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Record W2522494744 · doi:10.5539/elt.v9n10p156

Exploring the Roles of Parents and Students in EFL Literacy Learning: A Colombian Case

2016· article· en· W2522494744 on OpenAlexvenueno aff
Sergio Aldemar Hurtado Torres, Harold Castañeda-Peña

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyLiteracyFeelingEnglish as a foreign languageContext (archaeology)Exploratory researchQualitative researchPerceptionMathematics educationPedagogyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

<p>There is little scholarly information about parent involvement in their children’s English as a Foreign Language (EFL henceforth) literacy learning in the Colombian context. This exploratory-qualitative study looks into the possible roles of parents and children in EFL literacy learning at home, with special emphasis on parental roles and contributions. The study has a three-fold purpose: (1) to describe the behaviour of parents and students when doing EFL literacy tasks at home, (2) to explore feelings and thoughts (perceptions) of parents and students about working together on EFL literacy tasks at home, and (3) to identify ways in which parents contribute to a student’s EFL learning. Sixteen ninth grade students at a state school, and their parents or caregivers, carried out a series of EFL literacy tasks in their respective homes. Video recordings, field notes, qualitative interviews, and surveys were used as data collection tools. The results demonstrate that even when parents do not have a command of the English language, they have the potential to help with EFL learning from a non-linguistic point of view (e.g. monitoring children's homework, providing learning conditions, shaping children’s minds for EFL by giving advice, and feedback about EFL homework development).</p>

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)0.002
metaresearch head score (Gemma)0.001
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 score0.260
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.048
GPT teacher head0.363
Teacher spread0.315 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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