Abstract

What determines academic performance? Multivariate adaptive regression splines (MARS) technique is an adaptive non-parametric regression approach which has been used for various forecasting and data mining applications in recent years. MARS is flexible regression technique that uses a modified recursive partitioning strategy to simplify high dimensional problems into smaller yet highly accurate models. This technique is more useful when a large number of explanatory variable candidates need to be considered. In this paper, the MARS technique is applied to predict the student performance in the entrance examination in the engineering admission in ANNA UNIVERSITY, INDIA. The study has implications for the Engineering’s admission policy. The results should help us to identify an optimal set of admission indicators, which have the potential of predicting students’ performance. And also the effectiveness of MARS is demonstrated on a data set taken from literature. Its performance is compared with that of multiple linear regressions (MLR) in terms of normalized root mean square error (NRMSE) obtained on test data. Based on the experiments performed, it is observed that the MARS outperformed with MLR.