Experiences of Vocational Education at Community Learning Centers in Cambodia during Covid-19
Bibliographic record
Abstract
This research aimed to explore the experiences of instructors and learners in vocational education at community learning centers in Cambodia during the pandemic of Covid-19. The samples of this study were instructors and learners who have been involved closely in their academic conduction. The research instrument was the in-depth interview form which was examined by five intellectual experts. The researcher employed the component analysis to analyze the data. The findings of this study revealed the following: Learning conduction of community learning centers in Cambodia during Covid-19 consists of 3 types: 1) Onsite learning management of vocational education, instructors had to limit the number of learners for each class. It was because learners had kept their distance during the educational performance. 2) Distant learning management of vocational education uses online learning platforms such as National Khmer TV, National Khmer Radio, MoEYS Learning Application, and some other social media to conduct education performance. The digital literacy and digital accessibilities were issued and faced for instructors and learners. And 3) On-hand learning management of vocational education, instructors had to find the needs and conduct learning programs for specific targets. In contrast, learners had to integrate learning programs themselves. This study offers several directions to profound implications for future vocational education studies in Cambodia. Furthermore, it may help Cambodia solving the problem of academic conduction, thereby contributing experiences of facilitating and learning to support technical and strategic vocational education for the future development in Cambodia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".