Capacitación y acompañamiento pedagógico de profesores universitarios noveles: efectos sobre el uso de estrategias de enseñanza
Bibliographic record
Abstract
Several universities offer teaching continuous development courses and support to new professors with the purpose of leading them to concentrate more on their students’ learning than on transmitting knowledge. However, have such continuous development and follow-up had any effect on the teaching strategies employed by those professors? Is there a distinction between these strategies and those used by professors who are not in continuous development projects? This text presents data collected along three years, through observation and interviews, with 22 new professors, from which 9 are in continuous development courses, 8 have completed such courses and are in a follow-up phase and 5 who have never taken part in continuous development courses. The results have not revealed noticeable difference between these professors, which leads to the conclusion that all of the them employed strategies that go beyond knowledge transmission. Keywords: Continuous development. Education follow-up. New Professors. Teaching Strategies. University.
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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 | 0.010 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".