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Book Chapter: Statistical Discourse Analysis: Testing Educational Hypotheses with Large Datasets of Electronic Discourse

TitleStatistical Discourse Analysis: Testing Educational Hypotheses with Large Datasets of Electronic Discourse
Authors
Issue Date2014
PublisherInformation Science Reference
Citation
Statistical Discourse Analysis: Testing Educational Hypotheses with Large Datasets of Electronic Discourse. In Lim, HL & Sudweeks, F (Eds.), Innovative Methods and Technologies for Electronic Discourse Analysis, p. 285-303. Hershey, Pennsylvania: Information Science Reference, 2014 How to Cite?
AbstractEducators are increasingly using electronic discourse for student learning and problem solving, partially due to its time and space flexibility and greater opportunities for information processing and higher order thinking. When researchers try to statistically analyze the relationships among electronic discourse messages however, they often face difficulties regarding the data (missing data, many codes, non-linear trees of messages), dependent variables (topic differences, time differences, discrete, infrequent, multiple dependent variables) and explanatory variables (sequences of messages, cross-level moderation, indirect effects, false positives). Statistical discourse analysis (SDA) addresses all of these difficulties as shown in analyses of social cues in 894 messages posted by 183 students during 60 online asynchronous discussions. The results showed that disagreements increased negative social cues, supporting the hypothesis that these participants did not save face during disagreements, but attacked face. Using these types of analyses and results, researchers can inform designs and uses of electronic discourse.
Persistent Identifierhttp://hdl.handle.net/10722/205338
ISBN
Series/Report no.Advances in human and social aspects of technology book series (AHSAT)

 

DC FieldValueLanguage
dc.contributor.authorChiu, MMen_US
dc.contributor.authorChen, Gen_US
dc.date.accessioned2014-09-20T02:25:24Z-
dc.date.available2014-09-20T02:25:24Z-
dc.date.issued2014-
dc.identifier.citationStatistical Discourse Analysis: Testing Educational Hypotheses with Large Datasets of Electronic Discourse. In Lim, HL & Sudweeks, F (Eds.), Innovative Methods and Technologies for Electronic Discourse Analysis, p. 285-303. Hershey, Pennsylvania: Information Science Reference, 2014en_US
dc.identifier.isbn9781466644267-
dc.identifier.urihttp://hdl.handle.net/10722/205338-
dc.description.abstractEducators are increasingly using electronic discourse for student learning and problem solving, partially due to its time and space flexibility and greater opportunities for information processing and higher order thinking. When researchers try to statistically analyze the relationships among electronic discourse messages however, they often face difficulties regarding the data (missing data, many codes, non-linear trees of messages), dependent variables (topic differences, time differences, discrete, infrequent, multiple dependent variables) and explanatory variables (sequences of messages, cross-level moderation, indirect effects, false positives). Statistical discourse analysis (SDA) addresses all of these difficulties as shown in analyses of social cues in 894 messages posted by 183 students during 60 online asynchronous discussions. The results showed that disagreements increased negative social cues, supporting the hypothesis that these participants did not save face during disagreements, but attacked face. Using these types of analyses and results, researchers can inform designs and uses of electronic discourse.-
dc.languageengen_US
dc.publisherInformation Science Referenceen_US
dc.relation.ispartofInnovative Methods and Technologies for Electronic Discourse Analysis-
dc.relation.ispartofseriesAdvances in human and social aspects of technology book series (AHSAT)-
dc.titleStatistical Discourse Analysis: Testing Educational Hypotheses with Large Datasets of Electronic Discourseen_US
dc.typeBook_Chapteren_US
dc.identifier.emailChen, G: gwchen@hku.hken_US
dc.identifier.authorityChen, G=rp01874en_US
dc.identifier.doi10.4018/978-1-4666-4426-7.ch013-
dc.identifier.hkuros238838en_US
dc.identifier.spage285en_US
dc.identifier.epage303en_US
dc.publisher.placeHershey, Pennsylvaniaen_US

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