The Design of College Student Achievement Management System based on GA-BP Network

Abstract

Abstract With the development of computer technology, higher education and teaching work gradually depend on the score management system. The performance management system has a substantial amount of student curriculum performance data, and how to effectively analyze these data to improve the teaching quality of the school is a problem worthy of in-depth study. This study integrates a genetic algorithm and a neural network on the basis of the BP neural network to create a GA-BP network hybrid algorithm that addresses the BP neural network's issues, such as its proclivity to fall into local minima and slow convergence speed. The GA-BP network hybrid algorithm improves the connection weights and thresholds of the BP network using the genetic algorithms global search capability so that the network can search from a better initial value and complete the global optimization more quickly. According to the needs of student achievement management, this research designed and developed a student achievement management system and realized the application of the GA-BP network in the student achievement management system. Through the experiments of MATLAB software simulation, this study has verified the feasibility of the model applied to the graduation score prediction. The results show that the model can make a more accurate prediction of the unknown graduation scores by using the existing student course scores, so as to use the prediction results to carry out academic warning prompts for students, help students strengthen their professional knowledge learning, and help colleges and universities adjust their education and teaching work.