Science Teaching Department, Zhengzhou Preschool Education College, Zhengzhou 450000, China.
Comput Intell Neurosci. 2022 Jul 4;2022:1817990. doi: 10.1155/2022/1817990. eCollection 2022.
This paper proposes corresponding teaching methods and instructional modes based on predecessors' research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model based on DL algorithm based on an in-depth study of DL-related theories in order to accurately and scientifically analyze the problems that exist in mathematics teaching. This paper constructs an instructional quality evaluation index system based on rationality and fairness, and uses the BPNN evaluation model to train and study a set of instructional quality data. Finally, the experimental results show that this system has a high level of stability, with a 96.37 percent stability rate and a 95.42 percent evaluation accuracy rate. The results of this paper's evaluation of the mathematical instructional quality model are objective and reasonable. It can accurately assess instructional quality while also assessing problems in the teaching process based on the instructional quality scores and making reasonable recommendations for teaching improvement based on the weak links in the teaching process. It has the potential to provide a workable system for assessing instructional quality.
本文在前人对数学教学模式的研究和当前数学教学现状的基础上,提出了相应的教学方法和教学模式。此外,本文还深入研究了深度学习相关理论,构建了基于深度学习算法的教学评价模型,以便准确、科学地分析数学教学中存在的问题。本文构建了一个基于合理性和公平性的教学质量评价指标体系,并使用 BPNN 评价模型对一组教学质量数据进行训练和研究。最后,实验结果表明,该系统具有较高的稳定性,稳定性率为 96.37%,评价准确率为 95.42%。本文对数学教学质量模型的评价结果客观合理,既能根据教学质量得分准确评估教学质量,又能根据教学过程中的薄弱环节评估教学过程中存在的问题,并根据教学过程中的薄弱环节提出合理的教学改进建议。它有可能为评估教学质量提供一个可行的系统。