Revisión sistemática de los métodos de evaluación de experiencia de usuario de sitios web informativos
Bibliographic record
Abstract
El presente trabajo de tesis consiste en una revisión sistemática, presentada como artículo científico, sobre los métodos de evaluación que son empleados actualmente para la evaluación de la experiencia de usuario en sitios Web informativos. El trabajo de investigación consiste en una revisión de la literatura para identificar los métodos, criterios y herramientas empleadas para evaluar la experiencia de usuario en sitios web de acuerdo a la definición planteada para ambos términos en la ISO 9241. Las investigaciones consideradas para la revisión fueron encuestas, estudios de casos, estudios comparativos y experimentos que incluyan la descripción de la metodología aplicada. El artículo fue publicado en Springer como parte de la participación en el evento "HCI International 2017", realizado en Vancouver (Canadá) en el 2017.
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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 | 0.110 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".