| 摘 要: 对高校学生学习过程进行准确的评价,是提升学生学习效率、改进教师教学方法、完善学校教学管理的 重要环节。目前已经提出了多种数学模型来解决该问题,但这些方法均需要一定的先验知识且难以实现自学习。本文利 用SOM模型能在无监督、无先验知识的状态下对样本进行自组织的特性进行学习过程的评价,同时通过主成分分析, 提高了网络收敛速度和聚类准确性。实例分析表明:改进SOM模型能有效地进行学生学习过程的评价。 |
| 关键词: SOM模型;学习评价;聚类分析;主成分分析 |
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中图分类号: TP183
文献标识码: A
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| Study on the Evaluation of College Students' Learning Process Based on Improved SOM Model |
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MA Shouming, ZHENG Wu, CHENG Chen, ZHOU Yi
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(School of Networks & Telecommunications Engineering, Jinling Institute of Technology, Nanjing 211169, China )
mashouming@jit.edu.cn; zhengwu@jit.edu.cn; ngchen@jit.edu.cn; monday9086@jit.edu.cn
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| Abstract: The accurate evaluation of the learning process of college students is an important link in the improvement of students' learning efficiency, teachers' teaching methods and school teaching management. The existing evaluation methods of learning process mostly rely on accurate mathematical models, which cannot realize self-learning. In this paper, the SOM (Self Organizing Maps) model was used to evaluate the learning process of samples in an unsupervised state without prior knowledge. Meanwhile, through PCA (principal component analysis) algorithm, the convergence speed and clustering accuracy of the network can be improved. The case analysis shows that the improved SOM model can effectively evaluate students' learning process. |
| Keywords: SOM model; learning evaluation; cluster analysis; principal component analysis |
