Social and Informational Affordances of Social Media in Music Learning and Teaching
Bibliographic record
Abstract
This chapter examines the role of social media in music learning and teaching with the aim of discerning the affordances created by specific features and functions. While much scholarship has outlined the many merits and possibilities of including social media in formal and informal music education, not much is known about what aspects of social media lead to positive outcomes. Music education is defined broadly here and includes both learning about music and learning with the purpose of achieving classroom goals. The majority of research either tends to focus on single platforms or discusses social media more generally. The present chapter starts with a close look at the affordance concept, tracing its historical roots and problematizing its definition. The chapter then discusses how various affordances can contribute to different aspects of music education. Much of the literature on social media has examined the social affordances of social media and neglected to consider the informational affordances. The chapter argues that both social and informational affordances are important in investigations of social media for music education. Finally, conclusions are discussed for 21st-century learners, and the advantages of employing the affordances framework in studies of music education and social media are outlined. Future research based on the affordances framework that could examine what features and functions of social media are beneficial for music learning and teaching are examined, including discussion of a series of constraints placed on learners and teachers by the technology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".