Comparison between different educational methods in orthodontic teaching for undergraduate dental students (A cross sectional study)
Bibliographic record
Abstract
: Nowadays, E learning can be used along with traditional learning methods as a result of internet networks infrastructure development and available access to the most of students. This study aimed to compare students’ responses and satisfaction regarding online, blended and traditional learning methods in relation to orthodontic knowledge and skills acquired for undergraduate students. Materials and Methods: An online survey was done among 262 students studying in Ahram Canadian University. The students were divided into 3 groups, where Group I represented the traditional method of teaching, Group II represented the online method and Group III, the blended method of teaching. A questionnaire including 14 questions was filled with students’ opinions regarding different teaching methods. Then the data were collected and analyzed. Simple descriptive statistics was used to compare questionnaire responses for the whole sample and T-independent and Mann-Whitney tests to compare the 3 groups. Results: The results showed that there was no significant difference between the 3 groups in students’ opinions regarding different teaching methods. The traditional teaching method group was satisfied with the way traditional lectures were conducted before the covid-19 pandemic. In the online teaching method group, most of the students agreed that lecture recordings during online classes were beneficial for self-study. While, in the blended teaching method group, most of the students agreed that regarding the lectures, they preferred a combination of traditional and online learning. Conclusion: The blended learning was the most preferable method for teaching orthodontics for undergraduate dental students.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".