Vocational Instructors Preparedness for Provision of Competency-Based Training in Kenya and Apprenticeship Training in Saskatchewan, Canada
Bibliographic record
Abstract
Competency-Based Training (CBT) in Kenya and apprenticeship training in Canada provide trainees with job-ready skills, but emphasis on theory over practice hinders its effectiveness. To fittingly frame the problem, the study asked, what is the level of instructors' know-how in providing CBT in Kenya, and how does their preparation compare to journeypersons in Saskatchewan, Canada? The research involved 33 public Vocational Training Centres (VTCs) in Nakuru City County, Kenya and 23 branch institutions of Saskatchewan Polytechnic, Canada. Concurrent embedded design was utilised. The sample comprised 10 principals, 92 VTC instructors, 261 trainees, 10 industry managers and 4 programme heads drawn from a total population of 377 using stratified, purposive and simple random sampling methods. Questionnaires, interview schedules and observation checklist were used to collect data. Findings showed that Kenyan instructors are deficient in industrial training and professional development. In contrast, findings from Saskatchewan, Canada reveal greater emphasis on active and hands-on involvement, such as apprentice indentureship and mentorship under certified journeypersons. The study concluded that instructors' preparedness had major influence on the provision of competency-based skills to trainees. The study recommends an increase in funding to facilitate industrial training and refresher courses for instructors to improve quality of CBT in Kenya
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".