Designing CAST: A Computer-Assisted Shadowing Trainer for Self-Regulated Foreign Language Listening Practice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".