Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (17): 235-238.

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Teaching quality evaluation model based on neural network and analytic hierarchy process

FENG Yingying, YU Gan, ZHOU Hongzhi   

  1. College of Information Engineering, Fuyang Teachers College, Fuyang, Anhui 236041, China
  • Online:2013-09-01 Published:2013-09-13


冯莹莹,于  干,周红志   

  1. 阜阳师范学院 信息工程学院,安徽 阜阳 236041

Abstract: In order to improve the evaluation accuracy of teaching quality, this paper proposes a teaching quality evaluation method based on analytic hierarchy method and neural network. AHP is used to build the evaluation index system, it selects the important evaluation index as the input of  BP neural network. The neural network is used to establish the teaching quality evaluation model. The simulation results show that the proposed method can not only simplify the structure of neural network, bu also improve the evaluation accuracy and evaluation efficiency of teaching quality, it is a feasible, effective teaching quality evaluation method.

Key words: analytic hierarchy process, artificial neural network, teaching quality, evaluation model

摘要: 为提高教学质量评价准确性,提出一种基于层次分析法和神经网络相融合的教学质量评价方法(AHP-BPNN)。采用层次分析法构建评价指标体系,筛选出对评价结果有重要影响的指标作为BP神经网络输入,采用神经网络建立教学质量评价模型。仿真结果表明,AHP-BPNN不仅简化神经网络的结构,而且提高了教学质量的评价精度和评价效率,是一种可行、有效的教学质量评价方法。

关键词: 层次分析法, 人工神经网络, 教学质量, 评价模型