Adaboost Ensemble with Simple Genetic Algorithm for Student Prediction Model

Predicting the student performance is a great concern to the higher education managements. This prediction helps to identify and to improve students' performance. Several factors may improve this performance. In the present study, we employ the data mining processes, particularly classification, to enhance the quality of the higher educational system. Recently, a new direction is used for the improvement of the classification accuracy by combining classifiers. In this paper, we design and evaluate a fastlearning algorithm using AdaBoost ensemble with a simple genetic algorithm called "Ada-GA" where the genetic algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance. The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early, especially in very large classes. This early prediction allows the instructor to provide appropriate advising to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results showed that this algorithm has successfully improved the detection accuracy as well as it reduces the complexity of computation.