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El codiseño como impulso del compromiso del estudiantado universitario

2021· article· es· W4200066684 on OpenAlexaff
Anna Escofet Roig, Anna Maria Novella Càmara, Ma Victoria Morín Fraile

Bibliographic record

VenueAula Abierta · 2021
Typearticle
Languagees
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

La relación de partenariado entre estudiantes y docentes universitarios mediante prácticas de codiseño fomenta la participación activa, posibilita el compromiso académico y tiene un valor formativo clave al desarrollar una conciencia metacognitiva sobre lo que se está aprendiendo. El objetivo de la investigación ha sido analizar las representaciones y los marcos significativos de docentes que impulsaron prácticas de codiseño en una universidad pública española. Se implicó a 265 estudiantes de ocho grados universitarios y 10 docentes. Los procesos de codiseño realizados tuvieron que ver con aspectos centrados en los contenidos, la evaluación y las metodologías pedagógicas en asignaturas de distinta tipología. Los resultados permiten presentar cuatro marcos representacionales significativos que emergieron en los docentes implicados en experiencias de codiseño a partir de la técnica del grupo de discusión. Los hallazgos reafirman las cualidades del codiseño y proponen nuevas claves en relación a su concepción como proceso de construcción participativa, sumando el papel del estudiante como constructor de cambios. Además, proponen dos cualidades nuevas en su caracterización, configurando una propuesta de cinco cualidades: Respeto, Reciprocidad, Responsabilidad, Reflexión y Revisión. Estas aportaciones son clave para acompañar el desarrollo del compromiso del estudiante en la universidad y en su formación.

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)0.017
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.005
Scholarly communication0.0110.006
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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 designNot applicable
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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Citations2
Published2021
Admission routes1
Has abstractyes

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