The Challenges Facing Vocational Education Online from the Teachers’ Perspectives
Bibliographic record
Abstract
Purpose: This study investigated the obstacles facing vocational education online. The results of the questionnaire showed different views for the teachers and if there is an impact on these views according to the variables of gender and experience. Methodology: The study used the descriptive field survey method and included 132 teachers from different schools in the UAE. The study used a questionnaire to identify the obstacles to online vocational education in the UAE vocational schools from the point of view of vocational teachers. The questionnaire for vocational teachers consisted of (62) items distributed over five dimensions that measured the problems of vocational education. Findings: The findings showed that the use of online courses to teach vocational training seems difficult because of the practical aspect, which is an integral part of this kind of education. the study found equal views from both genders, as both groups were placed in the same conditions and were subject to the same laws and legislation. They apply the same study plan and face the same problems of capabilities, equipment, and financing problems. The results of the table showed that there are no statistically significant differences in the areas of problems and the total degree of problems in vocational education in vocational schools due to the years of experience variable.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".