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Designing CAST : a computer-assisted shadowing trainer for self-regulated foreign language listening practice

University of British Columbia

Speech shadowing, i.e., listening to some audio and simultaneously vocalizing the words, is a popular language-learning technique that is known to improve listening skills. However, despite strong evidence for its efficacy as a listening exercise, existing software tools do not adequately support listening-focused shadowing practice, especially in self-regulated learning environments with no external feedback. To bridge this gap, we introduce CAST, a shadowing system that makes self-regulation easy and effective through four novel interface features — contextual blurring for inducing self-reflection on misheard portions, in-situ annotations for self-monitoring progress through tracking and visualization, embedded recordings for post-practice self-evaluation, and adjustable pause-handles for self-paced practice. We base CAST on a formative user study (N=15) that provides fresh empirical grounds on the needs and challenges of those who practice shadowing using conventional software tools. We validate our design through a summative evaluation (N=12) that shows learners can successfully self-regulate their shadowing practice with CAST while retaining focus on listening.

Text

2021-08-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/75794

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/