Novel Method to Manage Student Questions in the Anatomy Laboratory Using a Virtual Meeting Platform
Bibliographic record
Abstract
Large class sizes and often few instructors in anatomy courses make it challenging for student laboratory groups to have their questions addressed in a timely manner. Instructors are often unaware of the number of requests for assistance, as well as the order in which assistance is requested, and students often spend a long time waiting for an instructor to become available. As a result of brainstorming with some of our students, a call button system of sorts was suggested. Instructors in consultation with the college's IT department came up with the idea of using Zoom Meetings question and answer (Q&A) feature to manage student questions. Zoom allows one to broadcast a Zoom Meeting to up to 50,000 participant attendees, and instructors, logged in as panelists (on a mobile device, e.g., iPad), can interact with the student attendees via the Q&A feature. The students join the webinar using their dissection table number as their ID and request assistance in the Q&A. These requests show up with a time stamp and are automatically queued on the panelist's Q&A window. Instructors employ the type answer feature to acknowledge the question by typing in their respective initials, which can be seen live by the other instructors (panelists). This allows student questions to be queued so that the instructors can address them in a timely, first-in/first-out order. Student feedback regarding the use of this system for the Small Animal Anatomy course was positive.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".