Two Tales of Time: Uncovering the Significance of Sequential Patterns among Contribution Types in Knowledge-Building Discourse

Autor/inn/en	Chen, Bodong; Resendes, Monica; Chai, Ching Sing; Hong, Huang-Yao
Titel	Two Tales of Time: Uncovering the Significance of Sequential Patterns among Contribution Types in Knowledge-Building Discourse
Quelle	In: Interactive Learning Environments, 25 (2017) 2, S.162-175 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Chen, Bodong) ORCID (Hong, Huang-Yao)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2016.1276081
Schlagwörter	Elementary Education; Knowledge Level; Databases; Coding; Cooperative Learning; Sequential Approach; Data Analysis; Group Discussion; Interaction; Learning Processes; Science Instruction; Elementary School Students; Foreign Countries; Computer Mediated Communication; Statistical Analysis; Inquiry; Canada (Toronto) + Suchen Sie Ihr Suchwort? Elementarunterricht; Wissensbasis; Datenbank; Codierung; Programmierung; Kooperatives Lernen; Schrittfolge; Auswertung; Gruppendiskussion; Interaktion; Learning process; Lernprozess; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Ausland; Computerkonferenz; Statistische Analyse
Abstract	As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish "productive" threads of knowledge-building discourse. A database of Grade 1-6 knowledge-building discourse was first coded for the posts' "contribution types" and discussion threads' "productivity." Two distinctive temporal analysis techniques--Lag-sequential Analysis (LsA) and Frequent Sequence Mining (FSM)--were subsequently applied to detecting sequential patterns among contribution types that distinguish productive threads. The findings of LsA indicated that threads that were characterized by mere opinion-giving did not achieve much progress, while threads having more transitions among questioning, obtaining information, working with information, and theorizing were more productive. FSM further uncovered from productive threads distinguishing frequent sequences involving sustained theorizing, integrated use of evidence, and problematization of proposed theories. Based on the significance of studying temporality in collaborative learning revealed in the study, we advocate for more analytics tapping into temporality of learning. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01