K Nearest Neighbour and Improved Artificial Neural Network Techiniques for Student Academic Performance Prediction

Abstract

Educational Data Mining has been an emerging topic nowadays due to the growth of educational data. This field makes it possible to develop methods in order to find out hidden patterns from educational data. The methods extracted from Educational Data Mining discipline are then used to understand students including their learning behavior as well as to predict their academic performance. In recent work used artificial neural network to predict academic performance. However in recent work does not used any proper method for missing value imputation and this will affect prediction performance. To mitigate the above mentioned issue in this work introduced an improved framework for student academic performance prediction. In which pre-processing is done by using data formatting, K Nearest Neighbour (KNN) based missing data imputation, min max normalization based data normalization and data filtering methods. Once the data is enhanced using pre-processing, it will be send to the classifier for Student academic performance prediction which is done by using Improved Artificial Neural Network (IANN) method. Experimental results demonstrate the effectiveness of the proposed model interms of precision, recall and f-measure.