{"title":"协作话语的自动化分析:识别思想集群","authors":"Nobuko Fujita, Chris Teplovs","doi":"10.3115/1599503.1599558","DOIUrl":null,"url":null,"abstract":"This poster explores CSCL practices relating to the use of a tool that employs information visualization techniques and large-scale text processing and analysis to complement qualitative analysis of collaborative discourse. Results from latent semantic analysis and qualitative analysis of online discussion transcripts are compared. Findings suggest that such tools that automate analyses of large text-based data sets can offer CSCL researchers a quantitative and unbiased way of identifying a subset of data to study in depth.","PeriodicalId":120843,"journal":{"name":"International Conference on Computer Supported Collaborative Learning","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Automating the analysis of collaborative discourse: identifying idea clusters\",\"authors\":\"Nobuko Fujita, Chris Teplovs\",\"doi\":\"10.3115/1599503.1599558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This poster explores CSCL practices relating to the use of a tool that employs information visualization techniques and large-scale text processing and analysis to complement qualitative analysis of collaborative discourse. Results from latent semantic analysis and qualitative analysis of online discussion transcripts are compared. Findings suggest that such tools that automate analyses of large text-based data sets can offer CSCL researchers a quantitative and unbiased way of identifying a subset of data to study in depth.\",\"PeriodicalId\":120843,\"journal\":{\"name\":\"International Conference on Computer Supported Collaborative Learning\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Computer Supported Collaborative Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/1599503.1599558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computer Supported Collaborative Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1599503.1599558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automating the analysis of collaborative discourse: identifying idea clusters
This poster explores CSCL practices relating to the use of a tool that employs information visualization techniques and large-scale text processing and analysis to complement qualitative analysis of collaborative discourse. Results from latent semantic analysis and qualitative analysis of online discussion transcripts are compared. Findings suggest that such tools that automate analyses of large text-based data sets can offer CSCL researchers a quantitative and unbiased way of identifying a subset of data to study in depth.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
