Interactive Computerized Learning Program Exposes Veterinary Students to Challenging International Animal-Health Problems
Bibliographic record
Abstract
This article describes a computerized case-based CD-ROM (CD) on international animal health that was developed to give veterinary students an opportunity to "virtually" work alongside veterinarians and other veterinary students as they try to solve challenging disease problems relating to tuberculosis in South African wildlife, bovine abortion in Mexico, and neurologic disease in horses in Rapa Nui, Chile. Each of the three case modules presents, in a highly interactive format, a problem or mystery that must be solved by the learner. As well as acquiring information via video clips and text about the specific health problem, learners obtain information about the different countries, animal-management practices, diagnostic methods, related disease-control issues, economic factors, and the opinions of local experts. After assimilating this information, the learner must define the problem and formulate an action plan or make a recommendation or diagnosis. The computerized program invokes three principles of adult education: active learning, learner-centered education, and experiential learning. A medium that invokes these principles is a potentially efficient learning tool and template for developing other case-based problem-solving computerized programs. The program is accessible on the World Wide Web at . A broadband Internet connection is recommended, since the modules make extensive use of embedded video and audio clips. Information on how to obtain the CD is also provided.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.014 |
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".