Lifelong Learning in Crisis: Vocational Education Management at Community Learning Centers in Cambodia During the COVID-19 Pandemic
Bibliographic record
Abstract
This study examines the management of vocational education at Community Learning Centers (CLCs) in Cambodia during the COVID-19 pandemic, focusing on policy implementation, operational challenges, and learning outcomes. Using a qualitative research approach, the study conducted in-depth interviews with administrators, trainers, and learners to assess the effectiveness of vocational training during the crisis. Findings reveal that national policies on vocational education were inadequately implemented due to insufficient funding and technical support. Three learning models—onsite, online, and on-hand training—were employed; however, they faced significant limitations, including low learner participation, lack of digital infrastructure, and inadequate hands-on training opportunities. The pandemic severely impacted learning outcomes, with graduates reporting low levels of skill development, limited career opportunities, and increased job migration. Major challenges included the absence of a structured digital education platform, poor ICT proficiency among trainers and learners, and inadequate resources for practical training. The study highlights the urgent need for policy improvements, enhanced digital infrastructure, and targeted capacity-building initiatives to ensure equitable access to vocational education. These insights offer valuable recommendations for policymakers and stakeholders seeking to strengthen vocational education management and prepare for future disruptions in Cambodia’s non-formal education sector.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".