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 useictwhen teachingasto children between 6 and 12 years old?; 2) Are the teachers’ socio-demographic characteristics,astraining and perception ofasteaching linked to the use and the frequency of use ofict? The results show that the use ofictto teachasis still relatively uncommon. Furthermore, it would be negatively correlated with age, competence felt during training and perceived competence to teachas. Finally, it appears that a smaller proportion of piano teachers and women useict, or use them less often. A better understanding of the teachers’ perception of technology could help develop more adapted resources.
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 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".