Online active learning in undergraduate dental education: A scoping review
Bibliographic record
Abstract
PURPOSE: Research on online active learning (OAL) in dental education has increased in recent years; however, this literature has yet to be comprehensively summarized to document the available evidence and identify research gaps. This scoping review aimed to comprehensively map the extent and depth of the research activity on OAL in undergraduate dental education. METHODS: The review adhered to Arksey & O'Malley's multi-step framework and followed the PRISMA Extension Scoping Reviews guidelines. Searches were conducted in MEDLINE, Embase, Scopus, and ERIC databases for peer-reviewed primary research articles in English published between December 2013 and 2023. Four trained researchers independently screened titles, abstracts, and full-text articles for eligibility and extracted relevant data. All activities and information were cross-checked by the same researchers. A tested, methodologically-informed form was used for data extraction. Descriptive statistics and content analysis were used to summarize the extracted data. RESULTS: Thirty-five articles were included in the review. Most studies focused on dental students exclusively, with only two studies involving students and faculty. All studies performed outcome evaluations at reaction and/or learning levels. Problem-based learning, case-based learning, small group discussion, flipped learning, and blended learning were the most common active learning strategies employed. Dental students were satisfied with OAL and perceived it as beneficial for knowledge acquisition and skill development. Test results confirmed the improvement of knowledge through OAL. CONCLUSION: OAL has shown to improve learning outcomes in dental education; however, robust research designs are needed to further demonstrate its effectiveness in this educational context.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".