How Can Primary Teachers Become Effective Entrepreneurship Educators? Insights from the ALLFA Learning Journey Model
Bibliographic record
Abstract
Entrepreneurship education is increasingly emphasized in primary schools, yet many teachers are not adequately prepared to effectively cultivate students’ entrepreneurial skills. This study investigated primary teachers’ professional development needs for promoting entrepreneurial characteristics among students and developed a tailored learning journey model to address those needs. A mixed-method approach was employed, including a survey of 467 primary teachers in Bangkok, Thailand, and focus group interviews to gather in-depth insights. Results indicated high overall development needs, especially in designing entrepreneurship-oriented learning activities, integrating technology to enhance learning, and understanding entrepreneurial traits. No significant differences in needs were found across teacher demographics. In response to these findings, a five-stage teacher learning journey model—termed ALLFA (Awaring, Learning, Linking, Facilitating, Assessing)—was formulated to guide educators in effectively fostering entrepreneurship in the classroom. This model provides a structured framework for ongoing, flexible teacher training in entrepreneurship education. Overall, the study contributes to the field of entrepreneurship education and teacher development by identifying key competency gaps and presenting the ALLFA model as an actionable framework, with implications for teacher training policy and future research on student outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".