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Record W2101351882 · doi:10.1177/1048371309354432

A Minds-On Approach to Active Learning in General Music

2009· article· en· W2101351882 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeneral Music Today · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Development and Education Research
Canadian institutionsBrandon University
Fundersnot available
KeywordsPsychologyDismissalClass (philosophy)Task (project management)Mathematics educationActive learning (machine learning)PedagogyFunction (biology)MusicalRelation (database)Computer science

Abstract

fetched live from OpenAlex

Minds-on engagement in active learning is explored through the experiences of Margaret Sanders, a general music teacher. Minds-on learners think about their experiences. They are actively involved as questioners and problem solvers while they complete musical tasks and reflect on their work after it is completed. Minds-off learners focus on their actions but not on the thinking required to complete a given task. This idea is explored in relation to the use of classroom routines to direct instruction. Routines serve a valuable function in moving students through their school day, assisting their progress from class to class to their dismissal at the end of the day. However, teachers may assume that students are involved in minds-on learning when, due to instructional routines, students’ responses represent a minds-off engagement in their learning. Teachers of general music must constantly challenge students in unexpected ways to maintain their minds-on engagement in music.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.352
Teacher spread0.280 · 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