Usos pedagógicos de las TIC según la actividad creativa del discente
Bibliographic record
Abstract
La integración de las tecnologías de la información y de la comunicación (TIC) en educación ha generado grandes esperanzas y, en algunos casos, ha generado innovaciones tecnológicas sin los fundamentos educativos necesarios. Algunos usos pedagógicos de las TIC permiten un mejor aprendizaje mediado por la tecnología (Laferrière et al., 2015) pero también existen usos de las TIC que ponen al alumno en situaciones de consumo pasivo o interactivo. Con el objetivo de integrar las TIC para mejorar el aprendizaje y analizar los límites de los usos actuales, presentamos cinco niveles de usos educativos de las TIC: (1) el consumo pasivo; (2) el consumo interactivo; (3) la creación de contenido individual; (4) la co-creación de contenido; y (5) la co-creación participativa de conocimientos orientada a la comprensión o la resolución de problemas en una comunidad de 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".