Association between learning style preferences and choices of teaching method among Iranian faculty members: A cross-sectional online survey
Bibliographic record
Abstract
Background & Objective: Aligning faculty members' learning styles with their teaching approaches is a complicated topic in education.Understanding these inclinations can help enhance pedagogical practices and build a more inclusive learning environment.Thus, this study examined the relationship between learning style preferences and teaching technique choices among Iranian faculty members.Material & Methods: From May to July 2022, a cross-sectional online survey was conducted among faculty members at Iranian medical universities via the Porsline website.Virtual snowball sampling was used to recruit 526 individuals.The VARK questionnaire was translated into Persian and showed excellent reliability, with a Cronbach's alpha of 0.94.The final data collection tools included the translated VARK questionnaire, self-assessment questions about teaching methods, university resources, and digital media center equipment.Analytical methods included descriptive statistics, logistic regression analysis, and chi-square testing for data evaluation.Results: The survey found that clinical teachers preferred reading and writing (54%), whereas fundamental science educators preferred visual-auditory and reading-writing.The effect of gender, field of study, learning style, age, and professional academic factors on the dependent variable, teaching method, was investigated using chi-square tests and logistic regression.The findings revealed that while there was no significant relationship between the variable gender and teaching method, significant associations were found with the variables field of study, learning style, age, and professional academic.Notably, it was observed that the effect size of field of study, age, and professional academic on teaching method is small, while for learning style, it is medium in magnitude. Conclusion:The study uncovers a significant correlation between learning styles and teaching methods, suggesting that their learning backgrounds may shape teachers' teaching methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".