Herramientas para mejorar la calidad de las guías docentes y los títulos de la UPCT (2019-2020)
Bibliographic record
Abstract
Este libro documenta tres de los trabajos desarrollados en dos proyectos de innovación y mejora docente correspondientes a la convocatoria 2020 de la ETS de Ingeniería de Caminos, Canales y Puertos y de Ingeniería de Minas (EICIM) y a la convocatoria 2019-20 del Vicerrectorado de Profesorado e Innovación Docente de la Universidad Politécnica de Cartagena (UPCT). El primero de ellos se ocupa de la calidad de las guías docentes desde la perspectiva de los sellos internacionales de calidad del programa SIC de ANECA. El segundo da continuidad a proyectos anteriores: es un estudio estadístico en el que se cuantifica la influencia de distintos indicadores académicos en los resultados de las encuestas de satisfacción con la actividad docente. El tercero es una propuesta de actividades formativas, metodologías docentes y sistemas de evaluación en las memorias de verificación de títulos, con el objetivo de intentar unificar los que se emplean en la UPCT.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".