2020 ◽  
Vol 39 (4) ◽  
pp. 5547-5558
Author(s):  
Peng Fan

In this paper, the author introduces the theory of fuzzy mathematics into the evaluation of higher education. By determining the set of evaluation factors and comments, the author constructs the relevant mathematical model and processes the data, thus turning the evaluation problem into the multiplication problem of the fuzzy matrix. Deep learning is a very active branch of machine learning research in recent years. By increasing the depth and breadth of the model, i.e. increasing the number of operations from the input end to the output end and the number of channels of the model, the scale of parameters of the model is increased, so that the model has the ability to express complex functions. It is appropriate to use deep learning in teaching quality evaluation. The simulation results show that the deep learning model is very effective in dealing with data diversity and extracting complex implicit rules. It can effectively model experts’ professional knowledge and experience. Deep neural network has powerful expressive ability, and can effectively extract the deep-seated laws affecting the teaching quality. It can be used as an assistant technology for the evaluation of teaching quality in Colleges.


2014 ◽  
Vol 651-653 ◽  
pp. 2437-2440 ◽  
Author(s):  
Wu Xue Jiang ◽  
Yin Zhen Zhong ◽  
Huan Liang

For improving the quality of vocational education evaluation validity, this paper introduces the research progress of teaching evaluation, use data mining technology to put forward a feasible model of higher vocational teaching quality evaluation, construct a decision tree has practical significance. By the method used, the traditional Apriori algorithm is improved decision tree to improve the teaching contents to the location of the second master node, reflecting the characteristics of vocational teaching practice. Case analysis indicated, the decision tree has a good operability and high credibility.


2011 ◽  
Vol 271-273 ◽  
pp. 1451-1454
Author(s):  
Gang Zhang ◽  
Jian Yin ◽  
Liang Lun Cheng ◽  
Chun Ru Wang

Teaching quality is a key metric in college teaching effect and ability evaluation. In many previous literatures, evaluation of such metric is merely depended on subjective judgment of few experts based on their experience, which leads to some false, bias or unstable results. Moreover, pure human based evaluation is expensive that is difficult to extend to large scale. With the application of information technology, much information in college teaching is recorded and stored electronically, which founds the basic of a computer-aid analysis. In this paper, we perform teaching quality evaluation within machine learning framework, focusing on learning and modeling electronic information associated with quality of teaching, to get a stable model described the substantial principles of teaching quality. Artificial Neural Network (ANN) is selected as the main model in this work. Experiment results on real data sets consisted of 4 subjects / 8 semesters show the effectiveness of the proposed method.


2012 ◽  
Vol 591-593 ◽  
pp. 2186-2189 ◽  
Author(s):  
Xiao Hong Zhu

The college teaching quality evaluation is a multi-factor, multi-variable fuzzy nonlinear process. This paper applied BP neural network to setup a teaching quality evaluation model according to the expert group standards, and trained the neural network model through MATLAB7.0 for learning complex knowledge and simulating capabilities. The data test validation shows that the evaluation result agreed to the actual teaching effectiveness, and has a wide range of applications in various types of teaching management.


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