El proceso de enseñanza-aprendizaje en contextos ubicuos y universitarios.Tres estudios de casos
Bibliographic record
Abstract
En este trabajo se pretende identificar los componentes del proceso de enseñanza-aprendizaje de lenguas extranjeras que mejor se adapten a diferentes entornos educativos- ya sea presencial u online- con el objetivo de observar cómo la optimización de diferentes estrategias metodológicas puede resultar en la optimización del aprendizaje. Para este propósito, identificamos la construcción de ambientes de aprendizaje que se han llevado a cabo, por un lado, en un entorno on line (Universidade Alberta) y, por otro, en uno presencial enriquecido digitalmente (Universidade Catolica Portuguesa). De esta manera, proponemos un diseño metodológico basado en la adaptabilidad tanto de los componentes cognitivos de los alumnos como del contexto en el que se produce el aprendizaje. Se apuesta a la diversificación no sólo de contenidos, sino también de las diferentes maneras de entender el aprendizaje.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".