The Reality of Using E-Learning Applications in Vocational Education Courses During COVID 19 Crisis from the Vocational Education Teachers’ Perceptive in Jordan
Bibliographic record
Abstract
The present study aimed to explore the reality of using E-Learning applications in vocational education courses during COVID 19 crisis in Jordan from the perspective of vocational education teachers. It aimed to explore the way students interact with e-learning applications in this regard. It aimed to explore the challenges associated with using E-Learning applications in this regard. A sample was selected. It consists from 60 vocational education teachers. These teachers were selected from the primary public schools in Jordan. A three-part questionnaire was used. It was found that respondents have negative attitudes towards using E-Learning applications in vocational education courses during COVID 19 crisis in Jordan. It was found that the severity of the challenges associated with using E-Learning applications in this regard is high. In the light of the study’s results, several recommendations were proposed. For instance, the researchers recommend providing vocational education teachers at Jordanian schools with training courses about the way of using E-Learning applications.
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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 | 0.004 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".