Using the Digital Platform ExamSoft in Veterinary Anatomy and Parasitology Assessments in Written and Laboratory Components
Bibliographic record
Abstract
The Ohio State University College of Veterinary Medicine (CVM), with a class size of 162, is one of the largest in the nation. In an effort to streamline examination procedures, create a consistent assessment format among courses, replace paper exams, track test questions linked to learning objectives, and reduce exam grading time, our DVM program adopted the use of ExamSoft for core courses beginning in the autumn semester 2014. ExamSoft is an electronic assessment application, which provides a secure testing environment and robust reporting features. CVM uses it for high stakes midterm and finals. Although easily adopted into a didactic course format, its application in laboratory-based examinations proved challenging. Designing, setting up and grading exams for Anatomy and Parasitology courses with a laboratory component have always required substantial time investment, and adding a testing application to the process demanded rethinking and restructuring logistics. After two semesters of process refinement and standardization of a testing device to the iPad, faculty teaching in the Anatomy and Parasitology courses were able to implement ExamSoft in a laboratory setting to realize the same assessment and efficiency gains. Here we describe the benefits of ExamSoft testing in the written and laboratory settings and the lessons learned during the 2-year transition.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".