The Use of ICT Resources For Teaching English As A Second Language To Kindergarten Learners
Bibliographic record
Abstract
Este documento tiene la intención de mostrar las ventajas y desventajas del uso de la tecnología (TIC) en la enseñanza del inglés como segundo idioma para estudiantes de jardín de infantes. El marco teórico tiene tres partes. La primera muestra una breve introducción a la educación moderna, una conexión entre educación y tecnología y, finalmente, un segundo idioma y tecnología. La segunda parte presenta ideas claras sobre la tecnología en la educación de la primera infancia. Además, las ventajas y desventajas de la tecnología (TIC) en el segundo idioma en niños pequeños. Y finalmente, la tercera parte muestra opiniones y puntos de vista sobre los padres, la educación y la tecnología, también, la influencia de la educación en los niños y, por último, la conciencia tecnológica sobre los estudiantes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".