Anyang Normal University, Anyang, China.
Front Public Health. 2022 Jul 29;10:966628. doi: 10.3389/fpubh.2022.966628. eCollection 2022.
Reflecting students' mental health data through vocal music teaching expressive system is a research hotspot in vocal music teaching psychology. Based on the theory of students' expressiveness in vocal music teaching, this paper constructs a psychological model of vocal music teaching. The model uses psychological data mining technology to conduct a feasibility study and analysis on the mental health education of vocal music students, solves the quantitative problem of mental health, and analyzes the relationship between psychological problems and students. In the simulation process, the psychological data of the vocal music freshmen of a certain college was taken as the research object, and the association rule Apriori algorithm was used to analyze the relationship between the factors of the psychological dimension. Psychological data mining was carried out, and descriptive indicators and univariate analysis methods were used to analyze the current situation of students' mental health and personality characteristics, and Pearson correlation analysis and structural equation model were used to explore the relationship between their mental health and personality characteristics. The amount of vocal music learning is the duration of the load and the total number of tasks completed within a single exercise or a series of exercises. ASP-NET and SQLServer2008 and other experimental results show that the chi-square test value of the overall fit of the model is 20.078, and the ratio of the chi-square value to the degree of freedom is 4.016, which has a relatively high accuracy and effectively enhances the psychological data mining technology in vocal music students for operation and practicality of applications in health data analysis.
通过声乐教学表现系统反映学生心理健康数据是声乐教学心理学的研究热点。本文基于声乐教学中学生表现力的理论，构建了声乐教学的心理模型。该模型采用心理数据挖掘技术，对声乐学生的心理健康教育进行了可行性研究和分析，解决了心理健康的量化问题，并分析了心理问题与学生之间的关系。在模拟过程中，以某高校大一声乐新生的心理数据为研究对象，运用关联规则 Apriori 算法对心理维度的因素关系进行分析。进行心理数据挖掘，采用描述性指标和单变量分析方法，对学生心理健康和人格特征的现状进行分析，采用 Pearson 相关分析和结构方程模型探讨其心理健康与人格特征的关系。声乐学习量是负荷量和单个练习或一系列练习中完成的任务总数。ASP-NET 和 SQLServer2008 等实验结果表明，模型整体拟合的卡方检验值为 20.078，卡方值与自由度的比值为 4.016，具有较高的准确性，有效增强了声乐学生心理健康数据挖掘技术在健康数据分析中的可操作性和实用性。
