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Record W4389129328 · doi:10.21432/cjlt28532

A Framework for Teaching Music Online. By Carol Johnson. Bloomsbury Academic.

2023· article· en· W4389129328 on OpenAlexaffvenue
Sandra Duggleby

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsScholarshipThe artsComputer scienceMusic educationProcess (computing)Educational technologySociologyTeaching methodInstructional designPedagogyMultimediaMathematics educationPsychologyVisual artsArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.274
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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