Arts Achieve, Impacting Student Success in the Arts: Preliminary Findings After One Year of Implementation
Bibliographic record
Abstract
The Arts Achieve: Impacting Student Success in the Arts project involves a partnership between the New York City Department of Education (NYCDOE) and five of the city’s premier arts organizations. Arts Achieve provides intensive and targeted professional development to arts teachers over a three-year period. The goal of the project is to improve the quality of arts teachers’ instruction through in-service professional development on the use of balanced (formative and summative) assessment, leading to increases in students’ arts achievement. Starting in the 2011-2012 school year, arts teachers formed art discipline-based professional learning communities (PLCs) to work together, using a process of inquiry and action research that focuses on reviewing student data and examining impact on current instructional practice. Additionally, each arts teacher was paired with a facilitator from the arts organizations to support them over the course of the project. The specific professional development activities included: on-site consultancies, assessment retreats, inter-visitations, and an online community. Arts Achieve also provides participating arts teachers with resources to support this work, such as units of study and technology bundles. To measure the impact of the Arts Achieve project on arts teachers and students, Metis Associates designed a cluster randomized control trial study, whereby 77 schools were assigned to treatment or status-quo control conditions by arts discipline (dance, music, theater, visual arts) and school level (elementary, middle, high). In the planning year of the project, Benchmark Arts Assessments were developed in each arts discipline and school level to measure students’ arts achievement. Findings from Year 1 indicate that, while there were not statistically significant differences between the growth of treatment and control teachers, the students of treatment teachers demonstrated significantly greater growth in arts achievement from the students of control teachers. The results suggest that a more sensitive tool for detecting change in teachers is needed. Successes and challenges of project implementation are discussed, and potential areas for additional inquiry in the coming years of the grant also are recommended.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".