Applications of Music Within the Neurolinguistic Approach in a German Bilingual School
Bibliographic record
Abstract
This paper presents a study exploring applications of music within the Neurolinguistic Approach (Germain, 2018) to enhance second or additional language teaching in a Kindergarten to Grade 6 German bilingual school. Each participating teacher was interviewed about how they employ music in the classroom, how they create musical resources for teaching language, and what benefits and challenges they have experienced from its use. One of the key findings is that teachers use songs as oral models to teach both the implicit grammar of the target language and accurate pronunciation. Furthermore, teachers are adapting existing musical resources and creating their own to provide rich texts for classroom activities to help establish routines, to aid in emotional regulation and to facilitate the memorization of difficult concepts. Finding age-appropriate materials that are suitable for the skill levels of their students remains the greatest challenge. As one of the first studies to study music with the Neurolinguistic Approach, the findings suggest that this music-integrated approach has the potential to facilitate second language teaching.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".