E-learning applications in training for repatriated workers in Vietnamese urban regions in the post-covid19 context
Bibliographic record
Abstract
This study was designed to analyze the factors influencing the utilization of an E-Learning system in training repatriated workers in Vietnam's urban regions in the new context. According to research findings, professional qualifications and education have a significant influence on the income and employment of repatriated workers in urban regions. E-Learning systems are employed as an effective channel to transfer information and skills to workers in urban areas to fulfill the improvement of professional certifications and professional abilities of workers. The analysis results also show that several factors have a significant impact on the usage of the E-Learning online training system for employees in Vietnam's urban regions, including the factor representing the ease of use of the E-learning system, Easy access to E-Learning system scale has the highest influence score with coefficient 0.932, the factor Learners feel useful, the scale of saving time getting to the study location has an influence coefficient of 0.965, and the element reflecting the joy of learning, the scale of getting more highly rated experiences have the greatest influence with a coefficient of 0.942. Data for the study were obtained from 188 repatriated laborers in urban regions of Vietnam. The multivariate regression analysis method and factor analysis were utilized to analyze the data in the study with the help of SPSS 20.0 software.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".