Hybrid Element Heuristic Algorithm Optimizing Neural Network-Based Educational Courses

Abstract

The rapid development of computer networks has enabled information technology to penetrate many fields, providing unprecedented opportunities for all aspects of our lives. In order to allow students to acquire necessary knowledge and skills through efficient learning, this article studies the design and development of educational technology courses based on hybrid metaheuristic algorithms to optimize neural networks. This paper proposes a metaheuristic algorithm and explains the simulated annealing algorithm and microregular annealing algorithm in detail. Using these algorithms, a mathematical model of the normal scheduling problem was also constructed, and the mathematical model was applied to the design and development of educational technology courses. In addition, the neuron model in the neural network and the activation function of the neural network are discussed from various aspects. In the experiment, according to the needs of students, a learning platform for educational technology courses was designed and developed. The experimental results of this article show that there are significant differences in the starting point of the ability level of learners for different majors. Education majors have a higher level of understanding of educational technology courses; 38.20% of students know well, while art majors have a low level of understanding of education technology courses, with 36.05% of students majoring in art.