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La mejora del rendimiento escolar y el clima social mediante un programa de intervención basado en el aprendizaje-servicio y mentoría entre iguales

2018· article· es· W2907748698 on OpenAlexaff
Mirian Hervás Torres, Francisco D. Fernández Martín, José Luís Arco Tirado, María Isabel Miñaca Laprida

Bibliographic record

VenueREOP - Revista Española de Orientación y Psicopedagogía · 2018
Typearticle
Languagees
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsHumanitiesSchool dropoutPsychologyCartographyArtGeographySociology

Abstract

fetched live from OpenAlex

RESUMEN Los datos actuales, sobre fracaso escolar en educación son alarmantes, con índices por encima de la media europea Por ello, las universidades han de reaccionar con propuestas de mejora, fiables, válidas y adaptadas, tales como, el establecimiento de metodologías alternativas docentes, que ayudan al incremento de la adquisición de aprendizajes y competencias, las cuales reducen conductas de riesgo, promocionando un entorno y clima escolar adecuado. Atendiendo a esta situación, el objetivo de este estudio fue determinar la eficacia del Programa Huelva Educa, experiencia basada en el aprendizaje-servicio y la mentoría entre iguales, para incrementar el rendimiento escolar y mejorar el clima social de alumnado de educación obligatoria. La muestra estuvo compuesta por 69 alumnos de educación obligatoria y 14 docentes-tutores. En esta investigación, se empleó un diseño cuasiexperimental pretest-postest con un solo grupo, donde la intervención consistió en una serie de sesiones de mentoría entre alumnado universitario, previamente entrenado, y el alumnado de educación obligatoria, en horario extraescolar durante el curso escolar. Los resultados arrojan mejoras en el rendimiento escolar del alumnado de educación obligatoria, y mejoras en la cantidad y calidad de las interacciones en el aula, tanto desde la perspectiva del alumnado como de sus docentes-tutores, contribuyendo a la prevención del fracaso y abandono temprano.ABSTRACT The current data about academic failure and dropout in education are alarming, with scores above average the European average. In this sense, the universities are to react with proposals, reliable, valid and adapted, such as, the establishment of teaching alternative methodologies, to increase the acquisition of learning and skills, reduces risk behaviors, and it promotes an environment and suitable school climate. Considering that situation, the aim of this study is to determine the efficacy of the Huelva Educa Program, an experience based on service-learning and peer mentoring, in order to increase school performance and improve the social climate in compulsory education students. The sample was composed of 69 students of compulsory education and 14 educational tutors. Likewise, in this investigation a quasi-experimental pretest-posttest design with a single group was used, where the intervention, which consisted of different sessions of mentoring among university students, previously trained, and the pupils in compulsory education, in out of school hours during the scholastic year. The results show improvements in school performance of the pupils in compulsory education, and improvements in the quantity and quality of interactions in the classroom, both from the perspective of students and of the educational-tutors, contributing to the failure and early dropout prevention.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.031
GPT teacher head0.396
Teacher spread0.364 · 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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Citations4
Published2018
Admission routes1
Has abstractyes

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