Problem Based learning by Evaluating Students Learning Preferences Using VARK
Bibliographic record
Abstract
Introduction: Learning methodology preference is one of different components of learning fashion. Sensory learning methodology inclination is one of the various components of learning fashion which decides the person’s ability to obtain modern information. It is one of the dimensions of the complex framework of inclinations that make up a person’s learning fashion. Objective: The objective of the study is to describe the learning styles of medical students. Material & Method Study design: quantitative cross sectional Settings: Continental Medical College, Lahore Duration: Six months i.e. 1st July 2021 to 31st December 2021 Data Collection procedure: It was quantitative cross sectional study conducted on a private sector medical college. Pre validated questionnaire was used to evaluate the students learning preferences using VARK. Results: There are hundred students participating in the study in which sixty were females and forty was males. The average age of the students is around 20-24 years. Mean and standard deviation were calculated after pre and post test. Conclusion: Most students are able to memorize successfully as long as the instructor provides different learning exercises within the zones surveyed in VARK. Dynamic learning might be upgraded in huge classrooms by showing models and demonstrations, discussions, wrangles about, replying questions, and part playing. Keywords: Problem based learning, learning, Preferences, VARK
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".