Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 3884587
• Main Authors: Li, Baozhen, Zhang, Lina
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Classrooms; Curricula; Data mining; Data processing; Design factors; Education; Efficiency; Humans; Intelligence; Mathematical analysis; Mathematics; Mathematics teachers; Methods; Neural networks; Principal components analysis; Quality assessment; Students; Teaching; Teaching methods
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/3884587

With the increasing importance of mathematics in basic education, how to evaluate and analyze the intelligent effect of mathematics teaching classroom through scientific methods has become one of the indicators to evaluate the intelligent classroom. This paper studies the design and application of mathematics teaching intelligent classroom based on the PCA-NN (principal component analysis-neural network) algorithm. Firstly, this paper briefly describes the current research status of mathematics teaching intelligent classroom design and PCA-NN algorithm. Secondly, combined with the key factors of mathematics teaching, it formulates specific standards and puts forward an adaptive strategy of intelligent and personalized intelligent mathematics teaching classroom. Finally, the algorithm is verified by experiments. The results show that, for students with different mathematics basic levels, the mathematics teaching intelligent classroom based on the PCA-NN algorithm can effectively improve the quality of mathematics classroom teaching. Through the research on the factors such as teaching quality, effect, and interaction mode involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting teaching quality are determined. This paper analyzes and studies the system from different angles. The research results provide some help for the current quality evaluation of classroom teaching and use the PCA-NN algorithm to make quantitative analysis and multivariate verification of mathematics classroom teaching effect.