Online Lectures in Undergraduate Medical Education: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The adoption of the flipped classroom in undergraduate medical education calls on students to learn from various self-paced tools-including online lectures-before attending in-class sessions. Hence, the design of online lectures merits special attention, given that applying multimedia design principles has been shown to enhance learning outcomes. OBJECTIVE: The aim of this study was to understand how online lectures have been integrated into medical school curricula, and whether published literature employs well-accepted principles of multimedia design. METHODS: This scoping review followed the methodology outlined by Arksey and O'Malley (2005). Databases, including MEDLINE, PsycINFO, Education Source, FRANCIS, ERIC, and ProQuest, were searched to find articles from 2006 to 2016 related to online lecture use in undergraduate medical education. RESULTS: In total, 45 articles met our inclusion criteria. Online lectures were used in preclinical and clinical years, covering basic sciences, clinical medicine, and clinical skills. The use of multimedia design principles was seldom reported. Almost all studies described high student satisfaction and improvement on knowledge tests following online lecture use. CONCLUSIONS: Integration of online lectures into undergraduate medical education is well-received by students and appears to improve learning outcomes. Future studies should apply established multimedia design principles to the development of online lectures to maximize their educational potential.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".