Mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS): A conceptual framework

Abstract

Many adult second and foreign language learners have insufficient opportunities to engage in language learning. However, their successful acquisition of a target language is critical for various reasons, including their fast integration in a host country and their smooth adaptation to new work or educational settings. This suggests that they need additional support to succeed in their second language acquisition. We argue that such support would benefit from recent advances in the fields of mobile-assisted language learning, self-regulated language learning, and learning analytics. In particular, this paper offers a conceptual framework, mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS), to help learning designers support second language learners through the use of learning analytics to enable self-regulated learning. Although the MALLAS framework is presented here as an analytical tool that can be used to operationalise the support of mobile-assisted language learning in a specific exemplary learning context, it would be of interest to researchers who wish to better understand and support self-regulated language learning in mobile contexts.
 Implications for practice and policy:
 
 MALLAS is a conceptual framework that captures the dimensions of self-regulated language learning and learning analytics that are required to support mobile-assisted language learning.
 Designers of mobile-assisted language learning solutions using MALLAS will have a solution with sound theoretically underpinned solution.
 Learning designers can use MALLAS as a guide to direct their design choices regarding the development of mobile-assisted language learning apps and services.