Research on Optimising Personalised Teaching Models in University Piano Courses Using Reinforcement Learning Algorithms

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Design of the teaching experiment

	Control group	Experimental group
Students	500 students in c city	500 students in c city
Enviroment	c city	c city
Expeiment	Traditional teaching mode	Personalized teaching mode
Variables	Reinforcement Learning Algorithm Optimization System

Statistics on the participation of different teachers and students in personalised music teaching

Project	Number of teachers	Proportion	Number of children	Proportion
Never used	46	46%	338	68%
Occasionally use	38	38%	107	21%
Frequently use	16	16%	55	11%
Total	100		500

Allocation of weights for student performance in university music teaching classroom

Project	Proportion	Duration	Proportion
Level of interest	20%	Never used	10%
Classroom performance	20%	Occasionally use	30%
Talent	10%	Frequently use	60%
Level of diligence	20%
Classroom feedback situation	30%

Satisfaction with different music classroom models among 1000 A-city university students

	Traditional teaching model	Interactive teaching mode	Reverse teaching model	Personalized teaching model
Satisfied	683	736	816	947
Unsatisfied	317	264	184	53
Satisfaction rate	68.3%	73.6%	81.6%	94.7%