Do background characteristics matter in Children's mastery of digital literacy? A cognitive diagnosis model analysis

Abstract

This study aims to investigate the mastery profiles of digital literacy skills of Hong Kong primary students using a general cognitive diagnosis model (CDM) framework. In particular, the relationship between the mastery of each digital skill and a number of students' background characteristics is explored using a three-step approach. The current study analyzes data collected from 642 Grade 3 students in Hong Kong using a newly developed digital literacy assessment (DLA). CDMs are fitted to the data to determine students' mastery profiles of five digital skills, as well as test properties; subsequently latent logistic regression analyses were implemented to determine the relationship between skill mastery and the covariates. Results indicate that CDM analysis is an appropriate method to analyze the DLA performance data, which exhibited measurement invariance across gender and socioeconomic status (SES). Despite low mastery proportions for all digital skills, students' skill mastery can be accurately classified. Finally, the latent logistic regression results indicate that children's background characteristics (i.e., gender, educational aspiration, home language, SES, and access to digital devices) are differentially related to their mastery of each digital skill.