Interactive Distance Learning in Orthodontic Residency Programs: Problems and Potential Solutions
Bibliographic record
Abstract
Sharing resources through distance education has been proposed as one way to deal with a lack of full-time faculty members and maintain high-quality content in orthodontic residency programs. To keep distance education for orthodontic residents cost-effective while retaining interaction, a blended approach was developed that combines observation of web-based seminars with live post-seminar discussions. To evaluate this approach, a grant from the American Association of Orthodontists (AAO) opened free access during the 2009-10 academic year to twenty-five recorded seminars in four instructional sequences to all sixty-three orthodontic programs in the United States and Canada. The only requirement was to also participate in the evaluation. Just over half (52 percent) of the U.S. programs chose to participate; the primary reason for participating was because faculty members wanted their residents to have exposure to other faculty members and ideas. The non-participating programs cited technical and logistical problems and their own ability to teach these subjects satisfactorily as reasons. Although participating distant faculty members and residents were generally pleased with the experience, problems in both educational and technical aspects were observed. Educationally, the biggest problem was lack of distant resident preparation and expectation of a lecture rather than a seminar. Technically, the logistics of scheduling distant seminars and uneven quality of the audio and video recordings were the major concerns of both residents and faculty members. Proposed solutions to these educational and technical problems are discussed.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".