Examining the impacts of learner backgrounds, proficiency level, and the use of digital devices on informal digital learning of English: an explanatory mixed-method study

Abstract

In Computer-assisted language learning (CALL), the relationship between learner background and classroom-based digital language learning has been widely studied; however, little attention has been directed to informal digital learning of English (IDLE), a crucial subdomain of inquiry of CALL. Building on our prior IDLE study, this explanatory mixed-method study set out to examine and explore the relationships between IDLE and learners’ demographics by drawing on questionnaire responses collected from 1080 Chinese university EFL learners and interview results from nine selected survey respondents. Quantitative results revealed the variance in IDLE that can be accounted for individually and in concert with learners’ demographic information, socioeconomic backgrounds, proficiency levels, and their use of digital devices and the predicting power of these factors. The results indicated that gender, university type, students’ self-rated speaking skills, standardized test results, and the number of digital devices in use contributed to the variance of IDLE. Learners’ majors, writing skills, devices using time, ethnicity, family income, and regional backgrounds predicted their engagement in IDLE activities. Qualitative results further explained these findings by highlighting how learners of various backgrounds engaged in IDLE practices in specific settings. Cross-case content analysis of the interview results provided interpretations and an in-depth understanding of learners’ situated IDLE experiences. Future studies are suggested to focus more on ethnic minority groups, students from less developed regions and ordinary universities, and non-English majors, who are less likely to benefit from IDLE activities. More attention should also be paid to how students of various sociocultural backgrounds actually engage with IDLE activities in various settings across time to address IDLE dynamics.