Factors Influencing Technology Use in Aural Skills Lessons
Bibliographic record
Abstract
In North America, aural skills ( as ) are usually taught to children during the instrumental music lessons. While learning musical dictation and sight-singing can be difficult for some learners, the use of appropriate technological tools could facilitate the process. However, the use of information and communication technologies ( ict ) by music teachers in aural skills instruction to children have not been documented. An online survey was conducted in the Province of Quebec (Canada) in order to answer the following questions: 1) To what extent do instrumental music teachers use ict when teaching as to children between 6 and 12 years old?; 2) Are the teachers’ socio-demographic characteristics, as training and perception of as teaching linked to the use and the frequency of use of ict ? The results show that the use of ict to teach as is still relatively uncommon. Furthermore, it would be negatively correlated with age, competence felt during training and perceived competence to teach as . Finally, it appears that a smaller proportion of piano teachers and women use ict , or use them less often. A better understanding of the teachers’ perception of technology could help develop more adapted resources.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".