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Record W3162409925 · doi:10.31234/osf.io/fywsp

Assessing Children’s Computational Skills: Validation of an Adapted Version of the Canadian Achievement Test - Second Edition for 10 year olds

2020· preprint· en· W3162409925 on OpenAlexaffabout
Gabrielle Garon‐Carrier, Michel Boivin, Emmanuel Ouellet, Richard E. Tremblay, Ginette Dionne

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversité de MontréalCentre hospitalier universitaire de QuébecUniversité Laval
Fundersnot available
KeywordsInternal consistencyDiscriminant validityTest (biology)PsychologyConsistency (knowledge bases)Measure (data warehouse)Developmental psychologyConvergent validityAchievement testMathematicsStandardized testPsychometricsMathematics educationComputer science

Abstract

fetched live from OpenAlex

This study tested the validity of the mathematic subtest of the Canadian Achievement Test – Second Edition (CAT/2; Canadian Test Centre, 1992) for 10 year olds, adapted from the original version administered at age 8. The analyses showed satisfactory internal consistency of the adapted version at age 10, and slightly higher internal consistency than that of the original version at age 8 (.81 vs .76). The total scores distribution of the age 10 version were slightly negatively skewed, suggesting that the tool is sensitive to assess children with lower mathematic abilities. Using a correlational design, the results showed substantial cross-age convergent validity between the age 8 and age 10 versions (.49, p < .001), and cross-measure convergent and discriminant validity of this adapted version. We conclude that the adapted mathematics subtest of the Canadian Achievement Test could be used to reliably measure children’s computational skills at age 10.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.319
Teacher spread0.280 · 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 teacher head, not a consensus.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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