Editorial Office of the Journal, Anhui Vocational College of City Management, Hefei 230011, China.
Institute of Psychology CAS, Beijing 100101, China.
Comput Intell Neurosci. 2021 Dec 31;2021:4123254. doi: 10.1155/2021/4123254. eCollection 2021.
The assessment of teaching quality is a very complex and fuzzy nonlinear process, which involves many factors and variables, so the establishment of the mathematical model is complicated, and the traditional evaluation method of teaching quality is no longer fully competent. In order to evaluate teaching quality effectively and accurately, an optimized GA-BPNN algorithm based on genetic algorithm (GA) and backpropagation neural network (BPNN) is proposed. Firstly, an index system of teaching quality evaluation is established, and a questionnaire is designed according to the index system to collect data. Then, an English teaching quality evaluation system is established by optimizing model parameters. The simulation shows that the average evaluation accuracy of the GA-BPNN algorithm is 98.56%, which is 13.23% and 5.85% higher than those of the BPNN model and the optimized BPNN model, respectively. The comparison results show that the GA-BPNN algorithm in teaching quality evaluation can make reasonable and scientific results.
教学质量评估是一个非常复杂和模糊的非线性过程，涉及许多因素和变量，因此数学模型的建立很复杂，传统的教学质量评价方法不再完全适用。为了有效地、准确地评估教学质量，提出了一种基于遗传算法（GA）和反向传播神经网络（BPNN）的优化 GA-BPNN 算法。首先，建立教学质量评估指标体系，并根据指标体系设计问卷收集数据。然后，通过优化模型参数建立英语教学质量评估系统。仿真表明，GA-BPNN 算法的平均评估准确率为 98.56%，分别比 BPNN 模型和优化后的 BPNN 模型高出 13.23%和 5.85%。对比结果表明，教学质量评价中的 GA-BPNN 算法可以得出合理、科学的结果。
