Exposure to entrepreneurship education interventions reveal improvements to vocational entrepreneurial intent: a two-wave longitudinal study
Bibliographic record
Abstract
Abstract The education sector has witnessed a growing recognition of the interdisciplinary nature of entrepreneurship education (EE), which has expanded beyond its traditional focus on business students. While higher education institutions have gained significant prominence in EE, little is known about the effects of EE in technical vocational education and training (TVET) institutions. This study aimed to examine the impact of EE on the entrepreneurial intentions (EI), attitudes towards business (ATB) creation, subjective norms (SN), and perceived behavioral control (PBC) of vocational students, grounded in the theory of planned behavior. Adopting a Two-Wave Longitudinal design, the study was conducted on a sample of 128 TVET students who underwent an intensive six-month EE program. The results showed that the Time 2 scores were significantly higher than the Time 1 scores for EI (9.510), ATB (9.773), SN (8.588), and PBC (2.993), indicating the effectiveness of EE in fostering pro-entrepreneurial desires among vocational students. The findings suggest that TVET institutions should consider incorporating EE into their curricula and provide adequate support systems for their student population. This study contributes to the limited research on the impact of EE in the TVET context, particularly within emerging economies, and offers insights for educational practice and future research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".