Year: 2026
Type: preprint
Abstract: Tailoring instruction to individual abilities can improve learning outcomes. Advances in digital technology have brought individualized learning to the forefront of pedagogical practice, with adaptive systems likely to become increasingly important. We aim to develop a digital tool that tailors language learning to the cognitive and environmental characteristics of individual learners. We focus on adolescents, given their distinctive learning potential and interest in foreign language learning. The first stage identifies key cognitive and environmental predictors of L2 learning in adolescents, focusing on phonology, grammar, and vocabulary. Based on a systematic literature review, we selected candidate predictors including phonological and visual working memory, phonological awareness, non-verbal intelligence, language aptitude, motivation, age of onset, quantity and quality of exposure, and L1–L2 typological distance. Predictors were assessed using standardized cognitive tasks and questionnaires implemented in an online test battery using Gorilla Experiment Builder. Adolescent learners of L2 English completed these measures alongside assessments of their phonological, lexical, and grammatical performance. Data from 350+ learners aged 10–24 reveal substantial individual variation in both English proficiency and cognitive performance. These data will be analysed using statistical and machine-learning approaches to establish how individual predictors and their interactions explain variation in L2 performance. Results will be presented at the Annual Symposium. The second stage will investigate the mechanisms and causal pathways underlying key predictors before translating these findings into a proof-of-concept study of digitally tailored language instruction.
Institutions: University of Cambridge, Cambridge University Press
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225621847
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