Using Learning Communities to Support Cantonese/Mandarin Family Child Care Providers in a Professional Development Intervention Program
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".