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Record W2917121599 · doi:10.5287/ora-zroor9mzx

Executive function, social-emotional skills, and academic competence in three preschool programmes: pathways to school readiness

2018· dissertation· en· W2917121599 on OpenAlexaboutno aff
Hanako Shimamura

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsnot available
FundersInstitute of Education, University of LondonDepartment for Children, Schools and FamiliesStrong
KeywordsCompetence (human resources)Social competencePsychologySocial skillsDevelopmental psychologySocial emotional learningPositive Youth DevelopmentSocial changeMedical educationMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Research findings indicate that executive function (EF), social-emotional skills, and pre-academic competence significantly promote children’s school readiness and later success. School readiness broadly refers to a combination of skills necessary to function successfully in school and lack thereof may increase the risk of children’s school problems. Therefore, it is essential for school systems to provide appropriate and timely support to the development of these fundamental skills. The present study focused on three particular preschool programmes: Montessori, Reggio Emilia, and the traditional play-based (British Columbia Early Learning Framework: BCEFL) programmes in Western Canada. Although they are popular, there is little empirical research that examines and compares the benefits of these programmes to the development of school readiness skills. As such, the present study aimed to 1) determine the effectiveness of these three preschool programmes in Western Canada on the development of children’s school readiness; and 2) examine other sources of influences in the child, family and school in relation to the development of school readiness skills. Overall, 119 preschool children (48 Montessori, 42 Reggio Emilia, 29 BCELF) participated in the study. Observation was conducted once in the autumn of 2015 for each classroom using the CLASS observation tool. Teachers and parents of participating children filled in a series of questionnaires regarding the quality of their relationship with their child and their perceptions of daily EF and social-emotional skills of their child. The researcher also assessed individual children’s fluid intelligence, EF, and pre-academic competence. The results showed that 1) although Montessori education appeared to be the most effective in facilitating numeracy skills, no curriculum stood out as notably more effective than any of the others at improving other areas of school readiness skills; 2) well-run classrooms where teachers were effective in time, behavioural, and attention management were most effective in promoting children’s numeracy skills; 3) EF, social-emotional skills, and pre-academic competence exhibited an overlapping developmental process over time; 4) relational quality in both home and school environments significantly affected the development of school readiness skills, especially social-emotional skills; and 5) adults’ perceptions of children’s EF and social-emotional skills had a significant consequence for how teachers and parents formed their relationships with their children.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.771
Threshold uncertainty score0.462

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.359
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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