Abstract
In order to solve the reliability of the evaluation results of teaching quality in universities and colleges, an improved model of teaching evaluation based on the Support vector machine was put forward. In this model, the evaluator does not need to give an evaluation result of the teacher’s teaching quality, but gives the score of each evaluation index, and then calls the Support vector machine, automatic classification of teachers’ teaching quality. The experiment proves that the improved algorithm can improve the teaching quality evaluation accuracy and the result is better.
Highlights
Teaching quality evaluation is a multi-objective and multi-level optimization work, which is a guarantee system for improving teaching quality and cultivating talents [1,2,3]
The binary tree Support vector machine classification Algorithm (BT-SVMs) divides classes into 2 classes, subclasses are further divided into two subclasses, and so on, until all nodes contain only a single class, a binary SVM classifier is trained at each non-leaf node
The fuzzy binary tree Support vector machine (BT-fuzzy support vector machine (FSVM)) algorithm is a combination of modular Support vector machine and Support vector machine, the Algorithm first constructs the appropriate tree structure according to the training samples according to the construction method of the Support vector machine tree structure, and carries on the mold processing to the samples at each node, the decision function is obtained by using the fuzzy Support vector machine
Summary
Teaching quality evaluation is a multi-objective and multi-level optimization work, which is a guarantee system for improving teaching quality and cultivating talents [1,2,3]. The commonly used evaluation methods of teaching quality in universities colleges mainly include: Multiple Linear Regression, grey correlation, analytic hierarchy and process, neural network, fuzzy clustering analysis and Support vector machine, etc [5,6]. With strong ability of classification, neural network can improve the teaching quality accuracy, but in the application process, due to its limitation and easy falling into the extreme point, the result will be inconsistent with the actual result, and inefficient. Support Vector machine is a kind of machine learning method with small sample and strong nonlinear approximation ability, but in the evaluation of class teaching quality in universities and colleges, the evaluation accuracy is closely related to the parameters selected. To improve the evaluation accuracy of class teaching quality in Universities and Colleges, concerned with the method of evaluation, an improved teaching evaluation model. The results show that the evaluation accuracy can be improved with the modified algorithm, and better evaluation results are got
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