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Programa de animación a la lectura digital y su incidencia en la comprensión de textos

2024· book-chapter· es· W4406094111 on OpenAlexaff
María Antonella Cornejo, Nathalí Pantigoso-Leython, Sindili Margarita Varas Rivera, Ennio Rodríguez, Mirelly Zulema Chávez Ojeda

Bibliographic record

VenueReligación Press eBooks · 2024
Typebook-chapter
Languagees
FieldSocial Sciences
TopicLiteracy and Educational Practices
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Se realizó esta investigación con la finalidad de promover el desarrollo de la comprensión lectora en niños de 4 años; a través de la aplicación de un programa de lectura digital interactiva, diseñado para fortalecer los niveles de comprensión de textos escritos, donde los estudiantes se pueden apropiar del sistema de escritura, recuperar información de diversos textos escritos e inferir el significado de los textos escritos. Para comprobar la eficacia de dicho programa, se recogió información, mediante dos instrumentos una encuesta para la variable programa de interacción a la lectura digital aplicado a las familias, y una rúbrica de observación para identificar el nivel de comprensión lectora, con siete niveles de complejidad, en el que se ubican los estudiantes. Para el análisis estadístico se utilizó la prueba no paramétrica de Wilcoxon, para describir, analizar, contrastar y comprobar las hipótesis planteadas. Arribando a la conclusión que la aplicación sistemática del programa de animación a la lectura digital fortalece significativamente los niveles de comprensión de textos escritos en los niños y niñas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.027
GPT teacher head0.344
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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