The Preferred Learning Styles Among the Undergraduate Dental Students at Foundation University College of Dentistry, Islamabad
Bibliographic record
Abstract
Objective: To determine the preferred learning styles of undergraduate dental students at the Foundation University College of Dentistry, Islamabad. Methodology: A cross sectional study was conducted at Foundation University College of Dentistry, Islamabad in January 2020 over a period of 2 weeks. On the basis of VARK questionnaire, learning styles of first to final year undergraduate dental students were analysed. The questionnaire consisted of 16 items and identified four different learning styles: visual, aural, reading/writing and kinaesthetic. A total of 132 students were included in this study. The filled out questionnaires were scored according to the VARK tool and then statistically analysed to determine the distribution of different learning styles among students. Results: A total of 126 students out of 132 responded with the response rate of 95.5%. The results showed that the frequency of a single learning style (unimodal) in the study population was 63.5% while the frequency of a combination of different learning styles (multimodal) was 36.5%. The frequencies of bimodal, trimodal and quadmodal learning styles were 26.1%, 6.9% and 3.5% respectively. Among the unimodal learning style kinaesthetic topped the list with a frequency of 27.9% while among the bimodal learning styles auro-kinaesthetic was more frequent accounting for 10.9%. Conclusion: The predominant learning style in our study population was unimodal and had a frequency of 63.5% with kinaesthetic being the chief preference making a major chunk of 27.9% followed by aural learners making up to 20.4%. In bimodal learning styles the most frequent in our study population was auro-kinaesthetic with a frequency of 10.9%.
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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 | 0.002 |
| 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 | 0.000 |
| 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".