Using Modern Technologies in Teaching Music
Bibliographic record
Abstract
The thesis consists of two parts. The artistic part is the concert “Young Soloists” with Turku Philarmonic Orchestra in Turku Concert Hall the 15th of October 2020. The concert is the weighted section of this thesis.The link to the recording of the concert is in the appendix of this thesis. \nIn the written part of the thesis, the using of modern technologies in teaching the violin and some music basics for beginners is speculated. \nAnother goal is to seek a new pedagogical approach with technological methods when teaching students who have just started studying music regardless of age.The author`s purpose is to enhance his teaching and get results faster than usual and become a better teacher upon completion of his thesis. \nAn online survey on the use of technological learning and teaching tools was used as a research method for this thesis. It was sent to Russia, Kazakhstan, Finland, Latvia, Czech Republic, Germany, Austria, Chile, Canada and the United States. Answers were received from each country, but the vast majority of participants came from Russia. The results of the survey are discussed in the first chapter of the thesis. \nThe results of this thesis can be applied to music teaching and can benefit all teachers interested in digital technology and those seeking new teaching methods.
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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 | 0.001 | 0.003 |
| 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 | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".