[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Abstract

In order to speed up the process of high-quality education and improve the level of education quality among the general public, people have pushed for the use of music art education in recent years. In this respect, this study covers the CNN-based assessment of the quality of music art teaching and creates a set of evaluation indices for that quality. The model architecture, network topology, learning parameters, and learning algorithm are all determined using this information, which also acts as the basis for the NN assessment model. The MATLAB simulation tool uses the CNN assessment model to train and learn a predetermined quantity of instructional quality data. The training experiment shows that this system can outperform other comparative systems in prediction accuracy by roughly 95%. Additionally, both the training and prediction accuracy of the model are completely acceptable. The evaluation findings and analytical data of the music art instructional quality assessment system created in this study can be used as a guide for determining the music art instructional quality and for making judgments regarding it.

Improving the Efficiency of College Art Teaching Based on Neural Networks

“…With the development of information network technology, neural networks have been applied to various industries, and their good prediction function is widely used in practical work. Neural networks can quickly and efficiently propose solutions for actionable behaviors with clear rules, typical universal laws, and programmable implementations with general characteristics (Lan & Fan, 2022). Their efficiency is almost a million times that of neurons in the human brain.…”

Design And Realization of A Comprehensive Quality Assessment System for College Students Based on the Bp Neural Network

“…The data must be cleaned, normalized, and organized to facilitate effective analysis and training of the neural network. Once the data is prepared, the BP neural network can be trained using supervised learning techniques [15]. During the training process, the network learns to recognize patterns and correlations between input parameters and desired outcomes, such as academic success or personal growth.…”

The rise of big data on the development of music education and innovation to promote

“…The development of music education internationally can be summarized in two basic features: the recognition of humanistic and pluralistic values in the basic teaching philosophy of music [7]. In terms of the teaching practices of music disciplines, the expansion of the field of music teaching, the enrichment of music teaching contents, as well as the attention to learners' feelings and experiences of music itself, allowing students to learn music in colorful music practice activities [8]- [9].…”