Tips for using mobile audience response systems in medical education
Bibliographic record
Abstract
BACKGROUND: With growing evidence on the benefits of active learning, audience response systems (ARSs) have been increasingly used in conferences, business, and education. With the introduction of mobile ARS as an alternative to physical clickers, there are increasing opportunities to use this tool to improve interactivity in medical education. AIM: The aim of this study is to provide strategies on using mobile ARS in medical education by discussing steps for implementation and pitfalls to avoid. METHOD: The tips presented reflect our commentary of the literature and our experiences using mobile ARS in medical education. RESULTS: This article offers specific strategies for the preparation, implementation, and assessment of medical education teaching sessions using mobile ARS. CONCLUSION: We hope these tips will help instructors use mobile ARS as a tool to improve student interaction, teaching effectiveness, and participant enjoyment in medical education.
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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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".