Development and Application of an Interactive Neuropathology iBook as a Complementary Learning Tool for Veterinary Medicine Students
Bibliographic record
Abstract
Neuropathology is a challenging subject for most medical students. Delivering a gamified learning tool on this topic may motivate students and increase knowledge in the discipline. We report on the development of such a tool in iBook format for the systemic pathology course delivered at Ross University School of Veterinary Medicine. Composed of 10 chapters (cases), this Neuropathology iBook (NP-iB) reviews basic clinical manifestations, neuropathology, and pathogenesis of common non-neoplastic conditions that cause brain disease in domestic animals. The goal of each chapter is to reach a diagnosis by interactively answering specific questions after reviewing relevant medical history, clinical pathology, and autopsy findings. Our hypothesis: the NP-iB improves students’ ability to recognize these diseases, reflected in higher test scores. Using a post-test-only control group design, data were gathered from all students enrolled in the course in two different semesters, fall 2017 and spring 2018 ( n = 300). NP-iB users ( n = 51, 17%) and usage time were identified by answers to multiple-choice questions embedded in the course assessments. Ninety percent of users had a positive perception although no statistically significant differences were found in median test scores between users and non-users. Statistically significant test score differences were found across how much time students used the NP-iB ( p = .005); the lowest test score median values were found for neuropathology questions not related to the NP-iB in students who used it for more than 3 hours. Unexpectedly, a low number of students preferred this digital learning tool, and its use did not improve their learning outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".