Creation and validation of the LEVANTE core tasks: Internationalized measures of learning and development for children ages 5-12 years
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".