The Effectiveness of Blended Learning for Dermatology Undergraduate Medical Students
Bibliographic record
Abstract
Background Novel internet-based applications and associated technologies have influenced all aspects of our society, ranging from areas of commerce and business to entertainment and health care. Education is no exception. In this context, this study was designed to evaluate the impact of a dermatology e-learning program on the academic performance of medical students. Objective We aimed to develop a dermatology blended learning course for undergraduate medical students and compare the knowledge gained by students who took this course to those who attended traditional classes. Methods This prospective study evaluated the performance of fourth-semester medical students from the Federal University of Bahia, Brazil. A total of 129 students were selected and divided into 2 groups. The first group (n=57) attended traditional classes and used printed material (books and handouts). The second group (n=72) took our e-learning course and used an e-book as a supplement in a hybrid setting comprising online plus traditional learning. Each course was evaluated with multiple-choice, paper-based tests that were administered at the beginning and end of the course. Results Although the precourse tests did not show any difference between the traditional and hybrid groups (mean 2.74, SD 1.25 vs mean 3.2, SD 1.22), students attending the hybrid course had better final term grades (mean 8.18, SD 1.26) than those who attended traditional classes (mean 7.11, SD 1.04). This difference was statistically significant (P<.05). Conclusions The results suggest that the performance of undergraduate students who took a course supplemented with e-learning material was superior to those who attended a traditional course alone. Conflicts of Interest None declared.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".