DocumentCode
3236577
Title
PSO-based neural network model for teaching evaluation
Author
Zhu, Changjun ; Zhao, Xiujuan
Author_Institution
Coll. of Urban Constr., Hebei Univ. of Eng., Handan, China
fYear
2009
fDate
25-28 July 2009
Firstpage
53
Lastpage
55
Abstract
At present, with the popularization of higher education and acceleration of college students every year, colleges and universities are increasing scale. Expansion of the scale for colleges and universities on the one hand, broadens the scope for development, on the other hand it also brings many problems, including the issue of the quality of teaching which is particularly prominent. Owing to the problems existing in the previous system of teaching quality, based on the teaching characteristics, a new PSO-based teaching quality evaluation model is set up by means of PSO theory and neural network method. And the application processes of the model are illuminated in detail. The model is applied into the evaluation of teaching quality. By analyzing a lot of practical examples, the experiment result indicates that this mathematical model has better appraisal effect than other appraisal model, which can overcome the complexity of traditional evaluation model. Compared with other methods, this method is scientific, simple and operable. And its structure and method will have a bright future.
Keywords
neural nets; particle swarm optimisation; teaching; college students; higher education; neural network model; particle swarm optimization; teaching quality evaluation model; Acceleration; Appraisal; Birds; Computer science education; Educational institutions; Fuzzy logic; Fuzzy neural networks; Industrial training; Mathematical model; Neural networks; BP neural network; PSO; evaluation; teaching quality;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science & Education, 2009. ICCSE '09. 4th International Conference on
Conference_Location
Nanning
Print_ISBN
978-1-4244-3520-3
Electronic_ISBN
978-1-4244-3521-0
Type
conf
DOI
10.1109/ICCSE.2009.5228525
Filename
5228525
Link To Document