Designing an International Large-Scale Assessment of Professional Competencies and Employability Skills: Emerging Avenues and Challenges of OECD’s PISA-VET
Bibliographic record
Abstract
Abstract Globally, vocational education and training (VET) is considered important for ensuring the supply of skilled labour to the economy and economic competitiveness but also for helping the next generation with the transition to working life and integration into society. However, despite this importance, there are no international comparative studies on the effectiveness of the very different VET systems. In March 2024, the Organisation for Economic Co-operation and Development (OECD) published the ‘Analytical and Assessment Framework’ for PISA-VET, an international study on professional competencies and employability skills in VET. In this paper, some of the lead experts that contributed to the framework provide an outline of the aims of the initiative, the target groups, the assessment approaches as well as strength and weaknesses to stimulate discussion in the scientific community. VISA-VET aims to deliver comprehensive data, inform decision making, facilitate peer learning between countries, and promote the image of VET, in general. Target populations are learners toward the end of their VET programmes in the occupational areas of automotive technicians, electricians, business and administration, health care, or tourism and hospitality. Assessment approaches to domain-specific professional skills are simulation-based questions, digital simulations, and live or recorded demonstrations. The professional skills assessments are expanded by the assessment of employability skills and comprehensive data collections on national contextual and system-level factors. This paper discusses the selection and breakdown of occupational areas, the various assessment approaches and possible supplementary studies. Its overall aim is to initiate a broader discussion in the scientific community about the design of and expected insights from PISA-VET.
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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 | 0.061 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".