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Record W4401817219 · doi:10.1177/10538151241271134

Authentic Assessment of Executive Functions in Early Childhood: A Scoping Review

2024· review· en· W4401817219 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Early Intervention · 2024
Typereview
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPsychologyEarly childhood educationExecutive functionsEarly childhoodDevelopmental psychologyCognitionCognitive psychology

Abstract

fetched live from OpenAlex

Executive functions (EFs) are cognitive skills that begin developing in early life and are crucial for children’s overall development and daily task performance. Generally, EFs are assessed through standardized neuropsychological tests, which may not always accurately capture real-world application. To overcome this limitation, alternative methods such as authentic assessment have emerged. A scoping review was conducted to map the information available regarding the authentic assessment of EFs in children under 6 years of age from 2010 to 2021. Out of 790 documents, 32 met the eligibility criteria after full-text revision. Two rating scales emerged as the most used EFs assessment instruments. The documents did not explicitly mention the term “authentic assessment.” Four commonly assessed EFs were identified. Findings highlight the need to develop multidimensional authentic assessment instruments to assess early EFs skills in all children. This includes children at risk or with developmental disabilities, and children from families with incomes below the poverty threshold.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.871
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.453
Teacher spread0.392 · 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