A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Individual differences in fraction arithmetic learning
Tekijät: David W. Braithwaite, Elena R. Leib, Robert S. Siegler, Jake McMullen
Kustantaja: ACADEMIC PRESS INC ELSEVIER SCIENCE
Julkaisuvuosi: 2019
Journal: Cognitive Psychology
Lehden akronyymi: COGNITIVE PSYCHOL
Vuosikerta: 112
Aloitussivu: 81
Lopetussivu: 98
Sivujen määrä: 18
ISSN: 0010-0285
eISSN: 1095-5623
DOI: https://doi.org/10.1016/j.cogpsych.2019.04.002
Tiivistelmä
Understanding fractions is critical to mathematical development, yet many children struggle with fractions even after years of instruction. Fraction arithmetic is particularly challenging. The present study employed a computational model of fraction arithmetic learning, FARRA (Fraction arithmetic Reflects Rules and Associations; Braithwaite, Pyke, and Siegler, 2017), to investigate individual differences in children's fraction arithmetic. FARRA predicted four qualitatively distinct patterns of performance, as well as differences in math achievement among the four patterns. These predictions were confirmed in analyses of two datasets using two methods to classify children's performance-a theory-based method and a data-driven method, Latent Profile Analysis. The findings highlight three dimensions of individual differences that may affect learning in fraction arithmetic, and perhaps other domains as well: effective learning after committing errors, behavioral consistency versus variability, and presence or absence of initial bias. Methodological and educational implications of the findings are discussed.
Understanding fractions is critical to mathematical development, yet many children struggle with fractions even after years of instruction. Fraction arithmetic is particularly challenging. The present study employed a computational model of fraction arithmetic learning, FARRA (Fraction arithmetic Reflects Rules and Associations; Braithwaite, Pyke, and Siegler, 2017), to investigate individual differences in children's fraction arithmetic. FARRA predicted four qualitatively distinct patterns of performance, as well as differences in math achievement among the four patterns. These predictions were confirmed in analyses of two datasets using two methods to classify children's performance-a theory-based method and a data-driven method, Latent Profile Analysis. The findings highlight three dimensions of individual differences that may affect learning in fraction arithmetic, and perhaps other domains as well: effective learning after committing errors, behavioral consistency versus variability, and presence or absence of initial bias. Methodological and educational implications of the findings are discussed.
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