Rúbrica de evaluación en la satisfacción del estudiante en una universidad peruana
Bibliographic record
Abstract
El estudio tuvo como propósito determinar el impacto del uso de las rúbricas de evaluación para lograr medir niveles de satisfacción de estudiantes universitarios en el Perú. El método pertenece a la ruta cuantitativa, de nivel correlacional causal, no experimental, método hipotético deductivo, el grupo de estudio estuvo representado por 83 estudiantes universitarios. Los cuestionarios de recolección de información se delinearon para cada variable empleando la escala Likert, se realizó por medio de la técnica de la encuesta virtual a través del Google forms. Los resultados obtenidos demostraron un valor de Wald de 9,541 alejado del 0 lo que señala que existe efecto entre las variables con un p valor = 0.002 mínimo a 0.05 el resultado se objeta a la suposición nula y se admite la suposición alterna. Se concluye que la aplicación de las rúbricas de evaluación impacta significativamente en la complacencia de los universitarios como agentes del servicio.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".