Hybrid data clustering algorithm and interactive experience in E-learning electronic course simulation of legal education

Abstract

With the development of multimedia technology, interactive entertainment experience is an important feature of online teaching mode. Students can engage in interactive learning through various mobile apps and online tools. E-learning provides an innovative way of learning that enables distance learning through electronic course simulation. The objective of this research is to design and develop an improved E-learning course simulation for law teaching by applying hybrid data clustering algorithms and enhancing interactive experiences to improve student engagement and learning outcomes. This article analyzes the simulation of legal education E-learning electronic courses based on mixed data clustering algorithms and interactive experiences. On the basis of the mixed data clustering model of the k-means algorithm, we propose a probability based mixed clustering model that can effectively handle the clustering problem of mixed data. Based on this algorithm, we have established a legal teaching network system platform based on artificial intelligence. The hybrid data clustering algorithm solves the instability problem of the k-means algorithm in the construction of the legal teaching network course platform, providing a reliable solution for the development of artificial intelligence network courses. Based on teaching concepts and combined with various technological characteristics such as network technology and multimedia technology in the information age, users who pass through this teaching system can fully utilize the teaching resources and various functions in the system. The hybrid data clustering algorithm proposed in this article has good clustering performance.