A population-level analysis of associations between school music participation and academic achievement.
Bibliographic record
Abstract
[Correction Notice: An Erratum for this article was reported online in Journal of Educational Psychology on Oct 17 2019 (see record 2019-62704-001). In the original article, Tables 2 and 4 contained typographical errors regarding the reporting of some sample sizes. In Table 2, for the Unadjusted Analyses section, the n for the “No participation in school music” group should read 75,616 for the Math 10 group, and 95,873 for the Science 10 group while the n for the “Participation in school music” group should read 13,772 for the Math 10 group, and 15,416 for the Science 10 group. In Table 4, for the Unadjusted Analyses section, the n for the “No participation in school music” group should read 75,616 for the Math 10 group, and 95,873 for the Science 10 group. All calculations were based on the correct sample sizes, the typographical error was isolated to n reported in the aforementioned instances in these two tables. All versions of this article have been corrected.] The present study employed population-level educational records from 4 public school student cohorts (n = 112,916; Grades 7–12) in British Columbia (Canada) to examine relationships between music education (any participation, type of participation, music achievement, and engagement level) and mathematics and science achievement in Grade 10 as well as English achievement in Grades 10 and 12, while controlling for language/cultural background, Grade 7 academic achievement, and neighborhood socioeconomic status. Music participation was related to higher scores on all 4 subjects and these relationships were stronger for instrumental music than vocal music (Cohen’s d range: .28 to .44 [small-medium effect sizes] and .05 to .13 [null-small effect sizes]). School music achievement positively related to scores on all subjects; such relationships were stronger for achievement in instrumental music compared with vocal music. Higher levels of music engagement (number of courses) was related to higher exam scores on all subjects; this pattern was more pronounced for very high engagement in instrumental music (d range: .37 to .55; medium effect sizes) compared with vocal music (d range: .11 to .26; small effect sizes). The effect sizes of these group differences are greater than the effect sizes corresponding to average annual gains of students’ academic achievement during high school—in other words, highly engaged instrumental music students were, on average, academically over 1 year ahead of their peers. The findings suggest that multiyear engagement in music, especially instrumental music, may benefit high school academic achievement. Findings and implications are discussed within the broader interdisciplinary literature on music learning. (PsycINFO Database Record (c) 2020 APA, all rights reserved)
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".