Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes

Autor/inn/en	Joksimovic, Srecko; Jovanovic, Jelena; Kovanovic, Vitomir; Gasevic, Dragan; Milikic, Nikola; Zouaq, Amal; van Staalduinen, Jan Paul
Titel	Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes
Quelle	In: IEEE Transactions on Learning Technologies, 13 (2020) 1, S.38-51 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Joksimovic, Srecko) ORCID (Jovanovic, Jelena) ORCID (Kovanovic, Vitomir) ORCID (Gasevic, Dragan) ORCID (Milikic, Nikola)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2019.2916808
Schlagwörter	Group Discussion; Speech Acts; Computer Assisted Instruction; Discourse Analysis; Social Structure; Holistic Approach; Outcomes of Education; Social Networks; Network Analysis; Online Courses; Foreign Countries; Mass Instruction; Industrial Arts; Course Descriptions; Computer Mediated Communication; Group Dynamics; Cooperative Learning; Netherlands + Suchen Sie Ihr Suchwort? Gruppendiskussion; Sprechakt; Computer based training; Computerunterstützter Unterricht; Diskursanalyse; Sozialstruktur; Holistischer Ansatz; Lernleistung; Schulerfolg; Social network; Soziales Netzwerk; Netzplantechnik; Online course; Online-Kurs; Ausland; Industriekultur; Kunstgewerbe; Kursstrukturplan; Computerkonferenz; Gruppendynamik; Kooperatives Lernen; Niederlande
Abstract	Learning in computer-mediated setting represents a complex, multidimensional process. This complexity calls for a comprehensive analytical approach that would allow for understanding of various dimensions of learner generated discourse and the structure of the underlying social interactions. Current research, however, primarily focuses on manual or, more recently, supervised methods for discourse analysis. Moreover, discourse and social structures are typically analyzed separately without the use of computational methods that can offer a holistic perspective. This paper proposes an approach that addresses these two challenges, first, by using an unsupervised machine learning approach to extract speech acts as representations of knowledge construction processes and finds transition probabilities between speech acts across different messages, and second, by integrating the use of discovered speech acts to explain the formation of social ties and predicting course outcomes. We extracted six categories of speech acts from messages exchanged in discussion forums of two MOOCs and each category corresponded to knowledge construction processes from well-established theoretical models. We further showed how measures derived from discourse analysis explained the ways how social ties were created that framed emerging social networks. Multiple regression models showed that the combined use of measures derived from discourse analysis and social ties predicted learning outcomes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01