Aprendizaje basado en problemas como estrategia didáctica en Odontología y su especialización de Ortodoncia
Bibliographic record
Abstract
Summary Teaching and learning in any profession require the use of one or several didactics that effectively lead to the formation of students and this is not foreign to dentistry and more specifically to the specialty of orthodontics. Problem-based learning (PBL) as a teaching tool is more than 40 years since its inception at the University of Mc Master and is sufficiently demonstrated its effectiveness in improving the quality of learning in students. In Colombia there is little or no documentary evidence about the application of PBL in the training of dentists and their specialties, this essay aims from the training in University Teaching and literary review open a space for the initiation of research and subsequent promotion of the use of the ABP in the study of Dentistry in our country. The methodology used in this essay is of qualitative descriptive documentary review, a description of PBL implementation experiences in different countries such as Germany, Canada, United States, England, China, Japan, Malaysia, Saudi Arabia among others. The results of this review made it possible to establish that the PBL is a didactic tool that is frequently used around the world in the curricula of dental schools of prestigious universities such as Harvard or Mac Master, it was also found that the literature focuses to a large extent on the perception of students against PBL and how motivation plays an important role in its implementation.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".