Teaching Tip: Developing an Intercollegiate Twitter Forum to Improve Student Exam Study and Digital Professionalism
Bibliographic record
Abstract
#VetFinals has been developed as a novel online Twitter teaching event designed to support intercollegiate veterinary teaching using social media. Previous studies in other fields have suggested that Twitter use within universities may have benefits for undergraduate education. This "teaching tip" paper describes a project using Twitter to host online exam study sessions. The project has been a highly successful collaborative effort between the Royal Veterinary College and Nottingham Veterinary School in the UK. Over 4 years, the #VetFinals project has developed into a long-term, self-sustaining enterprise. This initiative provides a semi-structured means for student exam preparation with direct real-time input from a faculty member. It also creates a network of peers both horizontally across institutions and vertically throughout year groups. Based on similar initiatives in other disciplines, an anticipated outcome of this project was to contribute to student online professionalism. This could help address the veterinary community's recently highlighted problems with professional conduct and appropriate use of social media. Analysis of the success of this endeavor will be available in a future publication.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".