Tips for using mobile audience response systems in medical education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Michael Gousseau, Connor Sommerfeld, Adrian Gooi Department of Otolaryngology – Head and Neck Surgery, University of Manitoba, Winnipeg, Canada 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. Keywords: mobile audience response systems, active learning, medical education, ARS
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it