A Unified Framework for Bounded and Unbounded Numerical Estimation

Autor/inn/en	Kim, Dan; Opfer, John E.
Titel	A Unified Framework for Bounded and Unbounded Numerical Estimation
Quelle	In: Developmental Psychology, 53 (2017) 6, S.1088-1097 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0012-1649
DOI	10.1037/dev0000305
Schlagwörter	Computation; Numbers; Children; Cognitive Development; Mathematics Achievement; Prediction; Models; Bayesian Statistics; Kindergarten; Grade 1; Grade 2; Elementary School Students; Cognitive Processes; Statistical Analysis; Ohio (Columbus) + Suchen Sie Ihr Suchwort? Zahlenraum; Child; Kind; Kinder; Kognitive Entwicklung; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Vorhersage; Analogiemodell; School year 01; 1. Schuljahr; Schuljahr 01; School year 02; 2. Schuljahr; Schuljahr 02; Cognitive process; Kognitiver Prozess; Statistische Analyse
Abstract	Representations of numerical value have been assessed by using bounded (e.g., 0-1,000) and unbounded (e.g., 0-?) number-line tasks, with considerable debate regarding whether 1 or both tasks elicit unique cognitive strategies (e.g., addition or subtraction) and require unique cognitive models. To test this, we examined how well a mixed log-linear model accounted for 86 5- to 9-year-olds' estimates on bounded and unbounded number-line tasks and how well it predicted mathematical performance. Compared with mixtures of 4 alternative models, the mixed log-linear model better predicted 76% of individual children's estimates on bounded number lines and 100% of children's estimates on unbounded number lines. Furthermore, the distribution of estimates was fit better by a Bayesian log-linear model than by a Bayesian distributional model that depicted estimates as being anchored to varying number of reference points. Finally, estimates were generally more logarithmic on unbounded than bounded number lines, but logarithmicity scores on both tasks predicted addition and subtraction skills, whereas model parameters of alternative models failed to do so. Results suggest that the logarithmic-to-linear shift theory provides a simple, unified framework for numerical estimation with high descriptive adequacy and yields uniquely accurate predictions for children's early math proficiency. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11