Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation
Author(s)
Alcalde-Llergo, José Manuel
Fernández, Mariana Buenestado
George-Reyes, Carlos Enrique
Yeguas-Bolívar, Enrique
Date Issued
2025
Type
article
Volume
16
Issue
929
Start Page
1
End Page
20
Journal
Abstract
This study develops machine learning models to assess Media and Information Literacy (MIL) skills specifically in the context of disinformation among students, particularly future educators and communicators. While the digital revolution has expanded access to information, it has also amplified the spread of false and misleading content, making MIL essential for fostering critical thinking and responsible media engagement. Despite its relevance, predictive modeling of MIL in relation to disinformation remains underexplored. To address this gap, a quantitative study was conducted with 723 students in education and communication programs using a validated survey. Classification and regression algorithms were applied to predict MIL competencies and identify key influencing factors. Results show that complex models outperform simpler approaches, with variables such as academic year and prior training significantly improving prediction accuracy. These
findings can inform the design of targeted educational interventions and personalized strategies to enhance students’ ability to critically navigate and respond to disinformation in digital environments.