A Deep Learning-Based National Digital Literacy Assessment Framework Utilizing Mobile Big Data and Survey Data

Abstract

With the rapid advancement of digital technology, artificial intelligence has ushered in a digital society. In this era, digital literacy has become a prerequisite for individuals, as its absence can lead to new vulnerabilities and inequalities, hindering the pursuit of sustainable development goals. Previous researches predominantly relied on questionnaires to assess digital literacy, often focusing on specific groups due to survey costs, making their methodology unsuitable for comprehensive countrywide measurement. To address these limitations, we propose FLAKE, a national digital literacy assessment framework. Within this framework, we devise a multi-task deep learning model called DLMaN, which employs mobile big data, such as users’ digital behaviors, to predict citizens’ digital literacy. FLAKE enables cost-effective assessment of digital literacy for massive citizens by surveying only a fraction of them and it also has valuable implications for other social research tasks. We test the framework’s performance using authentic survey data and mobile big data, achieving RMSE and MAPE of 5.233 and 8.65% respectively, and the improvement is significant compared to the baseline model. We further employ this model to assess the digital literacy of numerous citizens in China and explore the implications for the society and individuals based on the obtained results.

Keywords

• Digital literacy
• deep learning
• multi-task learning
• mobile big data
• data fusion