The Effectiveness of Computer‐Aided, Self‐Instructional Programs in Dental Education: A Systematic Review of the Literature
Bibliographic record
Abstract
Computer-aided learning (CAL), self-instructional programs provide an accessible, interactive, and flexible way of presenting curriculum material. In order to assess the effectiveness of CAL programs in dental education, a systematic review of the published literature comparing CAL with other teaching methods was performed. A systematic search of the published literature was performed. Articles formally assessed for inclusion had to meet the following criteria: randomized controlled trials comparing CAL with any other method of instruction, and the use of academically homogeneous dental students or dental professionals with objective, predefined outcome criteria measuring performance, time spent, and attitudes. The searches located a total of 1,042 articles; of these, only twenty-seven articles met the inclusion criteria. Further quality assessment identified twelve studies that were included in the final review. Five of the studies documented statistically significant differences in outcome measures (scores on multiple choice, written or oral tests, and clinical performance) favoring CAL over comparison group(s), while six revealed no statistically significant differences. One study documented a greater improvement in test scores in the seminar group over the CAL group. Participants' attitudes towards CAL in the included studies are also discussed. Our study concluded that CAL is as effective as other methods of teaching and can be used as an adjunct to traditional education or as a means of self-instruction.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".