Identifying Key Contextual Factors of Digital Reading Literacy Through a Machine Learning Approach

Abstract

Few of previous reading studies comprehensively examined the contributing factors of students’ digital reading literacy. To fill this gap, based upon the ecological perspective, this study aims to investigate which factors from the student, home, and school context are more important in discriminating high-performing digital readers from non–high-performing digital readers. The data of the Progress in International Reading Literacy Study 2016 with 74,692 Grade 4 students from 14 countries and economies was analyzed using the machine learning approach of support vector machine with recursive feature elimination. Results showed that except print reading levels, students’ reading self-efficacy, home resources for learning, talking about what have read in class, and the number of books in the home are the most influential contextual factors contributing to the high performance of digital readers. The selected 20 key contextual factors render a high prediction power for discriminating digital readers. Our findings show that, in general, home-related factors have overarching influences on children’s digital reading development; at the school level, instruction-related features are more influential than school characteristics.