Guía para implementar el Universal Instructional Design-UID (diseño instruccional universal) en la Universidad.
Bibliographic record
Abstract
Según datos estadísticos, (Peralta, 2007) durante las últimas 2 décadas, encontramos que en la universidad, existe un incremento de estudiantes matriculados que presentan algún tipo de discapacidad. Todo ello es el resultado de unas políticas obligatorias así como de una voluntad en el ámbito académico para garantizar la igualdad de oportunidades a todas las personas. En esta guía, elaborada por el Observatorio Universidad y Discapacidad (OUD), se establecen cuáles son las pautas para implementar o poner en práctica el Universal Instructional Design-UID (diseño instruccional universal) en la enseñanza universitaria a partir del análisis del modelo de la University of Guelph (Canadá). Los objetivos específicos que se persiguen son: a) delimitar el concepto teórico del UID, b) justificar la implementación en la Universidad del UID, c) analizar la implementación del UID en la University of Guelph y d) establecer las pautas para favorecer la implementación o puesta en práctica del UID en las universidades españolas.
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 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".