Abstract
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.
Highlights
With the promotion of diversified teaching mode and the improvement of customized teaching methods, mathematics teaching has become an important basic subject teaching [1]
Is paper proposes an optimization model of mathematics classroom design and teaching quality evaluation based on the PCA-NN algorithm. is paper is divided into four parts
E innovation of this study is to select the local PCANN optimization algorithm, using the model related to Computational Intelligence and Neuroscience classroom comprehensive evaluation; this paper studies the contents of different parameters related to the influencing factors of evaluation and proposes a mathematics classroom teaching design and evaluation system based on the PCANN algorithm. rough the research on the teaching quality, effect, interactive mode, and other factors involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting the teaching quality are determined. is paper evaluates the research evaluation system from different angles, provides a comprehensive purpose index for the construction of classroom teaching quality, and uses the PCA-NN algorithm to quantitatively analyze and verify the effect of mathematics classroom teaching
Summary
With the promotion of diversified teaching mode and the improvement of customized teaching methods, mathematics teaching has become an important basic subject teaching [1]. The evaluation model of mathematics classroom teaching based on the PCA-NN algorithm is proposed, and the evaluation index system of mathematics classroom teaching quality is constructed by using the Laplace factor method and combining with students’ learning effect. According to the five factors affecting the core quality of mathematics classroom teaching, this paper optimizes the traditional mathematics classroom teaching methods, puts forward the evaluation method based on the PCA-NN algorithm, and constructs a more scientific intelligent quality evaluation model of mathematics classroom teaching. Li et al through the simplified analysis of different mathematics teaching classroom made students in the process of learning to achieve a deep thinking state based on mathematical exercises; through experiments, it is proved that this teaching method can well improve students’ ability to accept new knowledge in a short time [10]. It is of great significance to study the design of mathematics teaching intelligent classroom based on the PCA-NN algorithm [20]
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