Learning French through music: the development of the Bande à Part app
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".