A Predictive Model for Predicting Students Academic Perfomance

predicting students' academic performance in advance is of great importance for parents, management of higher education institutions and the student itself. Selection of a right academic program at right time can save time, efforts and resources of both parents and educational institutions. To achieve this goal, an intelligent decision support system (MSS) is essential to predict students' performance prior to their admissions in any academic program or getting promoted to the higher classes in an academic program. Scope of this work is to first identify key features, influencing students' performance, and then develop an accurate predication model for prediction of their performance, prior to taking admission in an intended program or deciding to continue for higher classes and semesters in the same program or to quit the program at this stage. In this study, first, a subjective method is used for identification of academic and socio-economic features to develop the prediction model and then a decision tree-based algorithm, Logistic Model Trees (LMT), is adopted to learn the intrinsic relationship between the identified features and students' academic grades. The proposed model is trained and tested on a real-world dataset of 1,021 records, collected from examination database of the University of Peshawar. Simulation of the results is performed in Weka 3.8 environment with its default parameters and 10-folds cross validation setting. The proposed system achieved predictive accuracy of 83.48%,which guides parents, management of higher education institutions and students itself to decide whether they should go forward or quit this program at this stage.