A Systematic Review of Computer‐Assisted Learning in Endodontics Education
Bibliographic record
Abstract
Results of the efficacy and time efficiency of computer-assisted learning (CAL) in endodontics education are mixed in the literature. The objectives of this study were to compare the efficacy and time efficiency of CAL with traditional learning methods or no instruction. The search strategy included electronic and manual searches of randomized controlled trials (RCTs) completed in English up to June 2009. The intervention comprised any method of CAL, while the control group consisted of all traditional methods of instruction including no further instructions. Various outcome measures of CAL efficacy were considered and were categorized using Kirkpatrick's four-level model of evaluation: reaction, learning, behavior, results, with the addition of return on investment as a fifth level. The time efficiency of CAL was measured by the time spent on the learning material and the number of cases covered in a unit period. Seven RCTs met the inclusion criteria. Overall, students' attitudes were varied towards CAL. Results from the knowledge gain outcome were mixed. No conclusions can be made about students' performance on clinical procedures or cost-effectiveness of CAL. Better time efficiency was achieved using CAL compared to traditional methods. CAL is as efficacious as traditional methods in improving knowledge. There is some evidence to suggest that CAL is time efficient compared to traditional methods. Overall, the number of studies included in this review was small, thus warranting the need for more studies in this area and the exploration of various CAL techniques.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".