Nonprofit Arts Programs as Professional Development Experiences in Rural West Virginia
Bibliographic record
Abstract
Public educators in West Virginia are required to take 18-hours of professional development learning annually. The professional development opportunities offered are not varied, inclusive of all content areas, and have been found by the state’s Department of Education to lack in application and effectiveness. This study examined the benefits of living laboratory professional development experiences, utilizing Howard Gardner’s theory of multiple intelligence, with educators that volunteer with fine arts community-based nonprofit organizations, as a possible alternative to the professional development policy requirements by the state. The study’s key research questions were about the recognition and enhancement of intrapersonal and interpersonal intelligence of research participants, as a result of volunteering with the partner organization. Transcendental phenomenology guided the study and the development of the focused interview model used with 8 participants, who were active or retired certified public educators in the state of West Virginia with at least 1-year of volunteer experience with the partner program. It was concluded that participants were aware of his or her own intrapersonal and interpersonal intelligence while volunteering with the partner program and in the regular classroom, with interpersonal intelligence most frequently used by participants. Most importantly, the study found that experiences that allowed the research participants to demonstrate proficiency with acquired knowledge and skills, like that of the partner organization, increased respect and self-confidence for educators and self-efficacy and motivation for students resulting in positive social change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".