Abstract
Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm.
Highlights
The goal of teaching quality evaluation [1,2,3,4] is to promote teaching reform, improve teaching quality, reduce student burden [5,6,7], develop students’ intelligence [8], and help students evaluate and solve problems
In colleges and universities, teaching quality is often assessed through four channels: student evaluation, expert evaluation, peer evaluation, and instructor self-evaluation, with the final evaluation results synthesized
Teaching quality evaluation consists of four parts: leader evaluation, expert evaluation, peer evaluation, and student evaluation
Summary
The goal of teaching quality evaluation [1,2,3,4] is to promote teaching reform, improve teaching quality, reduce student burden [5,6,7], develop students’ intelligence [8], and help students evaluate and solve problems. In colleges and universities, teaching quality is often assessed through four channels: student evaluation, expert evaluation, peer evaluation, and instructor self-evaluation, with the final evaluation results synthesized. Certain issues remain in the process of developing, utilizing, and evaluating the teaching quality assessment system’s evaluation outcomes [3, 4], such as evaluation theory research [11], evaluation method usage, evaluation method update, and evaluation data analysis. These issues have a direct impact on educational institutions. Quality assessment and knowledge extraction will be critical [12]
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