Learning French through music: the development of the Bande à Part app
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article describes the development of Bande à Part, a mobile music application (app) for second language (L2) learners of French. Our focus on the pedagogical use of music results from the reported benefits that it offers language learners (e.g., it encourages repetitive exposure to the L2 in an enjoyable way, it extends the reach of the language classroom). In addition, Bande à Part has the potential to contribute to this under-researched area of L2 French pedagogy (Engh, 2013). The development of the app adopted current SLA theory and principles such as those set forth by Doughty and Long (2003). Some of these principles suggest that technology can help learners through input enhancements (e.g. grammatical gender highlighting, subtitles and translations) and grading content for proficiency level, particularly if offered in a mobile environment to foster “anywhere, anytime” learning (e.g., Stockwell, 2010). This paper introduces Bande à Part and the rationale for its development, including how Doughty and Long' (2003) principles were used to promote L2 learning in a mobile-assisted environment. Lastly, the current lyrical corpus is evaluated for vocabulary coverage in order to highlight the app's strengths and weaknesses according to this criterion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it