Automatic Classification of Semantic Content of Classroom Dialogue

Autor/inn/en	Song, Yu; Lei, Shunwei; Hao, Tianyong; Lan, Zixin; Ding, Ying
Titel	Automatic Classification of Semantic Content of Classroom Dialogue
Quelle	In: Journal of Educational Computing Research, 59 (2021) 3, S.496-521 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Song, Yu) ORCID (Hao, Tianyong)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0735-6331
DOI	10.1177/0735633120968554
Schlagwörter	Semantics; Classification; Classroom Communication; Dialogs (Language); Teaching Methods; Learning Processes; Feedback (Response); Artificial Intelligence; Elementary School Students; Secondary School Students; Networks; Computational Linguistics; Foreign Countries; Computer Software; Coding; Mathematics Instruction; Science Instruction; Physics; Literacy Education; China + Suchen Sie Ihr Suchwort? Semantik; Classification system; Klassifikation; Klassifikationssystem; Klassengespräch; Dialog; Dialogs; Dialogue; Dialogues; Teaching method; Lehrmethode; Unterrichtsmethode; Learning process; Lernprozess; Künstliche Intelligenz; Sekundarschüler; Linguistics; Computerlinguistik; Ausland; Codierung; Programmierung; Mathematics lessons; Mathematikunterricht; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Physik
Abstract	Due to benefits for teaching and learning, an increasing number of studies have focused on classroom dialogue and how to make it productive. Coding, in which the transcribed conversation is allocated to a set of features, is commonly employed to deal with the textual data arising from this dialogue. This is generally done manually and cannot provide timely feedback to the participants. To address this issue, we explored the possibility of automatically classifying the semantic content of classroom dialogue. Seven categories (prior-known knowledge, analysis, coordination, speculation, uptake, agreement and querying) were distinguished automatically using an artificial neural network-based model. The model achieved acceptable performance and was comparable to human coding. Information about quality of dialogue can be identified in a timely manner. With this knowledge, classroom dialogue can be managed more skilfully, and a more productive form of dialogue is likely to be achieved by teachers and students. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01