Multilayer feedforward networks are universal approximators
Neural Networks
Predicting graduate student success: a comparison of neural networks and traditional techniques
Computers and Operations Research
Computers and Operations Research
A comparative analysis of regression and neural networks for university admission
Information Sciences: an International Journal
Statistics for Engineering and the Sciences (5th Edition)
Statistics for Engineering and the Sciences (5th Edition)
Expert Systems with Applications: An International Journal
A new method to help diagnose cancers for small sample size
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mutual complement between statistical and neural network approaches for rock magnetism data analysis
Expert Systems with Applications: An International Journal
A multilayer perceptron-based medical decision support system for heart disease diagnosis
Expert Systems with Applications: An International Journal
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Simulating wheat yield in New South Wales of Australia using interpolation and neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Students' perception on course satisfaction through student surveys has become more influential in institutional operations because their experience in study may affect not only the prospective student's decision in choosing the institution for their tertiary education, but also the retention of existing students. Student course satisfaction is a multivariate nonlinear problem. Neural network (NN) techniques have been successfully applied to approximating nonlinear functions in many disciplines, but there has been little information available in applying NN to the modelling of student course satisfaction. In this paper, based on the student survey results collected from 43 courses in 11 semesters from 2002 to 2007, statistical analysis and NN techniques are incorporated for establishing some dynamic models for analysing and predicting student course satisfaction. The factors identified from this process also allow new strategies to be drawn for improving course satisfaction in the future. This study shows that both the number of students (NS) enrolled to a course and the high distinction (HD) rate in final grading are the two most influential factors to student course satisfaction. The three-layer multilayer perceptron (MLP) models outperform the linear regressions in predicting student course satisfaction, with the best outcome being achieved by combining both NS and HD as the input to the networks.