Comparison of Blended Learning With Traditional Dermatology Learning for Medical Students: Prospective Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Novel internet-based applications and associated technologies have influenced all aspects of society, ranging from commerce and business to entertainment and health care, and 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 in dermatology. OBJECTIVE: The aim of this study is to develop a dermatology blended-learning course for undergraduate medical students, evaluate the knowledge gained by students exposed to this course, and compare the results to those of traditional teaching methods. METHODS: In this prospective study, we evaluated the performance of fourth-semester medical students at the Federal University of Bahia, Brazil. Students who had been in their second year of the medical course in 2019 were considered the control group, while students in their second year in 2020 were considered the blended or hybrid group. The first group attended traditional classes, using printed material (books and handouts), while the second group used our web-based course and e-book as a supplement in a hybrid web-plus-traditional fashion. Neither participants nor evaluators were blinded. The students in both groups were subjected to the same pre- and postcourse face-to-face, multiple-choice, paper-based evaluations, and we compared their performances. The content of the classes was the same for both groups. All didactic activities were developed by a team of certified dermatologists and professors from the university. RESULTS: A total of 129 students were selected and divided into 2 groups: the control group (n=57) and the hybrid group (n=72). The precourse tests did not indicate any difference between the control group (mean score 2.74, SD 1.25) and the hybrid group (mean score 3.2, SD 1.22 SD; P>.05). The hybrid group had better final-term grades (mean 8.18, SD 1.26) than the traditional group (mean 7.11, SD 1.04). This difference was statistically significant (P<.05). CONCLUSIONS: This study explores pedagogical possibilities in the field of dermatology teaching for medical school students. The results suggest that the performance of undergraduate students who attended the course with additional e-learning material was superior when compared to the performance of those who participated in the traditional course alone.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".