The Effect of Differing Audience Response System Question Types on Student Attention in the Veterinary Medical Classroom
Bibliographic record
Abstract
The purpose of this study was to evaluate the ability of specific types of multiple-choice questions delivered using an Audience Response System (ARS) to maintain student attention in a professional educational setting. Veterinary students (N=324) enrolled in the first three years of the professional curriculum were presented with four different ARS question types (knowledge base, discussion, polling, and psychological investment) and no ARS questions (control) during five lectures presented by 10 instructors in 10 core courses. Toward the end of the lecture, students were polled to determine the relative effectiveness of specific question types. Student participation was high (76.1%+/-2.0), and most students indicated that the system enhanced the lecture (64.4%). Knowledge base and discussion questions resulted in the highest student-reported attention to lecture content. Questions polling students about their experiences resulted in attention rates similar to those without use of ARS technology. Psychological investment questions, based on upcoming lecture content, detracted from student attention. Faculty preparation time for three ARS questions was shorter for knowledge base questions (22.3 min) compared with discussion and psychological investment questions (38.6 min and 34.7 min, respectively). Polling questions required less time to prepare (22.2 min) than discussion questions but were not different from other types. Faculty stated that the investment in preparation time was justified on the basis of the impact on classroom atmosphere. These findings indicate that audience response systems enhance attention and interest during lectures when used to pose questions that require application of an existing knowledge base and allow for peer interaction.
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.
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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".