Assessing Students’ Perspectives of an Elective Digital Dentistry Course
Bibliographic record
Abstract
This brief report examines students' perspectives on teaching from a small size of fourth year dental students and Internationally Trained Dentists II candidates on an experiential learning digital dentistry elective course.A questionnaire was developed and distributed to 10 dental students before and after the course.Each question was rated on a five-point Likert scale.The Wilcoxon Signed Ranks Test was used.All data analysis was conducted by Excel at the 0.05 level of significance.The results indicated that after receiving the course the mean of students' perspectives varied more.The medians varied before and after the course.There was a 90% improvement that was detected in the students' perspectives after the course.All of the students (100%) reported an improvement in knowledge with the digital scanner after the course.Although students' perspectives demonstrated a change in almost all the participants (90%) after receiving the course, this change was not statistically significant.There was no significant difference in students' knowledge of digital dentistry before and after receiving the course.Conclusion: The dental profession is rapidly changing technologically.It seems appropriate that dental education should include digital dentistry and a suitable number of student participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".