Mohi Reza
University of British Columbia, Vancouver, British Columbia, Canada
Shadowing, i.e., listening to recorded native speech 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 shadowing systems do not adequately support listening-focused 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 design elements -- in-the-moment highlights for tracking and visualizing progress, contextual blurring for inducing self-reflection on misheard words, self-listening comparators 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 shadowers. 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.
https://doi.org/10.1145/3411764.3445190
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)