As a way to gain greater insight into the operation of Library and Information Science (LIS) e-learning communities, the presented work applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks within such communities. The main thrust of the study is to find a way to use computers to automatically discover social ties that form between students just from their threaded discussions. 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 via a survey. However, such a survey is difficult to collect due to the high cost associated with data collection and the sensitive nature of the types of questions that must be asked. To overcome these limitations, the paper presents a new, content-based method for automated discovery of social networks from threaded discussions dubbed name networks. When fully developed, name networks can be used as a real time diagnostic tool for educators to evaluate and improve teaching models and to identify students who might need additional help or students who may provide such help to others.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105553 |
| Date | January 2009 |
| Creators | Gruzd, Anatoliy |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
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