Effectiveness and Perceptions of Flipped Learning Model in Dental Education: A Systematic Review
Bibliographic record
Abstract
When educators adopt flipped learning in their courses, online sources are assigned for students to study prior to class, and then the class period is devoted to face-to-face (F2F) interactions. The aims of this systematic review were to evaluate published research on the effectiveness of flipped learning for dental students' learning and on dental students' perceptions of the model and to report the results based on the first two phases of Kirkpatrick's model: reaction and learning. A systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) was performed. Articles in which the objective was to determine the effectiveness of students' learning or students' perceptions of flipped learning in both dental and advanced dental education were collected. The Risk of Bias of the included studies was assessed using the MINORS Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Summary of Findings table. The authors screened the title and abstract of 650 studies; after application of inclusion criteria, eight articles remained for analysis. In those studies, a total of 572 dental students were participants. The effectiveness of flipped learning and conventional lectures was compared in five of the eight studies; three of the studies compared students' perceptions of flipped learning and the conventional format; and four of the studies assessed students' perceptions of flipped learning without comparison to another methodology. The findings suggest that flipped learning was an effective way to deliver knowledge in these eight studies. Time flexibility was a particular asset found in this review since flipped learning allowed each student to assimilate the educational material at her or his own pace.
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 | 0.031 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".