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Record W3094830950 · doi:10.1177/2158244020970231

Mothers’ Educational Beliefs and Preschoolers’ English Learning Attitudes: The Mediating Role of English Experiences at Home

2020· article· en· W3094830950 on OpenAlexaff
Naya Choi, Tae-Yeon Kim, Jieun Kiaer, Jessica M. Morgan-Brown

Bibliographic record

VenueSAGE Open · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyDevelopmental psychologyAffect (linguistics)Home languageEnglish languageEarly childhood educationEarly childhoodEnglish as a foreign languagePedagogyMathematics education

Abstract

fetched live from OpenAlex

This article analyzes the relationship between Korean mothers’ beliefs about early childhood English education and preschoolers’ attitudes toward English learning. English experiences in the home were also projected to be significantly related to the aforementioned factors. Participants consisted of 159 mother–child pairs in South Korea. This study yielded three main results. First, correlations were found between the mothers’ education level and all three factors, while the fathers’ education and family income levels correlated only with preschoolers’ English experiences at home. Second, the subfactors of the mothers’ beliefs, the preschoolers’ home English experiences, and their attitudes toward learning English were revealed to be partly related. Third, the study showed that preschoolers’ English experiences at home mediated the relationship between the mothers’ beliefs in the importance of English education and the preschoolers’ attitudes. In effect, while the mothers’ beliefs about early childhood English education did not directly affect their children’s attitudes, indirect effects were found to be mediated by English experiences at home. Based on these results, we propose that it is necessary for parents to create a rich language environment in the home that engenders in children positive foreign language learning attitudes.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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 designQualitative
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

Citations6
Published2020
Admission routes1
Has abstractyes

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