Instructional Design Variations in Internet-Based Learning for Health Professions Education: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: A recent systematic review (2008) described the effectiveness of Internet-based learning (IBL) in health professions education. A comprehensive synthesis of research investigating how to improve IBL is needed. This systematic review sought to provide such a synthesis. METHOD: The authors searched MEDLINE, CINAHL, EMBASE, Web of Science, Scopus, ERIC, TimeLit, and the University of Toronto Research and Development Resource Base for articles published from 1990 through November 2008. They included all studies quantifying the effect of IBL compared with another Internet-based or computer-assisted instructional intervention on practicing and student physicians, nurses, pharmacists, dentists, and other health professionals. Reviewers working independently and in duplicate abstracted information, coded study quality, and grouped studies according to inductively identified themes. RESULTS: From 2,705 articles, the authors identified 51 eligible studies, including 30 randomized trials. The pooled effect size (ES) for learning outcomes in 15 studies investigating high versus low interactivity was 0.27 (95% confidence interval, 0.08-0.46; P = .006). Also associated with higher learning were practice exercises (ES 0.40 [0.08-0.71; P = .01]; 10 studies), feedback (ES 0.68 [0.01-1.35; P = .047]; 2 studies), and repetition of study material (ES 0.19 [0.09-0.30; P < .001]; 2 studies). The ES was 0.26 (-0.62 to 1.13; P = .57) for three studies examining online discussion. Inconsistency was large (I(2) >or=89%) in most analyses. Meta-analyses for other themes generally yielded imprecise results. CONCLUSIONS: Interactivity, practice exercises, repetition, and feedback seem to be associated with improved learning outcomes, although inconsistency across studies tempers conclusions. Evidence for other instructional variations remains inconclusive.
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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 | 0.039 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".