A Systematic Review on Active Learning in Dentistry Education in Undergraduate Classrooms
Bibliographic record
Abstract
Background: Former assessments of active learning in dental education have not offered a thorough research effort on this topic because of their strong focus on specific active learning strategies. We carried out a systematic review to map the breadth and depth of the literature on active learning strategies in undergraduate dental education. Methods: Following the PRISMA guidelines for systematic review, the studies between January 2005 and October 2022 were included by using the databases of MEDLINE, ERIC, EMBASE, and Scopus. Original research articles in English that underwent peer review were selected. The articles that were not in English language and unrelated were excluded. Before extracting relevant material, two seasoned researchers independently verified the eligibility of whole texts, abstracts, and titles. Risk of Bias was assessed by using the Cochrane Risk of Bias 2.0 tool for RCTs and the Newcastle-Ottawa Scale (NOS) for observational studies. Results were synthesized qualitatively. Results: The review of 93 articles assessed research using three methodologies: learning only, reaction assessment, and response and learning evaluations combined. Most studies used post-intervention evaluations, quantitative techniques, and self-report measures to assess student satisfaction and knowledge gain. Active learning approaches like group discussions, problem-based learning, team-based learning, and flipped learning were most commonly studied. Conclusion: Active learning in undergraduate dentistry classes can enhance learning, but further research is needed to assess its impact on skill development and behavioral change.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".