Development and Implementation of a Reflective Writing Assignment for Undergraduate Students in a Large Public Health Biology Course
Bibliographic record
Abstract
Reflective writing may be undervalued as purely expressive rather than a critical or an academic tool in undergraduate public health biology courses. When grounded in course concepts and academic learning, a reflective essay can be a learning tool for students that helps them use discipline knowledge and apply it to real-world issues. Studies on teaching reflection have identified its value for training students in critical thinking and improving self-regulated learning. Considering Gibbs’ Reflective Cycle framework, in this article, we detail the design, implementation, and evaluation of a reflective writing assignment integrated into a lower-year undergraduate public health biology course. Through the design and implementation of the reflective writing assignment, four key lessons are drawn. First, reflective writing assignments facilitate learning and course enjoyment. Second, writing workshops improve the quality of reflective writing assignments. Third, a detailed grading rubric clarifies expectations for students and creates consistency in grading. Fourth, reflective writing assignments can help teachers effectively evaluate how students apply the knowledge gained from the course to promote personal and community health. By implementing the reflective assignment, we have created a narrative on how reflective writing could maximize learning in public health pedagogy and provided recommendations and lessons for course designers and instructors to consider in light of Gibbs’ Reflective Cycle framework.
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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 | 0.033 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".