用机器学习预测学生识破假信息的能力，助力教育干预。
Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation
- 构建分类与回归模型，基于问卷数据预测媒介素养水平。
- 学术年级和前期培训是关键影响因素，提升预测准确率。
- 适合教育政策制定者与未来教师参考，用于设计精准培训。
本研究针对教育与传播专业学生，开发机器学习模型以评估其在应对虚假信息情境下的媒介与信息素养（MIL）能力。尽管数字革命扩大了信息获取渠道，也加剧了错误信息的传播，使媒介素养成为培养批判性思维和负责任媒体参与的关键。然而，关于媒介素养与虚假信息关系的预测建模仍鲜有研究。本研究通过对723名学生进行量化调查，应用分类与回归算法，预测媒介素养水平并识别关键影响因素。结果显示，复杂模型优于简单方法，学术年级和前期培训显著提升预测精度。研究成果可为设计针对性教育干预和个性化策略提供依据，帮助学生在数字环境中更有效地识别与应对虚假信息。
原文摘要 · Abstract (English)
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.
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