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Autor/inn/enLeavy, Aisling; Hourigan, Mairead
TitelThe Role of Perceptual Similarity, Context, and Situation When Selecting Attributes: Considerations Made by 5-6-Year-Olds in Data Modeling Environments
QuelleIn: Educational Studies in Mathematics, 97 (2018) 2, S.163-183 (21 Seiten)Infoseite zur Zeitschrift
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ZusatzinformationORCID (Leavy, Aisling)
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0013-1954
DOI10.1007/s10649-017-9791-2
SchlagwörterTeaching Methods; Teacher Educators; Preservice Teachers; Young Children; Elementary School Mathematics; Elementary School Students; Mathematics Instruction; Mathematical Models
AbstractClassroom data modeling involves posing questions, identifying attributes of phenomena, measuring and structuring these attributes, and then composing, revising, and communicating the outcomes. Selecting attributes is a fundamental component of data modeling, and the considerations made when selecting attributes is the focus of this paper. A teaching experiment involving 2 teacher educators and 25 pre-service teachers (PSTs) was carried out with 24 young children (5-6-year-olds) as part of a 4-day data modeling investigation. Although perceptual features of the data influenced initial approaches to attribute selection, considerations of the problem situation influenced a shift from the perceptual and towards consideration of attributes such as taxonomy, habitat, behavior, and diet. Expertise in the data context (animal kingdom) and ability to collaborate and negotiate within groups supported children in their ability to switch attributes, attend to multiple situations presented by the problem, and modify and extend their categorizations of data. (As Provided).
AnmerkungenSpringer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2020/1/01
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