El e-Portfolio de Competencias en los Procesos de Acompañamiento de Enseñanza y Aprendizaje Escolar
Bibliographic record
Abstract
En el marco de la formación inicial para la docencia, se implementa el ePortafolio de competencias como dispositivo para apoyar el desarrollo profesional de los estudiantes en prácticas, que realizan una escritura reflexiva con acompañamiento. Nuestro artículo presenta dos estudios: uno desarrollado en la Universidad de Lleida, España y el otro en la Universidad de Sherbrooke, Quebec, Canadá, con estudiantes en prácticas de 2º y 4º curso, respectivamente, que editan sus ePortfolios de competencias profesionales. Se explican las características de las plataformas y del dispositivo. Seguidamente, se presenta la metodología y los resultados del estudio. Sobre estos, subrayaremos la importancia del acompañamiento y la orientación, dos aspectos esperados por los practicantes, el compromiso en la escritura reflexiva y el desarrollo profesional próximo.Palabras clave: Formación, ePortfolio de competencias, acompañamiento, escritura reflexiva, desarrollo profesional. In teacher education programs, the resource of ePortfolio of competencies is used in order to support the professional development of student teachers with the help of reflexive writing. This article presents two studies, one realized at Lleida University with 2nd year student teachers and the other one at Sherbrooke University with 4th year student teachers who have produced their own ePortfolio of professional competencies. The attributes of the digital platforms are explained. The methodology and some results of the studies are reflected in a constructed dialogue. Among the results obtained are underlined, the importance of scaffolding advice wished by student teachers, investment in reflexive writing and proximal professional development.Keywords: Formation, ePortfolio of competencies, Scaffolding advice, Reflexive writing, Professional development.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".