Building a mental toolbox: Relationships between strategy choice and sight-singing performance in higher education
Bibliographic record
Abstract
Sight-singing is an inescapable component of music training in higher education and is often challenging for students. However, some strategies could help students perform. Yet, the extent to which students can use strategies to improve their sight-singing performance remains unclear. This article asks two questions to fill this gap: (1) Which strategies do students use when sight-singing? (2) Does the application of some types of strategy predict performance? We recruited 56 postsecondary music students and asked them about their musical backgrounds. They then sight-sang a short melody while we recorded their eye movements. After that, we conducted semi-structured retrospective interviews, using eye-movement videos and attention distribution heatmaps to help participants remember the strategies they used. We analyzed the interview transcripts to identify the strategies students used and regrouped them into categories. We extracted seven categories and discovered that using body movements predicted rhythm scores, that using musical knowledge predicted pitch and combined scores, and that relying on automatic skills predicted all dimensions of sight-singing performance. We recommend that aural skills instructors teach strategies explicitly and help students develop robust musical knowledge, as they are required to build strong automatic skills.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".