Dental Students and Faculty Perceptions of Teaching Methods: Traditional Classes, Online Virtual Classes, and Recorded Lectures
Bibliographic record
Abstract
Background: Rapid advancement in technology has provided alternatives to traditional classroom teaching. Such instructional methods have gained increasing importance during the COVID-19 pandemic when physical classroom attendance was not possible. The study evaluated faculty’s and students’ perceptions concerning the online virtual classes and recorded lectures as compared to traditional classes delivered at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences. Materials and Methods: Surveys were developed and distributed to 34 faculty members and 186 students. Perceptions about virtual classes, recorded lectures, physical attendance, the effectiveness of different teaching methods, and overall experience were evaluated. Descriptive statistics were presented using frequencies and percentages. The Chi-square test compared the students’ and the faculty members’ responses. The level of significance was set at α =0.05. Results: Thirty-one faculty members and 149 dental students participated, and the overall response rates were 91.2% and 80.1%, respectively. While there was a general agreement on the usefulness of making recorded lectures available, a statistically significant difference (p<0.001) was found between students’ and faculty members’ views on making classroom-lecture attendance optional (67.1% of students and 12.9% of the faculty agreed/strongly agreed). Statistically significant differences (p<0.001) were found between the students and faculty members concerning the effectiveness of recorded lectures and attending online virtual classes as an alternative to classroom attendance. Conclusion: Overall, students were more accepting of technology than faculty members as a substitute for traditional classroom teaching. For a more efficient and satisfactory learning experience, both teaching methods should be considered in a blended-learning module.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".