Upgrading and expanding online Histology quizzes for first year Medical and Dental students at the University of British Columbia (UBC)
Bibliographic record
Abstract
Histology is an integral part of the curriculum at UBC’s Medical/Dental school. Online quizzes are used by students to deepen their understanding of course material. Our objective was to revise, update and expand the existing Histology quizzes for laboratories in the Principles of Human Biology (PRIN) block of the curriculum. Specifically, we ensured the learning objectives and materials of the course were comprehensively represented in the quizzes, the questions represented exam type questions, and students were given an opportunity to integrate their learning of Histology with other material in the PRIN curriculum. Seven quizzes were chosen for revision and two cumulative review quizzes were composed. Expansion of the quizzes included: multi-part and "look-alike" questions, moveable slides, "bridging questions", and feedback in the answer sections. Quizzes were written in the Respondus® program, reviewed by project supervisors and individual Histology instructors, then uploaded onto the Faculty of Medicine website. An online survey was launched for the incoming first year Medical/Dental students in order to assess the impact of these quizzes on student learning. Results of the survey will be collected and assessed at the end of the PRIN course (January, 2011). Funding for this project was provided by The University of British Columbia Faculty of Medicine Summer Student Internship Program.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".