TH‐D‐350‐05: Remote Real‐Time Teaching and Learning (RRTL) in Medical Physics — An Update
Bibliographic record
Abstract
Purpose: This is an update on the Remote Real‐time Teaching and Learning (RRTL) project. The purpose of the project is to conduct real‐time lectures to Medical Physics students over the Internet. Method and Materials: Since 2002 there has been ongoing collaboration between the University of Toronto and the University of Malaya, with lecturers in Toronto conducting lectures for Medical Physics students in Malaysia over the Internet. Another project has started this year between the University of Toronto and the University of Wuhan in China. Students at the University of Wuhan view Power Point slides as they are presented by the lecturer in Toronto. They could see a live video image of the lecturer, while listening to the live audio feed. Various typing and drawing tools allow the lecturer and the students to interact directly. The lectures could also be recorded and viewed at a later date. Guest lecturers as well as students can be included from multiple sites. The software platform Microsoft LiveMeeting is used as the main tool. Results: Some initial technical difficulties had to be addressed, but the system has attained a stable condition to allow the lectures to be carried out smoothly. The software platform is already a standard feature at our institution so the cost is minimal. The project is interesting enough to attract guest lecturers who have been generous to donate their time for a worthy course. Students find the experience very comparable to a traditional classroom environment, and the ability to interact directly with experts in the field highly valuable and rewarding. Conclusion: With the advent and ubiquity of the Internet, remote real‐time teaching/learning is an extremely cost‐effective way to deliver quality education, especially to locations where the profession is developing rapidly and there is high demand for training. It should be actively promoted.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.028 |
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".