Article Details

Abstract

Teaching diagnosis is a crucial component of educational activities. To address the challenges of traditional teaching diagnosis, such as heavy workload, low efficiency, and susceptibility to various human factors, this paper explores the application of Self-Organizing Map (SOM) neural networks in the teaching diagnosis of the single-chip microcomputer course in vocational schools. Through MATLAB simulations, it is found that after a certain number of training iterations, the SOM neural network can effectively extract features from the input data for clustering, clearly presenting the structural patterns of single-chip microcomputer competency among different student groups. This lays the groundwork for targeted capability enhancement strategies in subsequent stages.

Keywords

SOM neural network; vocational school; Single-chip Microcomputer course; teaching diagnosis

References

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About the authors

Yu Cheng (1984.11-), male, Han nationality, from Ningbo, Zhejiang, holds the title of associate professor and a doctoral degree. His research interests include educational technology, electronic information education, and pattern recognition.