Affective support for self-regulation in mobile-assisted language learning

Olga Viberg, Agnes Kukulska-Hulme, Ward Peeters

Research output: Contribution to journalArticleResearchpeer-review


Mobile-assisted language learning (MALL) research includes examination and development of second language learners' cognitive and metacognitive self-regulated learning skills, but the affective learning component of self-regulation in this context remains largely unexplored. Support for affective learning, which is defined by learners' beliefs, attitudes, and emotions, has been shown to influence learners' cognitive processes, performance, and engagement considerably, and is therefore critical to promote and foster throughout the learning process. This paper defines the importance of supporting affect in MALL, sets out a theoretical perspective on supporting affective self-regulation in MALL, and elaborates on what designers and teachers can do to facilitate affective development through the use of mobile technology, learning analytics, and artificial intelligence. It examines and further delineates the role of affective computing and the role of the teacher in fully harnessing the affective affordances of MALL.

Original languageEnglish
Article number26
Number of pages15
JournalInternational Journal of Mobile and Blended Learning
Issue number2
Publication statusPublished - 2023


  • Affect
  • Artificial Intelligence
  • L2 Learning
  • Learner Autonomy
  • Learning Analytics
  • Mobile App Design
  • Mobile- Assisted Language Learning
  • Self-Regulated Learning
  • Support

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