A Quiz Becomes a Multidirectional Dialogue with Web-Based Instructional Tools for an Anatomical Pathology Rotation
Bibliographic record
Abstract
Senior veterinary students in the Iowa State University College of Veterinary Medicine (ISU CVM) participate in clinical rotations, among them a two-week necropsy rotation. The students have access to the rotation syllabus on the ISU CVM intranet site. To promote rapid comprehension of necropsy protocol, students completed a pre-exam on the syllabus. This exercise evolved from a paper quiz to an online pre-exam, using course management software to improve use of class time, increase feedback, and shift the focus to acquisition of knowledge. The students were encouraged to work collaboratively on the pre-exam and could make repeated attempts. We predicted that professional students would make multiple attempts at the pre-exam until the desired score was attained. This exercise achieves multiple goals. First, the exam encourages early review of necropsy protocol. Second, use of WebCT allows for instant, automatic, and consistent feedback from the instructor, reducing redundancy while improving the quality of communication between student and instructor and thus using faculty time more efficiently. The instructor can quickly identify and rectify common misunderstandings through this interface. Third, by allowing discussion and repeated attempts, we can ensure that there is less pressure associated with the exam. Statistical analysis of the students' performance supports the prediction that students would repeat the exam until the desired score was achieved. Subjectively, as a result of implementation of an online pre-exam, the instructor has observed students to be more engaged with the material at an earlier point in the rotation.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.082 | 0.029 |
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".