Computer‐Assisted Learning in Orthodontic Education: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
The purpose of this systematic review and meta-analysis was to compare the efficacy of computer-assisted learning (CAL) with traditional methods of learning in orthodontic education. Comprehensive electronic and manual searches of randomized controlled trials and prospective studies were conducted. Participants considered were undergraduate or postgraduate orthodontic students or orthodontic educators. The main outcome measure of CAL efficacy was knowledge gain. The time efficiency of the method was assessed based on the time spent learning the material, while its qualitative effect was tested by the attitudes of participants. Nine studies assessing CAL in teaching orthodontic diagnosis and treatment planning met the inclusion criteria. A statistically significantly higher knowledge gain favoring CAL was identified in studies that used pre- and post-intervention tests (weighted mean difference [WMD] 9.78 percent, 95 percent confidence intervals [CI] 2.89 percent, 16.67 percent; test of heterogeneity p=0.25). For studies that used only post-intervention tests, significantly greater efficacy was noted, but the effect size was smaller (WMD 3.79 percent, 95 percent CI 0.31 percent, 7.28 percent; test of heterogeneity p=0.003). Overall, student attitudes were positive towards CAL. No conclusions can be drawn about the time efficiency of CAL. Further studies are warranted to examine other important outcomes, including CAL efficacy in teaching other orthodontic topics, cost-effectiveness, and knowledge retention.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".