A Framework for Teaching Music Online. By Carol Johnson. Bloomsbury Academic.
Bibliographic record
Abstract
In A Framework for Teaching Music Online, Carol Johnson formulates a clear and precise framework for teaching music online that is supported by 17 peer-reviewed articles she has authored on this topic. Well-known for her scholarship, Johnson’s framework is designed to guide online teachers of music through a well-reasoned and logical step-by-step process using clear communication, authentic design, and quality assessment. The three-part process explores her framework starting with design and assessment of case studies. She then focuses on practical application of designing an online teaching space using technology tools and approaches as supporting learning mechanisms. In the final section of the framework, Johnson capitalizes on future innovations that delve into sharing knowledge and creating professional learning networks. The framework masterfully allows for discipline specificity in an arts-based discipline with niche areas such as music performance, theory, history, and composition. Johnson ensures that authentic supports are in place for all.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".