Statistical Discourse Analysis: A Method for Modelling Online Discussion Processes

Autor/inn/en	Chiu, Ming Ming; Fujita, Nobuko
Titel	Statistical Discourse Analysis: A Method for Modelling Online Discussion Processes
Quelle	In: Journal of Learning Analytics, 1 (2014) 3, S.61-83 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Computer Mediated Communication; Data Collection; Data Analysis; Discourse Analysis; Statistical Analysis; Asynchronous Communication; Online Courses; Educational Technology; Individual Characteristics; Content Analysis; Gender Differences; College Students; Technology Uses in Education; Scaffolding (Teaching Technique); Metacognition; Hypothesis Testing; Markov Processes; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Computerkonferenz; Data capture; Datensammlung; Auswertung; Diskursanalyse; Statistische Analyse; Online course; Online-Kurs; Unterrichtsmedien; Personality characteristic; Personality traits; Persönlichkeitsmerkmal; Inhaltsanalyse; Geschlechterkonflikt; Collegestudent; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Hypothesenprüfung; Hypothesentest; Markowscher Prozess; Monte-Carlo-Methode
Abstract	Online forums (synchronous and asynchronous) offer exciting data opportunities to analyze how people influence one another through their interactions. However, researchers must address several analytic difficulties involving the data (missing values, nested structure [messages within topics], non-sequential messages), outcome variables (discrete outcomes, rare instances, multiple outcome variables, similarities among nearby messages), and explanatory variables (sequences of explanatory variables, indirect mediation effects, false positives, and robustness of results). We explicate a method that addresses these difficulties (Statistical Discourse Analysis or SDA) and illustrate it on 1,330 asynchronous messages written and self-coded by 17 students during a 13-week online educational technology course. Both individual characteristics and message attributes were linked to participants' online messages. Men wrote more messages about their theories than women did. Moreover, some sequences of messages were more likely to precede other messages. For example, opinions were often followed by elaborations, which were often followed by theorizing. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01