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Record W3135772061 · doi:10.4236/ce.2021.123038

The Efficacy of Teaching Advanced Forms of Patterning to Kindergartners

2021· article· en· W3135772061 on OpenAlexaboutno aff
Patrick E. McKnight, Julie K. Kidd, Debbie A. Gallington, Lauren I. Strauss, Hao Lyu, K. Marinka Gadzichowski, Robert Pasnak

Bibliographic record

VenueCreative Education · 2021
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
FundersInstitute of Education SciencesGeorge Mason UniversityU.S. Department of Education
KeywordsQuarter (Canadian coin)LiteracyMathematics educationEarly literacySubject matterPsychologyTest (biology)Developmental psychologyPedagogyHistoryBiologyCurriculum

Abstract

fetched live from OpenAlex

This project tested the effects of adding instruction of trios of children in patterning, mathematics, early literacy, or social studies to ongoing instruction in kindergartens. Children were randomly assigned to trios which were randomly assigned to one of four kinds of instruction. A quarter of the trios received patterning instruction with a mixture of repeating and growing patterns. Another quarter of the trios were taught number recognition, number order, counting, comparing quantities and related early mathematics. Another quarter were taught letter recognition and sounds, simple words, and other subject matter in early literacy. The remaining quarter of the trios were taught social studies, including recognition of important figures in history and important city and federal services. The children taught patterning outscored the others on a test of patterning, but there were no other significant differences. Implications for patterning instruction as a support for early mathematics or literacy were discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.364
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

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