Enhancing the Music Studio Community and Self-Regulated Learning Through the Use of Electronic Portfolios
Bibliographic record
Abstract
The body of research examining deliberate practice and self-regulation in musical instruction has grown extensively over the last several years and has indicated that students with higher levels of self-regulation develop superior performance skills. Recommendations from this literature have emphasized that skilled and expressive musical performances require the supportive development of self-regulatory behaviours. Developing these behaviours involves the incorporation of strategies that presuppose a certain level of discipline and organization on the part of the student. This study examines the implementation of an electronic portfolio, ePEARL, that was used in two studio settings to help students take more control over their learning and creative processes. ePEARL embeds self-regulation processes within an electronic portfolio, allowing students to document their work while at the same time developing planning, doing, and reflecting skills. This portfolio also builds community by allowing peers, parents and teachers to access each other’s work. ePEARL has been successfully used in classrooms in Canada, the United States, and parts of Europe to increase levels of self-regulation and achievement. Recently, this technology has been implemented in the music studio context. Using case-study methodology, we examined six students’ use of ePEARL over the course of four months. Using interview data from teachers and students as well as the observations of the portfolio use, we examined how students and teachers use ePEARL to plan, execute, and reflect on their music-making. ePEARL was an effective way to articulate musical goals and archive musical accomplishments. Students were able to solicit and incorporate feedback from their teachers, peers, and parents regarding their musical activities outside of their lesson time, which supported their learning. Overall, students enjoyed using the tool, and ePEARL was effective in helping set and achieve goals in the context of learning to play an instrument.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".