Stakeholders’ perceptions of occupational competency assessment and certification systems in Ethiopia’s TVET programs
Bibliographic record
Abstract
Purpose This research aims to explore stakeholder perspectives on Ethiopia’s occupational competency assessment and certification systems. Design/methodology/approach The study utilized a mixed-methods strategy within a concurrent embedded design and adopted a pragmatic perspective. Data collection involved questionnaires, interviews, and focus group discussions, selecting respondents via purposive sampling for their significant experience and deep understanding of competency assessment. Analytical methods included descriptive and inferential statistics, as well as narrative techniques. Findings There is a generally positive perception of the value of competency assessments. However, the study finds several major limitations: inadequate candidate competency assessment, lack of skill gap analysis in TVET institutions for improved training, failure to maintain assessment standards, a high candidate-to-assessor ratio, and assessment tools that do not meet occupational standards. These issues show that the existing method misjudges TVET candidates' skills. To increase employer acceptance of competency assessments, the Center of Competence (CoC) agencies should integrate industry expertise, highlight their benefits, and emphasize the importance of training quality and career goals for candidates and trainers. Practical implications A study reveals that African nations like Ethiopia, Ghana, South Africa, Rwanda, Morocco, Benin, and Senegal have been implementing competency-based training (CBT) for around two decades, with support from countries like Canada, France, Belgium, Germany, Switzerland, Australia, Luxembourg, and Japan. However, the programs are often inconsistent and disorganized, with little private sector participation. There is a significant difference between the goals of quality assurance entities and the resources allocated. Although competency assessment is a fundamental part of CBT, there is a lack of research demonstrating its practice. Therefore, we conducted this research in Ethiopia, the second most densely populated nation in Africa. The results apply to other comparable nations implementing CBT programs (IIEP-UNESCO, 2021). Originality/value The research on stakeholders' perceptions of competency assessment is still in its early stages, with most studies focusing on training quality-related issues. This study expands on our knowledge of occupational competency assessment by analyzing perspectives from a comprehensive stakeholder perspective, considering contextualized assessment practices, addressing stakeholder needs, providing practical implications, and identifying future research directions. Furthermore, it offers valuable perspectives on developing competency-based education in Africa and other regions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".