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Record W4417417655 · doi:10.31234/osf.io/r4dhw_v1

Creation and validation of the LEVANTE core tasks: Internationalized measures of learning and development for children ages 5-12 years

2025· article· W4417417655 on OpenAlexaboutno aff
George Kachergis, Fionnuala O'Reilly, Mika Braginsky, Xingyao Xiao, Amy A. Lightbody, Kat Adams Shannon, Zachary Watson, Lijin Zhang, Rebecca Zhu, Adani Bennett Abutto, Wanjing Anya, Bria Long, Tonya Murray, Jason D. Yeatman, Michael J. Sulik, Jelena Obradović, Nichola Jenkins, Daniel Ansari, María Camila Perfetti, Julián P. Mariño, Luise Hornoff, Manuel Bohn, Daniel B. M. Haun, Nilám Ram, Benjamin W. Domingue, Michael C. Frank

Bibliographic record

Venuenot available
Typearticle
Language
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsSet (abstract data type)Reliability (semiconductor)Core (optical fiber)Sample (material)Measure (data warehouse)CognitionSelection (genetic algorithm)

Abstract

fetched live from OpenAlex

We present the Learning Variability Network Exchange (LEVANTE) core tasks, a set of nine short and engaging tasks designed to assess learning and development in children ages 5--12 years across a wide range of languages and cultures. Using a simple and uniform multi-alternative forced-choice format, these tasks measure constructs including math, executive function, language, reasoning, and social cognition and can be administered on a tablet or computer, both in person and remotely, with all materials openly available. We describe the design and selection of these tasks, and then report on their reliability and validity in a sample of 1034 children recruited from sites in Colombia, Germany, and Canada. Tasks are scored using multi-group item response theory models, allowing testing for measurement invariance. The parameters of these models can then be used to create computer adaptive versions of the tasks, allowing the entire battery to be given in around an hour. We discuss the use, ongoing refinement, and extension of these tasks in the service of creating an open dataset to describe variability in children's development and learning across contexts.

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)0.005
metaresearch head score (Gemma)0.052
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.365
GPT teacher head0.457
Teacher spread0.092 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same topicPsychometric Methodologies and TestingFrench-language works237,207