Performance Management of Education and Teaching Reform Based on Convolutional Neural Network

Abstract

The teaching reform of higher education has always attracted much attention, and higher education has been the key object of the national education reform. The quality of higher education, the quality of talent training, the quality of scientific research, the quality of social service, and the structure to produce characteristics must all be improved. The success of higher education teaching reform is not only related to the quality of university personnel training and important task of serving the society, but also related to connotative development of universities and sustainable development of higher education. Education and teaching reform is an all-round and systematic project, which involves reforms in different aspects, such as teaching management, teacher teaching, professional setting, and curriculum development. The outcome of the extended efforts and practical advancements in education teaching reforms is to evaluate the effects and achievements of these reforms. However, the reforms and achievements of education and teaching are much higher and effective in comparison to improvements in theoretical research and practical experience. Therefore, this work is based on the CNN (Convolutional Neural Network) and designed an intelligent model for the evaluation and management of peer-to-peer education and teaching reform achievements. This work contributes in the following three manners: (I) It includes construction of a deep one-dimensional CNN and sends relevant data of college education and teaching reform into network with the objective to realize adaptive feature extraction and high-accuracy evaluation. (II) It also realizes the management of higher education teaching quality. (III) It aims to show a greater impact of hyperparameters in the CNN performance, genetic algorithm. This proposition is to optimize hyperparameters. It proves the validity and authenticity of this work through systematization, logic, and experiment.