Revisiting informal digital learning of English (IDLE): a structural equation modeling approach in a university EFL context

Informal digital learning of English (IDLE) is an increasingly important subfield of inquiry in Computer-Assisted Language Learning (CALL) for its concentration on the language learning practices of the digital native EFL students in out-of-class contexts. Attention in mainstream research of IDLE has been directed to (meta)cognition, learning outcomes, etc. as separate domains of IDLE; however, these significant efforts have neither clarified the blurry boundary between semi-structured IDLE practices and unstructured extramural practices nor investigated factors that positively predict learners' IDLE-enhanced learning outcomes or practices on the four language skills: listening, speaking, reading, and writing. As such, the present study sets out to extend the discussion of informal digital learning of English (IDLE) by establishing a recontextualized model of IDLE. To this end, a total of 1080 Chinese university EFL learners were invited to complete an IDLE questionnaire survey developed and validated in the context of China. Using IBM SPSS Amos 22, structural equation modeling was run to examine the inter-factorial relationships among six IDLE sub-constructs by examining eight hypotheses. The results confirm that 1) learners' IDLE-enhanced benefits are positively predicted by support from their important others, resources and cognition, but not learners' authentic L2 experience and IDLE frequency and devices, and 2) learners' IDLE practices can be significantly predicted by resources and cognition, authentic L2 experience, and IDLE frequency and devices. Based on these findings, we put forward an expanded conceptual framework of IDLE and suggest more replication studies in the future to testify this study's generalizability and to arrive at more stable conclusions.