Debating: effective and satisfactory learning method in dentistry
Bibliographic record
Abstract
BACKGROUND: Education in the modern world of health needs diverse methods of learning and teaching. The traditional education model has limited capacity for developing abilities such as critical thinking, problem-solving, and reasoning skills. Therefore, improving the quality of teaching-learning processes requires implementing educational innovations in the classroom and evaluating them. This study aimed to determine the impact of the debate teaching method on improving the abilities of general dentistry doctoral students. METHODS: The research was a semi-experimental study with pre-tests and post-tests to measure the knowledge and abilities of students. The study included 60 dental students who completed the fall 2022 session of the Community Oral Health (COH) 2 practical course. This course, one of three practical components within the Community Oral Health curriculum, aligns with the educational framework of general dentistry. Challenging topics on which there is no consensus in dentistry were chosen for the debate. The descriptive statistics indicators include an independent t-test and variance analysis test with a significance level of 5%. Were used to analyze the data. RESULTS: The results of the study showed that the average total knowledge (P < 0.001), 'perception of critical thinking skills (P < 0.001), expression power (P < 0.001), reasoning skills (P = 0.003), interpretation and Information analysis power (P < 0.001), the ability to find and use scientific databases (P < 0.001) and the ability to analyze and evaluate evidence (P < 0.001) increased significantly after intervention in students. 95% of students agreed/strongly agreed that this method enhances their ability to answer people's questions. From an instructor's point of view, students had 93.1% of the ability to reason and analyze information after intervention and 88.5% of the ability to think critically. CONCLUSION: The results of the study showed that the use of debate in the classroom is an effective way to present content. The process of evaluating data-driven arguments promotes higher-level cognitive skills and teaches students about the knowledge base and the use of scientific databases. TRIAL REGISTRATION: Registration date: 21/11/2022, Registration number: IRCT20141128020129N3.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".