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

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.