Student Performance Prediction

In education system it is necessary to build the system that can keep students performance which accurately, predicts students future performance. Existing approach shows difficulty to cover the diversities of students educational background and it became a critical for undefined set of course. In case of relevant courses students background information is consider which helps to estimate the relevant course in a term. Traditional existing system, Intelligent Tutoring system and Massive open online courses are working on past records. It is a difficult or hard task to predict the student performance over on-going records so, a prediction model required. Here, it uses machine learning approach for prediction of performance which speeds up the performance and also reduces the computation time. We develop algorithm for making predictions based on students progressive performance states and intermediate tests. This type of model is consists of two layers of predictor which is named as base predictor and the ensemble predictor for performance evaluation. The performance is gradually increases the accuracy of the prediction and classification more precisely.