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Record W2969444085 · doi:10.1111/mbe.12215

Effects of a Teacher‐Designed and Teacher‐Led Numerical Board Game Intervention: A Randomized Controlled Study with 4‐ to 6‐Year‐Olds

2019· article· en· W2969444085 on OpenAlexafffund
Zachary Hawes, Michelle Cain, Shelly Jones, Nicole Thomson, C Bailey, Ji-Soo Seo, Beverly Caswell, Joan Moss

Bibliographic record

VenueMind Brain and Education · 2019
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of TorontoWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNumeral systemIntervention (counseling)Task (project management)Identification (biology)Control (management)Mathematics educationPsychologyRandomized controlled trialMedical educationComputer scienceEngineeringMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of the current pilot study was to examine the effects of a teacher‐designed and teacher‐led numerical board game intervention. Fifty‐four 4‐ to 6‐year‐olds were randomly assigned to either the number board game intervention or an active control group. Relative to the control group, children who received the number game intervention demonstrated significant improvements on a numeral identification task. This finding is significant in so far as numeral identification skills play a critical role in more advanced numerical and mathematical reasoning. There was no evidence of training‐related improvements on any of the other tasks. In addition to the intervention effects, the present study provides an example of a successful teacher‐researcher collaboration, providing new insights into the making of bidirectional relations between research and practice.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.286
Teacher spread0.279 · 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 designRandomized trial
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

Citations15
Published2019
Admission routes2
Has abstractyes

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