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Autor/inn/enBalyan, Renu; Arner, Tracy; Taylor, Karen; Shin, Jinnie; Banawan, Michelle; Leite, Walter L.; McNamara, Danielle S.
TitelModeling One-on-One Online Tutoring Discourse Using an Accountable Talk Framework
[Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).
Quelle(2022), (7 Seiten)
PDF als Volltext kostenfreie Datei Verfügbarkeit 
ZusatzinformationWeitere Informationen
Spracheenglisch
Dokumenttypgedruckt; online; Monographie
SchlagwörterTutoring; Guidelines; Mathematics Instruction; Computer Assisted Instruction; Teaching Methods; Classroom Communication; Learning Processes; Thinking Skills; Models; Classification; Discourse Analysis; Comparative Analysis; Computer Mediated Communication; Teacher Student Relationship; High School Students; Mathematics Tests; Academic Failure; Algebra; Videoconferencing; Florida
AbstractThe National Council of Teachers of Mathematics (NCTM) has been emphasizing the importance of teachers' pedagogical communication as part of mathematical teaching and learning for decades. Specifically, NCTM has provided guidance on how teachers can foster mathematical communication that positively impacts student learning. A teacher may have different academic goals towards what needs to be achieved in a classroom, which require a variety of discourse-based tools that allow students to engage fully in mathematical thinking and reasoning. Accountable or academically productive talk is one such approach for classroom discourse that may ensure that the discussions are coherent, purposeful and productive. This paper discusses the use of a transformer model for classifying classroom talk moves based on the accountable talk framework. We investigate the extent to which the classroom Accountable Talk framework can be successfully applied to one-on-one online mathematics tutoring environments. We further propose a framework adapted from Accountable Talk, but more specifically aligned to one-on-one online tutoring. The model performance for the proposed framework is evaluated and compared with a small sample of expert coding. The results obtained from the proposed framework for one-on-one tutoring are promising and improve classification performance of the talk moves for our dataset. [For the full proceedings, see ED623995.] (As Provided).
AnmerkungenInternational Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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