Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction

Expert Systems with Applications

Volumn 37, Issue 4, 2010, Pages 3358-3365

  Guo, William W ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

Linear regression; Multilayer perceptron; Neural networks; Nonlinear function approximation; Student course satisfaction

Indexed keywords

INFLUENTIAL FACTORS; MULTI LAYER PERCEPTRON; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; NEW STRATEGY; NONLINEAR FUNCTION APPROXIMATION; NONLINEAR FUNCTIONS; NONLINEAR PROBLEMS; STATISTICAL ANALYSIS; STUDENT SURVEYS; TERTIARY EDUCATION; THREE-LAYER;

EDUCATION COMPUTING; ELECTRIC LOADS; LINEAR REGRESSION; LINEARIZATION; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NONLINEAR ANALYSIS; STUDENTS; SURVEYS;

TEACHING;

EID: 71549166008     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.10.014     Document Type: Article

References (29)

1. Bain K. What the best college teaching do (2004), Harvard University Press, Cambridge
2. Barrie S., Ginns P., and Symons R. Rewarding and recognising quality teaching and learning in higher education: Student surveys on teaching and learning (2007), Carrick Institute, Sydney
3. Black K., Asafu-Adjaye J., Khan N., Perera N., Edwards P., and Harris M. Australian business statistics (2007), John Wiley and Sons, Australia: Milton, Qld
4. Curry B., and Morgan P.H. Model selection in neural networks: Some difficulties. European Journal of Operational Research 170 (2006) 567-577
5. Demuth H., and Beale M. Neural network toolbox for use with Matlab (2004), The MathWorks, Natick, MA
6. Demuth H., Beale M., and Hagan M. Neural network toolbox 5 (2007), The MathWorks, Natick, MA
7. Flitman A.M. Towards analysing student failures: Neural networks compared with regression analysis and multiple discriminant analysis. Computers & Operations Research 24 (1997) 367-377
8. Gaudart J., Giusiano B., and Huiart L. Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data. Computational Statistics and Data Analysis 44 (2004) 547-570
9. Gorr W.L., Nagin D., and Szczypula J. Comparative study of artificial neural network and statistical models for predicting student grade point averages. International Journal of Forecasting 10 (1994) 17-34
10. Guo W.W., Li M.M., Whymark G., and Li Z.X. Mutual complement between statistical and neural network approaches for rock magnetism data analysis. Expert Systems with Applications 36 (2009) 9678-9682
11. Hagan M.T., and Menhaj M. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5 (1994) 989-993
12. Hagan M.T., Demuth H.B., and Beale M.H. Neural network design (1996), PWS Publishing, Boston
13. Hardgrave B.C., Wilson R.L., and Walstrom K.A. Predicting graduate student success: A comparison of neural networks and traditional techniques. Computers & Operations Research 21 (1994) 249-263
14. Hillman K. The first year experience: The transition from secondary school to university and TAFE in Australia (2005), Australian Council for Educational Research (ACER), Victoria
15. Hobsons. The good university guide (2008), Victoria, Hobsons
16. Hornik K., Stinchcomb M., and White H. Multilayer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-366
17. Li D.C., Wu C.S., Tsai T.I., and Lina Y.S. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Computers & Operations Research 34 (2007) 966-982
18. Li D.C., Hsu H.C., Tsai T.I., Lu T.J., and Hu S.C. A new method to help diagnose cancers for small sample size. Expert Systems with Applications 33 (2007) 420-424
19. Lizzio A., Wilson K., and Simons R. University students' perceptions of the learning environment and academic outcomes: Implications for theory and practice. Studies in Higher Education 27 (2002) 27-52
20. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 11 (1963) 431-441
21. Mendenhall W., and Sincich T. Statistics for engineering and the sciences. 5th ed. (2007), Upper Saddle River, Pearson Prentice Hall
22. McInnis G., Griffin P., James R., and Coates H. Development of the course experience questionnaire (CEQ) (2000), Department of Education, Training and Youth Affairs, Commonwealth of Australia, Canberra
23. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. Parallel distributed processing (1986), MIT Press, Cambridge
24. Siripatrawan U., and Jantawat P. A novel method for shelf life prediction of a packaged moisture sensitive snack using multilayer perceptron neural network. Expert Systems with Applications 34 (2008) 1562-1567
25. Somasundaram J., Bowser D., and Danaher P.A. Guest editors' introduction to special theme issue: Retaining attrition? Studies in learning. Evaluation Innovation and Development 2 (2005) 1-6
26. Stathacopoulou R., Grigoriadou M., Samarakou M., and Mitropoulos D. Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model. Expert Systems with Applications 32 (2007) 955-975
27. Walczak S., and Sincich T. A comparative analysis of regression and neural networks for university admissions. Information Sciences 119 (1999) 1-20
28. White H. Some asymptotic results for learning in single hidden layer feedforward network models. Journal of the American Statistical Association 84 (1989) 1008-1013
29. Yan H., Jiang Y., Zheng J., Peng C., and Li Q. A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications 30 (2006) 272-281