Reflections on Experiential Learning in an Undergraduate Global Health Course
Bibliographic record
Abstract
Experiential learning has the potential to enhance students’ success and prepare them for the job market, including through class experiences that mirror experiences in the workforce. In this article, I lay out the process of incorporating experiential learning in a global health course. I have derived three key lessons from the design and implementation of this course: focus on one overarching goal, align learning activities with real world expectations, and help students understand connections between their projects and course content. These lessons provide insights to integrate experiential learning activities in the classroom. L’apprentissage expérientiel a le potentiel d’améliorer la réussite des étudiants et de les préparer pour le marché du travail, notamment en créant en classe des expériences qui ressemblent aux expériences en milieu de travail. Dans cet article, je présente le processus d’incorporation de l’apprentissage expérientiel dans un cours sur la santé mondiale. Je tire trois leçons de la conception et de la mise en œuvre de ce cours : il faut 1) se concentrer sur un objectif primordial, 2) harmoniser les activités d’apprentissage et les attentes du monde réel et 3) aider les étudiants à comprendre les rapports entre leurs projets et le contenu du cours. Ces leçons permettent de mieux comprendre comment incorporer des activités d’apprentissage expérientiel en classe.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".