Effectiveness of a Flipped Classroom in Learning Periodontal Diagnosis and Treatment Planning
Bibliographic record
Abstract
The aim of this study was to assess whether a flipped classroom was an effective model for dental students to learn periodontal diagnosis and treatment planning (DTP). Participants were all third-year students in three academic years (2015-17) at Harvard School of Dental Medicine: two groups that experienced the flipped classroom (Classes of 2017 and 2018), and a control group (Class of 2019) that received the same content in traditional lecture format. All three groups completed a DTP knowledge quiz before and after the educational experience; the flipped classroom groups also completed pre and post surveys of their opinions about flipped classrooms. The flipped classroom group received a 23-minute video and corresponding PowerPoint presentation to view on their own time. In class, these students were divided into groups to diagnosis and treatment plan cases and discuss them with the instructor. Of 71 students in the two flipped classroom groups, 69 pre and post quizzes were returned (response rate 97%), and 61 pre and post surveys were returned (response rate 86%). Of 35 students in the lecture group, 34 completed pre and post quizzes (response rate 97%). The mean pre scores on the knowledge quiz in the flipped classroom groups and the conventional lecture group were 64% and 54%, respectively. After the DTP education, students' quiz scores improved in all three groups, but only the difference in the flipped classroom groups was statistically significant (p<0.01). After the flipped classroom session, 84% of the students agreed or strongly agreed that this methodology was effective for learning periodontal DTP, and 90% agreed or strongly agreed they understood the fundamentals of periodontal DTP-both increases over their pre survey scores. Overall, this flipped classroom model was effective in educating students on periodontal DTP and was well received by the 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".