Impact of an E-Learning Histology Course on the Satisfaction and Performance of Medical, Nursing and Midwifery Students
Bibliographic record
Abstract
The importance and relevance of e-learning courses in medicine and health sciences has increased significantly in the last decade.Despite this, there are few published teaching experiences of e-learning histology courses in the literature worldwide.The histology course we designed was structured on the Moodle platform as a learning management system, and the content was proposed in a synchronous (zoom) and asynchronous (recordings) format.We also included the use of free virtual microscopy tools.This study aimed to investigate the impact of an e-learning histology course on the satisfaction and performance of medical, nursing and midwifery students.The sample included 424 Chilean medical, nursing, and midwifery students from two cohorts.A Likert-type survey was administered at the end of the course.We performed exploratory analysis and ordinary least squares regression.In this study, we present a positive experience of an e-learning histology course.Exploratory factor analysis revealed three main factors related to "elearning satisfaction", "in-person class activities", and "course design and teaching quality".We also found that there was a positive and significant relationship between students' perceptions of the adaptation of the traditional (face-to-face) histology course into an e-learning format and their academic performance.Our study shows that e-learning histology courses that integrate lectures and practical sessions can be a valuable teaching method for learning histology.Curriculum developers and teachers need to consider the limitations and advantages of this type of teaching and incorporate these three factors into the design and assessment of e-learning histology courses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".