Effectiveness of E‐Learning in Oral Radiology Education: A Systematic Review
Bibliographic record
Abstract
E-learning has been used recently in dental curricula to support traditional learning methods. However, the published literature concerning e-learning in oral radiology has shown mixed conclusions. The aim of this systematic review was to provide a synthesis of the effectiveness of e-learning in oral radiology education when compared with traditional classroom learning methods. A search of the literature was conducted on the LILACS, PubMed, Science Direct, Scopus, and Web of Science databases. Trials registries were also consulted for ongoing trials, and a partial grey literature search was conducted. Controlled trials about oral radiology education that compared any e-learning method with a control group using any traditional classroom instruction method were included. E-learning effectiveness was measured using three outcomes from Kirkpatrick's model of evaluation: attitudes about e-learning, knowledge gain, and performance on clinical procedures. Data were analyzed descriptively. Qualitative appraisal was performed according to the Cochrane risk of bias tool for randomized trials and MINORS tool for non-randomized trials. Eleven studies met the inclusion criteria. Risk of bias was identified related to the selection procedures, blinding, lack of sample size calculation, and incomplete analyses. Ten studies reported that students had positive attitude when using e-learning. Results from the knowledge gain outcome were mixed. Only two studies examined performance on clinical procedures, showing contrasting results. The evidence reviewed in this study suggests that e-learning in oral radiology is at least as effective as traditional learning methods and that students have positive attitudes about e-learning.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| 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".