Educational Methodologies University of Toronto’s Dental School Shows “New Teeth”: Moving Towards Online Instruction
Bibliographic record
Abstract
Abstract: This article presents the approach the Faculty of Dentistry at the University of Toronto employed to modernize its methods of instruction by using online technologies. A small team of faculty, students, and content developers was assembled to work with individual faculty members to brainstorm and research ideas for innovative teaching practices in dental studies. The team was not content to simply post digital versions of the ubiquitous PowerPoint lectures in Blackboard, selected in 2006 by the University of Toronto as its sole platform for online course delivery, but rather set out to introduce interactivity with the course material. Consequently, a series of interactive applications was created, such as the virtual microscope in Oral Pathology, the 3D cavity preparations in Restorative Dentistry, and the Master Media Repository. During the summer of 2006, the Faculty of Dentistry made progress toward becoming one of the university’s front-runners in online course innovation. The result of this col-laboration between faculty members and the team was ten courses with interactive online presence, representing approximately 20 percent of the undergraduate curriculum. Since the summer of 2006, the Faculty of Dentistry has continued to pursue its goal of providing meaningful online instruction in all of its courses.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".