Genetically Optimized Ensemble Classifiers for Multiclass Student Performance Prediction.

The knowledge obtained from data can be useful for the improvement of education systems, giving rise to a research space called Educational Data Mining (EDM). EDM covers the development of methods to explore information collected from educational environments, allowing to understand students more effectively and adequately, providing better educational benefits to them. Machine learning (ML) technologies are growing considerably in recent years. The field of data mining in education provides researchers and educators with metrics of success, failure, dropout, and more, allowing students to guess. The main reason for dropping out of school is not studying. Several researchers have proposed various educational data mining techniques to predict student performance and analyzed the techniques found in educational datasets. This paper proposes a student predictive model with the use of ensemble classifiers. Initially data is pre-processed and an analysis of the correlation between the entrance attributes was carried out to identify the existence of possible redundancies between them, resulting from a very high positive correlation. The filtered attribute is trained and tested with Boosting, Bagging and Random subspace classifiers. Further to improve the accuracy of predictive model genetic algorithm is applied on three classifiers. Genetic Algorithm is an approach used to find optimized solution to search problems and it intend to increase the probability of solving the problem. The process of optimization involves selection of the best option from the available set of options to achieve the desired goal. Selection is done such that the efficiency can be maximized and error can be minimized. An analysis of the correlation between the entrance attributes was carried out to identify the existence of possible redundancies between them, resulting from a very high positive correlation. There is significant improvement in classifier accuracy, when tested mathematic and Portuguese data i.e. 3 % and 11% respectively. [ABSTRACT FROM AUTHOR]