Learning Styles in Students of Medical Sciences
Bibliographic record
Abstract
INTRODUCTION: learning is a human’s natural tendencies; therefore, the identification of factors affecting it, is very important in fixing problems and deficiencies in educational systems. One of the factors contributing to students’ academic achievement and increased learning outcome is to identify their learning styles leading to better and more satisfying learning. The aim of the present study was to examine learning styles in students of Medical Sciences. METHODOLOGY: The present descriptive-analytical study was conducted on 417 students of Medical Sciences selected based on cluster random sampling method in the academic year 2015-2016. The data were collected through a two-part questionnaire. The first part was consisting students’ demographic characteristics. The second part was validated VARK questionnaire to categorize learning styles in the students. The collected data were analyzed through descriptive statistics, Fisher’s exact test, Chi-square test and ANOVA in SPSS version 19.0 RESULTS: The results showed the following average scores for students’ learning styles: read/write learning style (7.21±1.52), kinesthetic learning style (6.59±0.97), visual learning style (6.23±1.00), auditory learning style (6.00±0.84) and multiple learning styles (5.25±1.00). The results showed no significant relationship between students’ demographic characteristics and their learning styles (p>0.05). CONCLUSIONS: Based on the results of this study, the most preferred learning style by medical students was the read/write style. Most university teachers believe that the cause of students’ academic failure is lack of studying; however, this failure may be due to a mismatch between students’ learning styles and teachers’ teaching styles. In view of that, one of the requirements for appropriate education is to examine students’ learning styles at the beginning of each educational year and apply appropriate teaching styles accordingly.
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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 | 0.001 |
| 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) | 0.003 | 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 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".