Bach. Antonio Jesús Piedra Pacheco

Bach. Antonio Jesús Piedra Pacheco

Es estudiante: 
No
Programa en que estudia: 

Proyectos

Publicaciones

Evaluation of Usability, User Experience and Accessibility of Applications: a Tertiary Review

Descripción:

In the present time, there is a growing need to understand how users interact with digital applications to ensure the success of new products and a good experience for the users. Three main concepts are central to this subject: usability, user experience, and accessibility. As such research efforts have been undertaken to create evaluation instruments for each and apply them to multiple tools. This study aims to perform a compilation of the research gathered in multiple secondary studies on the subject of evaluating these concepts.A systematic search process was performed to answer research questions related to how evaluations in the field of usability, user experience, and accessibility are performed. From this search 45 secondary studies were obtained and further analyzed. We were able to identify the most common methodologies and tools high-lighted across the secondary studies for each concept. The main results found were the following: questionnaires are ubiquitous in the field of user experience evaluation and to a lesser extent for usability while accessibility evaluation is mostly performed with the aid of automated tools, there is a tendency for authors to develop and use their own questionnaires without validating them and 24 main categories for the evaluated characteristics were found, of which Satisfaction, Efficiency, Effectiveness, and Attractiveness were the most common.

Tipo de publicación: Conference Paper

Publicado en: 2024 IEEE VII Congreso Internacional en Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC)

A Data-Driven Approach to Knowledge Assessment in Usability, UX, and Accessibility

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