A Cognitive Framework for On-line Music Education - Students’ Performance in On-line Listening Activities in a Blended Post-secondary Music Course
Bibliographic record
Abstract
This paper describes a cognitive framework for designing on-line listening activities for students in post-secondary music courses. Drawing on music cognition and knowledge acquisition theories, technology-based listening activities were developed as supplemental to classroom-based activities. The study sample consisted of fifty-nine post-secondary students in a World Music course. Before engaging in the listening activities, students completed four pre-activity surveys: 1) general demographics (e.g., program, year in program, gender, age), 2) a music experience survey (non-credit music experience), 3) a self-regulation questionnaire (SRQ) and, 4) a Computer Experience Questionnaire. Students then completed two on-line t is not easily enacted in the large classroom due to noise and other distractions, and to the lack of time for students with higher self-regulation scores took significantly less time to complete the on-line listening activities than those with lower self-regulation. However, as predicted by Honing’s (2009) music cognition theory, students’ levels of music experience were not related to students’ efficiency in completing the activities; nor was their computer experience or their levels of self-regulation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".