Learning Styles of Students and Professors of a Dental School in Latin America: A Cross-Sectional Study
Bibliographic record
Abstract
PURPOSE: To recognize the learning styles of applicants, dental students, and professors with several academic degrees. Besides, it was explored the association between grade point average (GPA) of the students and their learning styles. METHODS: It was designed a cross-sectional research. The present research was attended by applicants admitted for a place in the academic period 2019-1 and by the students of the Faculty of Dentistry enrolled during the academic period 2018-2. Besides, all professors were invited to participate. The participants responded to a validated questionnaire to identify their learning style after signing the informed consent. The T-student test was executed to calculate the differences between the groups; besides, post-hoc check was run. Linear regression models were performed expressed in β and 95% confidence intervals. P values of <0.05 were contemplated as statistically significant. RESULTS: A total of 34 applicants, 425 students, and 121 professors participated in the present research. Students and professors presented preference for the theorist and reflector styles; however, the applicant students had a higher preference for the theorist (p = .002), and pragmatist styles (p < .001). A significant correlation was observed between the higher GPA of the students and the theorist style (r = .2; p < .0001). The multivariate linear regression model showed that the theorist style protects against a lower mean of GPA in students (β = -.2; p = .018) after adjusting for sex, age, public high school, and semester enrolled. Besides, professors favored the theorist and reflector styles. CONCLUSIONS: Students and professors preferred the theorist and reflector styles; however, the applicant students favored the theorist and pragmatist. Besides, the theorist style protected against a lower mean of GPA in students.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".