학술논문

Development of Primary School Mathematics Education Technology Resources based on Fuzzy Clustering Algorithm
Document Type
Conference
Source
2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) Smart Technologies For Smart Nation (SmartTechCon), 2023 Second International Conference On. :72-76 Aug, 2023
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Systematics
Sensitivity
Clustering algorithms
Educational technology
Linear programming
Mathematics
Real-time systems
Fuzzy clustering algorithm
primary school mathematics
educational technology
resource development
Language
Abstract
India's compulsory primary education emphasizes mathematics. Math helps boost students' logical and rational thinking. It supports our nation's scientific development and modern students' scientific and cultural growth. Students learn elementary school mathematics by facing a lot of data, thinking, and checking results. Teaching materials are not the only educational technology in the primary school mathematics teaching reform. Math teachers have started to focus on the need for more teaching resources and new tools and technology. Due to the lack of complete and systematic teaching materials, primary school mathematics technical resource development has new goals and requirements. Resource mining with fuzzy clustering is great. Fuzzy clustering algorithm can introduce fuzzy mathematics into cluster analysis, use membership function clustering to determine sample affinity, and analyze and aggregate a large number of text resource information into meaningful classes or clusters for primary school mathematics education technology resource mining and development. An optimization strategy for cluster number is given to meet classic fuzzy clustering algorithm initialization requirements. The optimized fuzzy clustering algorithm optimizes the initial clustering center, overcoming its sensitivity to the beginning value. Thus, the data set is fuzzy-divided unsupervised. This article analyzes created resources to inform primary school campus mathematics education development.