An Observation Tool for Self-Regulatory Events in Music Teaching (T-SREM):Development and Testing of a Video Coding Tool for Music Lessons
Bibliographic record
Abstract
Many young people embark on music lessons during childhood, but few pursue such instruction beyond a few years’ time, thus missing out on the life-long pleasure of making music for oneself. Problems with children’s self-regulation of learning, particularly the three-phase cycle of forethought, performance, and reflection proposed by Zimmerman (2000, 2006, 2008, 2011), may influence the abandonment of formal music lessons, as suggested by the research of McPherson and his colleagues (McPherson et al., 2012; McPherson & Renwick, 2011; McPherson, Nielsen, & Renwick, 2013; McPherson & Zimmerman, 2011). As part of a larger project examining self-regulation and music learning in the digital age, an observation tool for coding self-regulatory events in music lessons was developed. The tool uses categories from Zimmerman’s self-regulatory cycle of learning to code verbal and nonverbal interactions and behaviours of teachers and students in videotaped music lessons. The iterative process of the tool’s development is presented and discussed, including an analysis of issues around using videotaped material. Results from a pilot test, in which researchers coded 12.9 hours of videotaped lessons from four music teachers in the Greater Toronto Area, are given. Patterns in self-regulated learning that emerged during the pilot test are explored, suggestions for triangulating the results with other project data are presented, and suggestions for further research are given.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".