The Research of Teaching Quality Evaluation Model Based on the Principal Component Analysis and Learning Vector Quantization

Yang Ning, Kong Dehao
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引用次数: 1

Abstract

To further improve the accuracy of teaching quality evaluation, a teaching quality evaluation model based on the combination of principal component analysis (PCA) and learning vector quantization (LVQ) is proposed. The teaching quality evaluation system is established using analytic hierarchy process (AHP), and then the characteristic information of the initial evaluation index system is extracted using principal component analysis. The characteristic information after dimensionality reduction is input to the LVQ neural network, and the network model is trained and tested for generalization ability. The experimental results show that the PCA-LVQ network model is simpler in structure, stronger in learning ability, faster in convergence speed, higher in evaluation accuracy and generalization ability than the single LVQ and BP neural network.
基于主成分分析和学习向量量化的教学质量评价模型研究
为了进一步提高教学质量评价的准确性,提出了一种基于主成分分析(PCA)和学习向量量化(LVQ)相结合的教学质量评价模型。运用层次分析法(AHP)建立了教学质量评价体系,然后运用主成分分析法提取了初始评价指标体系的特征信息。将降维后的特征信息输入到LVQ神经网络中,对网络模型进行训练和泛化能力测试。实验结果表明,与单一LVQ和BP神经网络相比,PCA-LVQ网络模型结构更简单,学习能力更强,收敛速度更快,评估精度和泛化能力更高。
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