Entrepreneurial Thinking in Secondary Education: Fostering the development of employable skills among secondary school students
Bibliographic record
Abstract
Addressing a lack of relevance and authenticity of the learning environment in the secondary education system in terms of preparing high school students for the future workforce is essential. Supporting young generations to learn how to become adaptable in a future workforce should become a greater focus point of the education system. The objective of this study is to explore the impact of entrepreneurial education and how entrepreneurial thinking activities foster the development of employable skills. The need for students to learn how to communicate their ideas effectively, adapt, problem-solve and think critically is essential. This thesis is grounded in Golden & Rodriguez’s (2018) work on the “Measuring Entrepreneurial Mindset In Youth: Learnings From the NFTE's Entrepreneurial Mindset Index”. Golden & Rodriguez’s (2018) suggest that introducing an entrepreneurial mindset into the classroom is an important focus point in many programs and initiatives that aim to prepare the young generation for the future (Gold & Rodriguez, 2018). In fact, one of the most common forms of experiential learning in secondary education in Ontario is entrepreneurial education. The Entrepreneurial Thinking program’s approach has students working on topics of their own interest. The program integrates new concepts of teaching and learning, and STEM curriculum connection through problem solving, critical thinking, and creativity to foster the development of employable skills in a collaborative and engaging environment. A multi-method research methodology was used to explore a variety of in-person and online experiences aiming to give high school students the skills they need to learn how to organize a business enterprise as well as the adoption of employable skills. This study highlights that giving students the flexibility and the opportunity to work on a project of their own interest, increases student engagement and improves learning outcomes by empowering them to choose and define the scope of their innovation project and see the tangible link between entrepreneurial and STEM education, and the success of their project. Findings further suggest that, by shifting the focus to a student-centered approach, students can better connect with their learning, creating meaningful experiences and connections.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".