Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation
It addresses the underexplored problem of predicting MIL competencies for disinformation among future educators and communicators, offering incremental insights for educational interventions.
This study developed machine learning models to predict Media and Information Literacy (MIL) skills related to disinformation among 723 students, finding that complex models outperformed simpler ones with key factors like academic year improving accuracy.
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.