: 71-05, Section: A, page: 1479.
500 $a Adviser: Caroline Haythornthwaite.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.
520 $a As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as 'name network'. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed 'name network' method for collecting social network data is a viable alternative to costly and time-consuming collection of users' data using surveys. The study also demonstrates how social networks produced by the 'name network' method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the 'name network' method in other types of online communities.
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