Cómo valoran y usan las TecnologÃas de la Información y la Comunicación (TIC) los profesores de alumnos con Necesidades Educativas Especiales (NEE)
Bibliographic record
Abstract
En este artculo se muestran distintos aspectos relativos al uso y valoracin que hacen los profesores de enseanza obligatoria sobre el papel de las TIC en su trabajo escolar.Se trata de un diseo cualitativo sobre diez centros de Castilla y Len, en el que se han realizado entrevistas semiestructuradas para conocer cuestiones relacionadas con el valor que conceden a las TIC; tiempos y espacios de uso; reas del currculum para las que se utilizan; tipo de tareas en que se introducen, etc.Tambin se analiza la preparacin que tienen los profesores para realizar las prcticas escolares con este tipo de recursos; as como la dotacin de estos instrumentos en los centros escolares.Los datos obtenidos muestran que la valoracin de las TIC es alta, si bien su uso es anecdtico por distintos motivos: recursos insuficientes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".