Mentoring as a Tool for Development of Preservice Early Childhood Music Teachers: A Pilot Study Using the Classroom Assessment Scoring System (CLASS)
Bibliographic record
Abstract
This article focuses on mentoring as a learning tool for the music specialist teacher in early childhood. The purpose of this pilot study was to assess the development of three early childhood preservice music teachers in a 7-week mentoring experience and to analyze the feedback provided by the mentor and its influence on the development of the mentees. Using a multiple-case study methodology, this research incorporated the Classroom Assessment Scoring System (CLASS) to observe and evaluate interactions. Results were compared with published studies on early childhood educators to assess CLASS’s potential for broader application with music specialists. Our results indicate that two of the three participants improved significantly on some of the dimensions studied. The greatest amount of feedback the mentees received concerned the Instruction Learning Format. No significant association was found between categories of feedback and improvement. Finally, when compared to published results, a significant correlation was found between music teachers in this study and early childhood educators from previous studies using the CLASS. Therefore, the tool has the potential to be used with music specialists in the future. More resources should be attributed to investigating mentoring as a useful supplement to the formal training of music teachers.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".