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Allison Starks; Stephanie Michelle Reich – Information and Learning Sciences, 2024
Purpose: This study aims to explore children's cognitions about data flows online and their understandings of algorithms, often referred to as algorithmic literacy or algorithmic folk theories, in their everyday uses of social media and YouTube. The authors focused on children ages 8 to 11, as these are the ages when most youth acquire their own…
Descriptors: Concept Formation, Children, Social Media, Video Technology
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Carter, Philip; And Others – Journal of Experimental Child Psychology, 1983
Two experiments studied nine-year-olds, l3-year-olds, and adults in their encoding of two kinds of stimuli taken from a psychometric measure of spatial aptitude. The first experiment used letter-like stimuli; the second employed multi-element flags. (CI)
Descriptors: Adults, Age Differences, Algorithms, Children
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Dreger, Ralph Mason; And Others – Multivariate Behavioral Research, 1988
Seven data sets (namely, clinical data on children) were subjected to clustering by seven algorithms--the B-coefficient, Linear Typal Analysis; elementary linkage analysis, Numerical Taxonomy System, Statistical Analysis System hierarchical clustering method, Taxonomy, and Bolz's Type Analysis. The little-known B-coefficient method compared…
Descriptors: Algorithms, Children, Clinical Diagnosis, Cluster Analysis
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Mills, Carol J.; And Others – Journal of Educational Psychology, 1993
Among 1,453 male and 1,133 female academically talented 7- to 11-year-old students, boys performed better overall than girls on mathematical reasoning. Gender differences appeared as early as second grade, varying according to mathematics subskills. Male performance was better on tasks requiring application of algebraic rules and understanding of…
Descriptors: Academically Gifted, Age Differences, Algebra, Algorithms