Scale to Measure Medical, Nursing and Midwifery Students’ Engagement in an E-learning Histology Course
Bibliographic record
Abstract
E-learning courses become increasingly important and relevant in medicine and health sciences over the last decade.However, there are few teaching experiences of e-learning histology courses published in the literature worldwide.Moreover, most of these studies focus on the didactic aspects of the course without exploring student participation.The study presented below aimed to validate a scale to measure student participation in an e-learning histology course.We provide evidence of validity of the instrument based on its internal structure for use with medical, nursing, and midwifery students.The participants in this study were a group of 426 Chilean medical, nursing and midwifery students from a public university who completed the questionnaire in two consecutive semesters (2020)(2021).Data from the first group of students were used to perform an exploratory factor analysis (EFA), while data from the second group of participants were used to perform a confirmatory factor analysis (CFA).The three factors identified according to the CFA were: "Habits of online," "Motivation for online learning," and "Interaction of online".After eliminating one of the initial items of the instrument, the scale showed acceptable psychometric properties suggesting that it is a useful instrument to measure students' perception of their participation in e-learning histology courses.The factors identified through the validation of the instrument provide relevant information for teachers and curriculum developers to create and implement different ways of encouraging student participation in elearning histology courses to support online learning.
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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 | 0.004 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".