Quality and predictors of L2 digital multimodal composition: Mixed-methods study

Abstract

Investigations pertaining to digital multimodal composition (DMC) in second language (L2) learning is expanding. However, focus on the quality of L2 learners’ DMC products in content-area courses as well as the variables influencing DMC quality is limited. This mixed-methods study aims to fill this gap by identifying the measurable characteristics of DMC quality and testing predictor variables. Thirty-three South Korean college students enrolled in an L2 literature course were instructed to individually create a DMC blog post over two weeks by thematically analyzing a stage play. Additionally, they completed a questionnaire to measure their digital literacy and an English writing task to assess their L2 writing proficiency. The DMC quality was evaluated using an analytic rubric. Cluster analysis revealed two distinct groups in terms of DMC quality, with significant differences between them. Qualitative analysis confirmed this variation. Stepwise regression analysis considering predictor variables such as digital literacy, L2 writing proficiency, and gender, showed that both digital literacy and L2 writing proficiency significantly contributed to DMC quality. These findings offer valuable pedagogical insights for enhancing L2 teaching practices using DMC.