The Extent to Which Vocational Education (VE) Teachers Are Able to Perform Practical Activities in the Vocational Education Course in Light of the Provision of Distance Education in Jordan
Bibliographic record
Abstract
This study investigated the ability of vocational education (VE) teachers to do practical activities in the VE course in the light of delivering distance education in Jordan. It explored this ability from. The descriptive analytical and quantitative approaches were adopted. The study’s sample consists from two hundred (200) VE teachers who were chosen randomly from several public schools in Amman. To meet the goals of this study, the researcher developed a questionnaire. This questionnaire consists from two parts. The first part obtains data about gender and experience (i.e. demographic data). The second part obtains data about the study’s areas (i.e. teachers, VE curricula, and grade). SPSS was used. In addition, several descriptive statistical methods were used. The researcher found that the ability of vocational education (VE) teachers to do practical activities in the VE course in the light of delivering distance education in Jordan is poor. He found that there isn’t any significant difference –at the significance level of (a=0.05) between the respondents’ attitudes which can be attributed to gender or experience. He provided several recommendations. He recommends using e-learning platforms that are more interactive when delivering online education during any crisis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".