INVITING MUSIC STUDENTS TO IDENTIFY AS CONTENT CREATORS TO ENCOURAGE PARTICIPATION AND LEARNING
Bibliographic record
Abstract
A variety of instructional strategies can be used to facilitate effective learning environments.This article reports on a particular instructional strategy, i.e., inviting music students to take on the identity of a content creator.Over a period of 20 weeks, 18 piano students ages 10 to 15 used a mobile app designed as a self-contained social media platform which allowed them to create and share audio recordings of their piano practice with one another.At first, the student participants used the app in limited ways, due to their sense of individualism, as well as their performance-based mindset.After week 10, participants were encouraged to take on the identity of content creator as a means of using the mobile technology to engage in meaningful learning.To support students' envisioning of themselves as content creators, activities were designed to help them celebrate process over product, and to set content-creation goals.Introducing the content creator identity, as a strategy, was effective for increasing and expanding the use of the mobile app for musical thinking and learning.Instructors who are considering ways to engage learners in relevant and participatory ways may benefit from this discussion of the content creator identity strategy.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".