Audience and Presenter Comparison of Live Web‐Based Lectures and Traditional Classroom Lectures During the COVID‐19 Pandemic
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to assess participants' and presenters' perceptions of a live web-based lecture series in comparison to traditional in-person lectures. MATERIALS AND METHODS: A virtual lecture series was organized by the---from March 25th until June 3rd of 2020. Twenty-five postgraduate prosthodontics programs and 81 presenters participated. Two surveys were developed and distributed to the audience (N = 330) and the presenters (N = 81). Follow-up emails were sent one week, three weeks, and four weeks after the initial email survey to encourage its completion. The data were analyzed descriptively. One-way ANOVA (p = 0.05), followed by a post hoc test, were used to compare the response percentages among the different generations of presenters and participants. RESULTS: Fifty-two percent of participants, and 65% of presenters, completed the survey. More than 96% of participants and presenters were satisfied with the lecture series. Seventy-nine percent of audience members felt that the live web-based lectures were as effective as traditional classroom lectures, or more effective; 32% of presenters agreed. Millennial audience members had significantly (p = 0.0028) more negative responses than the other generations. CONCLUSION: Participants have more positive perceptions of web-based lectures than presenters.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".