A Fuzzy Probabilistic Neural Network for Student’s Academic Performance Prediction

The paper presents a Neural Network model for modeling academic profile of students. The proposed model allows prediction of students’ academic performance based on some of their qualitative observations. Classifying and predicting students’ academic performance using arithmetical and statistical techniques may not necessarily offer the best way to evaluate human acquisition of knowledge and skills, but a hybridized fuzzy neural network model successfully handles reasoning with imprecise information, and enables representation of student modeling in the linguistic form the same way the human teachers do. The model is designed, developed and tested in MATLAB and JAVA which considers factors like age, gender, education, past performance, work status, study environment etc. for performance prediction of students. A Fuzzy Probabilistic Neural Network model has been proposed which enables the design of an easy-to-use, personalized student performance prediction component. The results of experiments show that the model outperforms traditional back-propagation neural networks as well as statistical models. It is also found to be a useful tool in predicting the performance of students belonging to any stream. The model may provide dual advantage to the educational institutions; first by helping teachers to amend their teaching methodology based on the level of students thereby improving students’ performances and secondly classifying the likely successful and unsuccessful students.