Designing CAST: A Computer-Assisted Shadowing Trainer for Self-Regulated Foreign Language Listening Practice
| Autor: | Mohi Reza, Dongwook Yoon |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Trainer
Computer science 05 social sciences Foreign language 020207 software engineering 02 engineering and technology Bridge (nautical) Speech shadowing Formative assessment Summative assessment Human–computer interaction 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Active listening Self-regulated learning 050107 human factors |
| Zdroj: | CHI |
| DOI: | 10.1145/3411764.3445190 |
| Popis: | 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 Computer-Assisted Shadowing Trainer (CAST), a shadowing system that makes self-regulation easy and effective through four novel design elements — (i) in-the-moment highlights for tracking and visualizing progress, (ii) contextual blurring for inducing self-reflection on misheard words, (iii) self-listening comparators for post-practice self-evaluation, and (iv) 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. |
| Databáze: | OpenAIRE |
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