Experiential and Cognitive Predictors of Sight-Singing Performance in Music Higher Education
Bibliographic record
Abstract
Sight-singing is prevalent in aural skill classes, where learners differ in experience and cognitive abilities. In this research, we investigated whether musical experience, level of study, and working memory capacity (WMC) can predict sight-singing performance and if there is a correlation between WMC and performance among some subgroups of participants. We hypothesized that more experienced students and those with a higher WMC might sight-sing better than those with less experience and lesser WMC. We also hypothesized that the relationship between WMC and sight-singing performance would be more salient for less experienced and less proficient sight-singers. We surveyed 56 subjects about their experience with music, assessed their WMC, and evaluated their performance on a short sight-singing task. The results showed that the age when students began learning music could predict sight-singing performance independently from the number of years of experience and the educational level, suggesting a possible developmental component to sight-singing skill. We also found a negative relationship between WMC and pitch score in the low-performing group and between rhythm and pitch score, suggesting that pitch and rhythm are processed differently. Teachers should be aware of how students' backgrounds might be related to performance and encourage them to develop strong automated skills, such as reading music or singing basic tonal patterns.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".