Creating online Histology study tools for Medical/Dental students at the University of British Columbia (UBC)
Bibliographic record
Abstract
Histology is a core preclinical program at UBC Medical and Dental School. In conjunction with lectures and laboratories, a significant portion of student learning is through online resources such as weekly practice quizzes. We aimed to create new quizzes in Term Two of the first year Histology curriculum.In order to makequizzes more representative of questions encountered in summative assessments and in clinical situations, we animated virtual slides to simulate microscopic viewing. The movable virtual slides were created by compiling snapshots of tissue sections at increasing magnifications into a "gif" file using Adobe Photoshop. To producean online version of the quiz, questions were formatted using Respondus® software and uploaded to WebCT.WebCT allows easy access to online quizzes and instant feedback for students. In May 2009, we will survey the Medical/Dental Class of 2011 and compare student satisfaction with quizzes from Term One and Term Two. This project was supported by Summer Studentships from the Department of Cellular and Physiological Sciences at UBC. Grant Funding Source AAA
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".