Use of Top Hat Audience Response Software in a Third-Year Veterinary Medicine and Surgery Course
Bibliographic record
Abstract
Audience response devices are useful tools that can improve student engagement and learning during instructional sessions. The purpose of this article is to describe our experience with a new cloud-based application known as Top Hat, which includes audience response tools in its application suite. The software was used in a multi-specialty, multi-instructor medicine and surgery course in the third year of a veterinary curriculum. In addition to standard multiple-choice and short-answer questions, Top Hat has several unique question types and methods of displaying the responses given. These include displaying free-text responses in a word cloud format and a "click-on-target" question type that allows students to indicate their response by clicking on a location within an image. Responses for this latter question type are displayed in a heat map format. A discussion tool is also available, which allows students to respond, read other students' responses in real time, and then reply again if warranted. This feature also supports drawing-based responses. The variety of question types was very useful in keeping students engaged during teaching sessions, giving this application several advantages over systems that are limited to multiple-choice questions only. In addition, the application allowed rapid identification of areas of student knowledge and misunderstandings, which facilitated the direction of further discussion and clarification of important learning issues.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".