A Faculty Development Workshop for Planning and Implementing Interactive Virtual Case-Based Teaching
Bibliographic record
Abstract
Introduction: The virtual learning environment has become increasingly important due to physical distance requirements put in place during the COVID-19 pandemic. The transition to a virtual format has been challenging for case-based teaching sessions, which involve substantial audience participation. We developed a faculty development workshop aimed at teaching health professions educators how to use various interactive virtual tools within videoconferencing platforms to facilitate virtual case-based sessions. Methods: Two 90-minute workshops were piloted as a faculty development initiative. The facilitators demonstrated interactive teaching tools that could be used within virtual case-based sessions. Then, participants discussed how to incorporate these tools into case-based teaching sessions of different class sizes in small-group breakout sessions. Participants completed an online survey following each workshop to evaluate the sessions. Results: = 17) for the first and second workshops, respectively. Both groups provided overall high ratings and reported that the workshop was clear, organized, and relevant. Participants were more familiar and comfortable with the use of various interactive tools for online teaching. Discussion: Distance online teaching will be increasingly required for an undetermined time. Faculty development efforts are crucial to facilitate effective interactive teaching sessions that engage learners and maximize learning. This virtual teaching workshop is a simple and straightforward way to introduce a more interactive format to virtual case-based teaching in the health professions.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".