Identification of Medical Students’ Learning Styles in Terms of Gender
Bibliographic record
Abstract
INTRODUCTION: Every amazing human development in the modern world is resulted from learning; therefore, teaching and learning improvement are the bases of all activities. There is a wide-range of factors affecting learning and the identification of these factors is very important in fixing problems and deficiencies in educational systems. Among the factors contributing to academic achievement are the consideration of students’ differences and identification of their learning styles. Accordingly, the present study was conducted to identify medical students’ learning styles in terms of gender. METHODOLOGY: This is a descriptive-analytical study that it conducted in 2015 on 360 students of Medical Sciences. The data collected through a two-part questionnaire consisting of questions about students’ demographic characteristics and the validated VARK questionnaire to categorize their learning styles. Using the SPSS-19 software, the collected data were analyzed through descriptive statistics, Chi-square test and ANOVA. RESULTS: The results showed that the mostly preferred learning style among medical students was the Read/Write style (48.3% of students at school of nursing, 56.7% of students at school of dentistry, 40% of students at school of medicine, 36.7% of students at school of paramedics, 53.3% of students at school of public health and 43.3% of students at school of rehabilitation). The results also showed no significant relationship between learning style and age (p=0.60), school (p=0.106) and gender (p=0.41). CONCLUSIONS: The results of this study showed that there is no significant relationship between gender and learning style of medical students. Furthermore, the mostly preferred learning style of students at Zahedan University of Medical Sciences is the Read/Write style.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".