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Record W2656046703 · doi:10.5539/jel.v6n4p127

Using Learning Communities to Support Cantonese/Mandarin Family Child Care Providers in a Professional Development Intervention Program

2017· article· en· W2656046703 on OpenAlexvenueno aff
Ya‐Fen Lo, Shu-Chen Yen, Shinchieh Duh

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkMandarin ChineseIntervention (counseling)Professional developmentMedical educationFaculty developmentPsychologyCommunity collegeNursingMedicine

Abstract

fetched live from OpenAlex

High-impact educational practices can promote student involvement and learning outcomes, but are rarely tested in the community college setting—where involvement is a typical challenge to student success. For Family Child Care (FCC) providers, who tend to be older and overworked, higher-education training can be especially difficult. The present study examined the use of learning communities as a high-impact practice in Project Vista Higher Education Academy (PVHEA), a two-year professional development intervention program for Cantonese/Mandarin FCC providers at the East Los Angeles College in California. Quantitative and qualitative data during the inaugural term (January 2012-December 2013) indicated that PVHEA successfully helped FCC providers access and complete college coursework towards Child Development degrees and credentials. Course completion rate reached 100%, and the providers gradually increased course load while maintaining above-average grades. Positive changes were also observed in self-efficacy, aspirations, and professional image. The associated program challenges were discussed.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.998

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.491
Teacher spread0.412 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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