Volume 18, Number 3

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

  Authors

Antonio Piedra, Ignacio Diaz-Oreiro, Jose A. Brenes and Gustavo Lopez, University of Costa Rica, Costa Rica

  Abstract

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.

  Keywords

Machine learning, Usability, User Experience, Accessibility, Questionnaire, Self-assessment