Descripción:
We present a data-driven method for constructing a self-evaluating questionnaire that assesses knowledge in usability, user experience (UX), and accessibility. Traditional diagnostic tools in these domains are often long and cognitively demanding, reducing response rates and practical utility. Our approach leverages supervised machine learning methods such as Multiple Linear Regression, Random Forest, XGBoost, and Univariate Feature Selection to quantify the informational value of each question based on its predictability from others. Using this technique, we generate weighted scores that reflect a respondent’s relative expertise and enable real-time ranking among peers. Applied to 153 responses collected from graduate students and professionals, our system demonstrated that the questionnaire could be reduced by up to 82% from 62 to just 10 questions—while maintaining high accuracy in final scores and ranks. This work contributes a scalable, interpretable framework for knowledge assessment in HCI education and practice and supporting efficient evaluation.
Tipo de publicación: Journal Article
Publicado en: International Journal of Computer Science and Information Technology
Autores- Piedra, Antonio
- Díaz-Oreiro, Ignacio
- A. Brenes, Jose
- López, Gustavo
Investigadores del CITIC asociados a la publicación
Bach. Antonio Jesús Piedra Pacheco
Dr. Ignacio Díaz Oreiro
Dr. José Antonio Brenes Carranza
Dr. Gustavo López Herrera
Proyecto asociado a la publicación
Diseño y establecimiento de un servicio de evaluación de la experiencia de usuario, usabilidad y accesibilidad de software