Learning styles of medical students change along the study program: from ‘thinking and watching’ to ‘thinking and doing’
Bibliographic record
Abstract
Background: Most students admitted to medical school are abstract-passive learners. However, as they progress through the program, active learning and concrete interpersonal interactions become crucial for the acquisition of professional competencies. The purpose of this study was to determine if and how medical students' learning styles change during the course of their undergraduate program. Methods: All students admitted to the Pontificia Universidad Católica de Chile (PUC) medical school between 2000 and 2011 (n = 1,290) took the Kolb’s Learning Style Inventory at school entrance. Two years later 627 students took it again, and in the seventh and last year of the program 104 students took it for a third time. The distribution of styles at years 1, 3 and 7, and the mobility of students between styles were analyzed with Bayesian models. Results: Most freshmen (54%) were classified as assimilators (abstract-passive learners); convergers (abstract-active) followed with 26%, whereas divergers (concrete-passive) and accommodators (concrete-active) accounted for 11% and 9%, respectively. By year 3, the styles' distribution remained unchanged but in year 7 convergers outnumbered assimilators (49% vs. 33%). In general, there were no gender-related differences. Discussion: Medical students change their preferred way of learning: they evolve from an abstract-reflexive style to an abstract-active one. This change might represent an adaptation to the curriculum, which evolves from a lecture-based teacher-centered to a problem-based student–centered model.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".