Music Education Theory Teaching and Research, Belarusian State Pedagogical University Named after Maxim Tank, Minsk 220013, Belarus.
J Environ Public Health. 2022 Sep 10;2022:7724675. doi: 10.1155/2022/7724675. eCollection 2022.
Music is closely related to people's lives, and it has a certain impact on people's lives. In school teaching activities, mastering the skills of playing musical instruments can effectively improve students' music appreciation ability and level and enhance students' comprehensive quality through subtle influence. Based on the analysis of students' behavior data, this paper analyzes the role of mastering musical instrument playing skills in improving students' comprehensive quality and puts forward research ideas and schemes. It focuses on students' group behavior in the digital campus environment, integrates multisource data in the digital campus, quantificationally calculates students' multidimensional behaviors, studies the behavior rules of students with different academic performance levels, and uses machine learning algorithm to build a multifeature integrated model of students' comprehensive quality, providing personalized feedback for the improvement of students' comprehensive quality. The results show that the effect of mastering musical instrument playing skills combined with data mining analysis of students' behavior is generally 30% higher than that of the previous research. Compared with a single model, the fused model can fully consider each algorithm to observe data from different data spaces and structures and give full play to the advantages of different algorithms. The training of a single model will fall into the local minimum, which may lead to the relatively poor generalization performance of its model. However, the weighted fusion of multiple basic learners can effectively reduce the probability of falling into the local minimum.
音乐与人们的生活息息相关,对人们的生活有一定的影响。在学校教学活动中,掌握乐器演奏技巧可以通过潜移默化的影响,有效提高学生的音乐欣赏能力和水平,增强学生的综合素质。本文基于学生行为数据分析,分析掌握乐器演奏技巧在提高学生综合素质方面的作用,并提出研究思路和方案。重点研究数字化校园环境中学生群体行为,整合数字化校园中的多源数据,对学生多维行为进行量化计算,研究不同学业成绩水平学生的行为规律,利用机器学习算法构建学生综合素质的多特征综合模型,为学生综合素质的提升提供个性化反馈。结果表明,掌握乐器演奏技巧与学生行为数据挖掘分析相结合的效果普遍比以往研究提高了 30%。与单一模型相比,融合模型可以充分考虑每个算法,从不同的数据空间和结构观察数据,并充分发挥不同算法的优势。单一模型的训练可能会陷入局部最小值,从而导致其模型的泛化性能相对较差。然而,多个基本学习者的加权融合可以有效地降低陷入局部最小值的概率。