Tablet Computers in the Veterinary Curriculum
Bibliographic record
Abstract
Tablet computers offer a new method of information management in veterinary medical education. With the tablet computer, students can annotate class notes using electronic ink, search for keywords, and convert handwriting to text as needed. Additional electronic learning resources, such as medical dictionaries and electronic textbooks, can be readily available. Eleven first-year veterinary students purchased tablet computers and participated in an investigation of their working methods and perceptions of the tablet computer as an educational tool. Most students found the technology useful. The small size and portability of the tablet allowed easy transport and use in a variety of environments. Most students adapted to electronic notetaking by the second week of classes; negative experiences with the tablet centered on a failure to become comfortable with taking notes and navigating on the computer as opposed to writing and searching on paper. A few performance-related problems, including short battery life, were reported. Tablet software allowed conversion of faculty course notes from a variety of original formats, meaning that instructors could maintain their original methods of note preparation. Adopting a consistent naming convention for files helped students to locate the files on their computers, and smaller file sizes helped with computer performance. Collaboration between students was fostered by tablet use, which offers possibilities for future development of collaborative learning environments.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".