The application of fuzzy clustering in teacher-evaluating model
Nianyun Shi, Kun Long Chen, Chunhua Li · 2009
In existing teacher-evaluating model, teachers are ranked according to the weighted average score given by students based on the instruction pointers. Although it can reflect the teaching standard in a manner, yet it cannot discover the implicit information in data, such as some teachers are standout in some way. Further more, teachers cannot be sorted by a certain index in this method, which lacks for a more in-depth analysis for sorting data. In this paper, a new method is proposed based on the existing teacher-evaluating model. By taking fuzzy clustering into consideration, this method analyzes existing data deeply to discover the rules implicit in data, and then gives a division of fuzzy equivalence classes. And each class has a reasonable evaluation and interpretation which can be as an authority for evaluating teaching standard.