Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation

TL;DR

This paper develops machine learning models to assess students' media literacy skills related to disinformation, aiming to improve educational strategies for critical digital engagement.

Contribution

It introduces predictive models for MIL skills in disinformation contexts, highlighting key factors influencing literacy levels among students.

Findings

Complex models outperform simpler ones in predicting MIL.

Academic year and prior training significantly improve prediction accuracy.

Results inform targeted educational interventions.

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.