Assessing the Impact of On- the Job-Training Quality Factors on TVET Students’ Satisfaction in Developing Work Competence Skills in Oman
Bibliographic record
Abstract
The study is based on quantitative and positivists approach method of research. The descriptive and inferential methods used to analyse data and delivered in the method of explanatory notes. The research adopted Coates (2009) AQTF quality indicator and a random sampling method was selected. The research objective is to examine the impact of On-the-Job-Training (OJT) quality factors on TVET students’ satisfaction during work experience in Oman. A sample of 317 out of 400 participants from eight TVET institutes was conducted, descriptive and inferential tool used to determine the impact of OJT training quality on TVET students. The reliability test using alpha Cronbach’s and Pearson correlations test indicated an acceptable level. In addition, three measurements of Goodness of fit were considered in the study. The study is only examining TVET students from the public sector which is under responsibility of the Ministry of Manpower, Oman. The research provides a platform for practitioners and authorities to discover the OJT training factors that effect on TVET students ’satisfaction. This paper provides upon the impact of OJT training quality on TVET students' satisfaction in preparing for work competence in Oman. The work experience provides a good platform for TVET students to exercise and develop their work competences skills that would assist them to engage in the labour market. The TVET candidates are provided a work-related training to develop work competence in the country. There is a necessity to define whether the quality of OJT training has any positive impact towards TVET students or else.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".