Student-Centered Learning Environment Based on Multimedia Big Data Analysis

Abstract

The application of current information technology in education, especially multimedia network technology, has brought about major changes in the content and methods of instruction. It has replaced the conventional teacher-centered, textbook-centered, and classroom-centered teaching environment with a student-centered, information technology-based learning environment that includes a rich network of multimedia learning resources and virtual reality. Through the interaction between students and students and between students and the learning environment, students can acquire knowledge on the basis of observation, understanding, and cognition, so as to grasp the essence of things. It is an effective cognitive tool for students to explore freely and visualize various knowledge and skills. Therefore, the teacher is no longer the authority of knowledge imparting, but the learner’s guide and helper or even the senior partner in the learner’s learning activities. This shift will allow teaching staff to focus more on the design and development of learning environments and resources. This paper proposes a new clustering algorithm CURE, which overcomes the shortcomings of the detection rate and stability of the classical clustering algorithm and is suitable for solving the clustering problem in the learning environment of big data analysis. Experiments are carried out on some international standard network security dataset KDDCUP101, and the running time of the algorithm is 1230 s. The results show that the stability of the proposed algorithm is increased by 30.22% and the detection rate is increased by 10.98% compared with the common algorithm. Compared with the global K-means algorithm, the time complexity is also greatly enhanced.

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