Implementation of Online Piano Teaching System Based on Internet of Things Technology
Bibliographic record
Abstract
More and more parents put their children's education at the center of their lives. As the king of musical instruments, piano is considered the first choice for music education. Due to the ever-increasing demand for piano learning, the teaching tasks of piano professional teachers are increasing day by day, and the traditional one-to-one teaching mode has been unable to accommodate to the fast evolution of piano education. In the context of the Internet of Things, new piano teaching methods that break the traditional piano teaching methods are gradually emerging. The online piano teaching mode is conducive to the optimization of teaching forms and the improvement of teaching efficiency. In order to improve the efficiency of piano teaching, this paper used the Internet of Things technology to study the online piano teaching system. In this paper, the software and hardware functions of the online piano teaching system were explained in detail, and the teaching process of using the online piano system for piano teaching was described. At last, the availability of the teaching system in piano teaching was verified by comparative experiments. The research results showed that, compared with the traditional piano teaching mode, the online piano teaching mode can improve students' learning interest and learning efficiency, and better solve the problems encountered by students in piano learning. This experiment verified the feasibility of the online piano teaching system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".