Evaluation of Classroom Teaching Quality in Universities Based on Artificial Neural Network

Yan-ming Qi, Chang-long Wang, Kai-Ti Yang
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Abstract

It is of great importance to evaluate scientifically teachers’ teaching quality in the classroom. The methods of artificial neural network were applied on evaluation of classroom teaching quality. By analyzing constituent elements of teaching quality, proposed with teaching attitude, teaching method, teaching content and teaching effectiveness as input layer, quality of classroom teaching quality as output layer, establish artificial neural network model, and actual data were used for training and practice of the network. The practice showed that the model has well recognizes the precision. Finally obtains digitized the evaluation result,can be accurate, direct-viewing reflection the classroom teaching quality was the fit or unfit, thus the wide prospect of artificial neural network in the evaluation of the classroom teaching quality were presented.
基于人工神经网络的高校课堂教学质量评价
科学评价教师的课堂教学质量具有重要意义。将人工神经网络方法应用于课堂教学质量评价。通过分析教学质量的构成要素,提出以教学态度、教学方法、教学内容和教学效果为输入层,课堂教学质量为输出层,建立人工神经网络模型,并利用实际数据对网络进行训练和实践。实践表明,该模型具有较好的识别精度。最后得到数字化的评价结果,能够准确、直观地反映课堂教学质量是否合适,从而提出了人工神经网络在课堂教学质量评价中的广阔前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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