Entrepreneurial knowledge and skill exposure in vocational education: development of a new assessment scale
Bibliographic record
Abstract
Appetite for entrepreneurship education (EE) among vocational students has surged dramatically, driven by persistent challenges of unemployment. As a result, vocational institutions are increasingly focused on how much entrepreneurship exposure students receive, particularly how frequently instructors impart core business knowledge and skills to meet growing demand. However, despite this focus, existing measures primarily assess the overall impact of EE by gathering self-reported data on student attitudes with specific metrics for the frequency of exposure to these vital competencies still lacking. To address this gap, the Vocational Education Entrepreneurship Knowledge and Skills (VEEKS) Scale has been developed to assess the extent of exposure that technical and vocational education and training (TVET) institutions provide to students regarding entrepreneurial competencies. This scale focuses specifically on essential business knowledge and soft skills crucial for entrepreneurial success. With a total sample size of 446, an EFA (n = 180) and CFA (n = 266) determined that business knowledge and soft skills exposure was an acceptable model for measuring VEEKS exposure. Contributing to theory, this measure shifts the focus in EE literature from impact to the exposure of key competencies—knowledge and skills. By moving beyond attitude-based assessments, the scale provides valuable practical insights for TVET institutions, supporting curriculum reform, instructor training, and strategic marketing.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".