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Record W2933138355

Differences in Calculation Fluency between Typically Achieving and Arithmetic Disabled Children

2019· article· en· W2933138355 on OpenAlexaff
Pamela McDonald, Derek H. Berg

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsQueen's University
Fundersnot available
KeywordsFluencyArithmeticContrast (vision)CognitionPsychologyCognitive psychologyMental arithmeticDevelopmental psychologyMathematicsAudiologyComputer scienceArtificial intelligenceMedicineMathematics education
DOInot available

Abstract

fetched live from OpenAlex

This study examined which cognitive processes accounted for differences in arithmetic fluency between arithmetic disabled (AD) children and their typically achieving peers (TA). Specifically, the study explored fluency differences in horizontally and vertically aligned arithmetic problems. A contrast variable was created to capture fluency differences between the two groups. This contrast was used in a series of stepwise regression models to examine which cognitive domains accounted for fluency differences in horizontally and vertically aligned problems. Overall, AD children exhibited impairments in arithmetic fluency relative to their TA peers, regardless of alignment. Further, both naming speed and shifting attention emerged as significant predictors of horizontal and vertical arithmetic fluency. Finally, the importance of predictors relied upon the alignment of arithmetic problems.

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)0.001
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.287
Teacher spread0.256 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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