Students Perceive Similar Gains in Collaboration, Communication and Professional Skills in Two Distinct Experiential Learning Courses
Bibliographic record
Abstract
Experiential learning (EL) is a high-impact teaching practice. Despite this, it can be challenging to embed EL into educational curricula at scale due to resource constraints, such as the number of faculty members available to supervise research projects. Here we report on two distinct elective courses in a Pharmacology curriculum, both of which incorporate EL in different ways. The first course, Pharmacology and Toxicology in Society, involves community partnerships and a focus on harm reduction and drug misuse. The second course, Biomedical Incubator Capstone Project, includes student teams working as a simulated biotechnology startup. Our research questions were: (1) To what extent did students perceive gains in their skills in four domains: teamwork, career preparedness, critical thinking and problem solving, and application of theory to practice ? (2) Did student responses differ between the two EL courses? We surveyed students in both courses over three iterations to assess their perceived gains in skills across these four domains. Surveys contained both quantitative (Likert) elements and qualitative open-ended questions. We conducted mixed methods analyses of student responses. Overall student responses were positive to Likert prompts (87%-96% either agreed or strongly agreed) exploring these domains. Thematic analysis of responses to open-ended questions highlighted the transformative nature of EL experiences in both courses. Our work highlights the finding that strikingly different EL experiences can result in similar student perceptions of gains in teamwork, career preparedness, critical thinking and problem solving, and application of theory to practice. The work demonstrates the effectiveness of expanded opportunities for quality EL in Pharmacology programs and beyond.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".