Acceso usuarios
Shan Wang
With the rapid development of information technology, big data has penetrated into various fields, providing unprecedented convenience for decisionmaking. This study takes big data as the perspective to deeply explore the planning path of ideological and political education for college students. By optimizing the wolf pack algorithm, we propose a more concise and flexible parameter control strategy, which is an improved wolf pack algorithm based on the elite strategy. This algorithm is applied to solve efficient teaching problems, seek the best teaching strategies, meet the actual needs of students, and achieve time-saving and low-cost learning effectiveness improvement. Our goal is to build a sustainable ideological education and teaching system to improve the learning situation of students. The experimental results show that the improved wolf pack algorithm simplifies the processing flow and effectively controls parameters, providing new ideas and methods for the path planning of ideological and political education for college students.
