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Record W2921732999 · doi:10.1177/0022466919832371

Teaching Children With Fetal Alcohol Spectrum Disorder to Use Metacognitive Strategies

2019· article· en· W2921732999 on OpenAlexaff
Marnie L. Makela, Jacqueline Pei, Kimberly A. Kerns, Jennifer MacSween, Aamena Kapasi, Carmen Rasmussen

Bibliographic record

VenueThe Journal of Special Education · 2019
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of VictoriaUniversity of Alberta
Fundersnot available
KeywordsMetacognitionFetal Alcohol Spectrum DisorderIntervention (counseling)PsychologyCognitionFetal alcoholDevelopmental psychologyPrenatal alcohol exposureFetal alcohol syndromeCognitive strategyCognitive InterventionClinical psychologyAlcoholPsychiatryPregnancy

Abstract

fetched live from OpenAlex

Metacognitive training is an emerging cognitive intervention for children with fetal alcohol spectrum disorder (FASD) that teaches children to think about their thinking and use strategies to improve learning and regulation. We investigated how children with FASD acquired metacognitive strategies during a computerized intervention delivered in a school setting. The number, type, and process of strategy acquisition were recorded for seven children with FASD during the intervention. As an indication of strategy learning, we recorded prompted and spontaneous strategy use over time. Children with FASD were found to use a total of 26 different metacognitive strategies, with eight strategies used spontaneously by all participants. Participants demonstrated a significant decrease in the number of different prompted strategies and a significant increase in the number of spontaneous strategies used over the course of the intervention. Implications for the use of a metacognitive approach for students with FASD are discussed, emphasizing the value of a strength-based approach.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.310

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 communication0.0000.001
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 head0.009
GPT teacher head0.278
Teacher spread0.269 · 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.

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

Citations16
Published2019
Admission routes1
Has abstractyes

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