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Record W4386947697 · doi:10.61468/jofdl.v26i2.527

Supporting English Language Development of English Language Learners in Virtual Kindergarten: A Parents’ Perspective

2023· article· en· W4386947697 on OpenAlexaff
Sara Shahbazi, Geri Salinitri

Bibliographic record

VenueJournal of Open Flexible and Distance Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPerspective (graphical)Thematic analysisPsychologyLanguage developmentLanguage acquisitionBreakoutComputer-mediated communicationPedagogyMathematics educationQualitative researchDevelopmental psychologyComputer scienceThe InternetSociologyWorld Wide Web

Abstract

fetched live from OpenAlex

The researchers of this case study explored English language learner (ELL) parents’ experience as they supported their children’s English language development in an online (virtual) kindergarten programme. One-on-one semi-structured interviews were used to collect data. Then the researchers used thematic analysis to describe the participants’ lived experience with the phenomenon. Findings indicated that online learning increased the emotional stressors for parents of ELL children, and altered the communication between parents and teachers. Meanwhile, the use of breakout rooms reinforced the children’s language development, and translation services supported parents. Based on the findings, the researchers recommend that schools and boards provide the parents and families of multilingual learners with ongoing workshops to give them the tools and confidence to continue supporting their children in person and online. They also recommend a greater investment in translation services.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.389
Teacher spread0.349 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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