Visualization Technologies for Learning and Teaching Veterinary Acarology and Entomology
Bibliographic record
Abstract
Educational technologies are tools and resources used for improving teaching, learning, and creative inquiry. Visualization technologies (VTs) fall within this category and comprise a high diversity of strategies from simple infographics to complex forms of visual data analysis. Traditionally, parasitology has been a challenging subject in medical and veterinary degree courses due to the high number of scientific names, morphological characters, and complex life cycles, among other factors. This has been reinforced by conventional teaching methods with limited innovation strategies. Here we present the design and evaluation of an interactive album of veterinary acarology and entomology, "Álbum Interactivo de Acarología y Entomología Veterinaria" (AIAEV). This tool was assessed through three strategies: (1) a mean grade comparison between veterinary parasitology classes before and after VT implementation, (2) a system usability scale (SUS), and (3) a student/user satisfaction index. The grade value was higher in the class after implementation, the SUS total score was 80.05 (excellent), and 93.75% considered it a useful tool. This is the first study aimed at investigating the use of VTs to teach veterinary acarology and entomology and shows promising results to develop and implement digital technologies in this and other veterinary curricula disciplines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".