Aprendizaje basado en problemas (ABP) : una innovación didáctica para la enseñanza universitaria
Bibliographic record
Abstract
The conceptual and methodological elements as well as the judgements contained in this paper are the result mainly of a ten year work experience with a didactic-curricular approach of PBL in the faculties of the area of health at the University of Antioquia. Reference to the concrete case of this university has been kept to a minimum, in order to search for the general aspects of the method, regarding their historical as well as methodological and operational range, and also their applicability in areas different than the area of health. At the beginning of the paper, some historical background of the method is presented, from its origins at the University of McMaster in Canada in the 70’s to their trials at several Latin American and Colombian universities. After that, PBL is given a place within the strategy of learning-by-discovery and construction, an active pedagogy widely applicable in present-day education. Next, the central, active role of “the problem” in the PBL methodology is discussed. Subsequently, the method’s syntax or organizational sequence is presented, and four proposals on PBL, which operationalize the very basic structure of the method, are brought to matter. Fifth, the operational scheme is described, Then, the characteristics of a good tutor or conductor of the method and the academic-administrative obstacles faced by this innovative methodology at our universities and mentioned. To conclude, some findings of the research on PBL are mentioned.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".