Experiential Learning in the Classroom: The Impact of Entrepreneurial Pitches for Global Health Pedagogy
Bibliographic record
Abstract
Traditional experiential learning techniques have been incorporated into public health curricula in the past; however, research has demonstrated the need for more applied and innovative approaches to experiential learning. We introduced an entrepreneurial pitch project where students had the opportunity to design and present technological and social innovations to an external panel of judges. We then evaluated the impact of such pitches on experiential learning by conducting semistructured, face-to-face interviews with student participants. The interview transcripts were analyzed in light of Kolb’s experiential learning theoretical framework. The results of the study indicated that the process of preparing and delivering entrepreneurial pitches was rewarding for students and enhanced their learning experience. The process provided students with concrete experiences and demonstrated elements of abstract conceptualization and active experimentation. However, the results also illustrated that the entrepreneurial pitch process could be strengthened by the addition of critical self-reflection activities. Through the results of this study, we have created a narrative on how entrepreneurial pitches might foster experiential learning in global health pedagogy and provided recommendations for course designers and instructors to consider in maximizing experiential learning for students.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".