Strategic Planning for the Sustainable Development of Educational Enterprises: Integrating Optimization Models and Creating Innovative Learning Environments

Abstract

Due to market fluctuations caused by changes in policies regarding extracurricular education and training, education and training companies face significant development pressures. This paper collects teaching activity data from Z Education and Training Company and applies complex network theory to evaluate the course feature network by analyzing the average path length, clustering coefficient, and degree distribution of a bipartite network. Additionally, the K-means algorithm and correlation analysis methods are used to analyze learning environment data and optimize learning environment pathways. The results indicate that the average path length of the complex network for Z Education and Training Company is 2.471, with the shortest path being 1 or 7, and a clustering coefficient of 0.211. The overall network exhibits small-world scale characteristics, while the user behavior network of the education company follows a power-law distribution. Furthermore, characteristics such as the difficulty level and mastery of knowledge points provide guidance for optimizing learning environment paths. Education companies should base their strategic adjustments and optimizations on the learning situation data of their own students’ participation in teaching activities.