El m-learning en la didáctica del patrimonio: desarrollo de una App sobre el palacio de la Aljafería (Zaragoza) para Educación Primaria.
Bibliographic record
Abstract
Ante la aparición en nuestra Sociedad de las nuevas tecnologías, surge la necesidad de incorporarlas al ámbito de la educación. Con este fin, hemos desarrollado este trabajo fin de grado, dirigido a alumnos de quinto de primaria y con la posibilidad de extenderlo a otros cursos. Comenzamos este TFG presentando el marco teórico actual de las nuevas tecnologías y su influencia en las ciencias sociales, seguido de una revisión histórica, que fundamenta nuestro trabajo. El trabajo principal consiste en la creación de una aplicación para Smartphones y Tablets, cuyo objetivo es ser implantada en las visitas culturales al monumento de la Aljafería, haciendo hincapié en su historia y algunos de sus detalles arquitectónicos más relevantes. Esta aplicación será facilitada al comienzo de la visita al monumento y tiene como objetivo conseguir un aprendizaje sencillo, cómodo y divertido.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".