Bibliographic record
Abstract
This study examined the use of music software as a pedagogical tool for the delivery of specific content in a music education course offered to Certificate and Bachelor of Education Program students at a Caribbean university. The existing course uses a traditional approach, and thus, the study is significant as the results would propel a shift toward transformational teaching. Twenty-four university students were chosen for the study which adopted a mixed methods approach. Over one semester, participants used a free, open-source music software program to learn simple time signatures. Students produced an assignment as well as completed a questionnaire. Ninety percent of students were able to compose eight bars of music according to a simple time signature using the software. Most participants intimated they felt comfortable and motivated using the software, they understood concepts taught, and they suggested its continued use. The majority of participants also stated that they required more training. Some participants even said that they would adopt this methodology on their teaching practicum. Based on the results, recommendations include the adoption of this and other technological teaching tools within the music program, a teaching practicum assessment, and a progressive training component for both students and staff.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".