Affective Support for Self-Regulation in Mobile-Assisted Language Learning

Introduction

Recent meta-analyses have shown that language learners’ affective states have a significant impact on their performance (Dewaele, 2022) and engagement (Shen, 2021). Scholars also stress that positive emotions contribute to the development of self-regulation and autonomy (Oxford, 2017), which are essential for successful second and foreign language (L2) acquisition.

The psychological concept of self-regulation began to attract attention in language learning research two decades ago (Dörnyei, 2005; Oxford, 1999) in relation to earlier established work on language learning strategies and learner autonomy. Research on self-regulation has grown in prominence alongside – and in synergy with – the development of technology-supported learning platforms and environments that present opportunities for self-paced and student-directed learning, as well as a growing body of research on language learning on mobile devices (e.g., Hsu & Lin, 2021; Lin et al., 2020). As apps, digital resources and online communities for mobile assisted language learning (MALL) continue to expand, there is a growing sense among teachers and researchers that learners should be supported to expand their abilities, skills and strategies in order to take full advantage of the new opportunities for self-regulated mobile learning in and out of class. In doing so, they can be encouraged to learn regularly and reflectively, with greater self-awareness of how to progress and improve their language learning across formal and informal learning settings (Perry & Moses, 2019). Recently, Lai et al. (2022) examined L2 learners’ use of mobile technology in self-directed learning. They stress that students utilise mobile-accessible apps (e.g., Duolingo) to create their own learning environment in which they predominantly do not receive support from facilitators (e.g., teachers), especially when “the whole process is self-initiated” (p.2). However, earlier research emphasises that students are generally bad at calibrating their own learning (Dunlosky & Lipko, 2007), including evaluating and regulating their abilities, strategies and motivations. They often need support to develop and grow their self-regulated learning (SRL) skills, strategies and knowledge (Viberg, Khalil, et al., 2020).

Since mobile technologies provide opportunities for L2 learners to engage in complex interactions involving a multitude of cognitive, meta-cognitive, and affective factors (Peng et al., 2021), self-regulation is a key ability. The lens of self-regulation (e.g., Zimmerman, 2000) is therefore a fitting one for the design and development of appropriate support mechanisms in MALL; even more so considering recent developments in the field of learning analytics for SRL (Viberg, Khalil, et al., 2020; Winne, 2017), and the establishment of the MALLAS framework (Mobile-assisted language learning through learning analytics for self-regulated learning). MALLAS is a conceptual framework that captures the dimensions of self-regulated language learning and learning analytics that are required to support MALL (Viberg, Wasson et al., 2020; further explained below).