No abstract

Growth of an educational institute can be measured in terms of successful students of the institute. The analysis related to the prediction of students academic performance in higher education seems an essential requirement for the improvement in quality education. Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Here some significant factors have been considered while constructing the decision tree for classifying students according to their attributes (grades). In this paper four different decision tree algorithms J48, NBtree, Reptree and Simple cart were compared and J48 decision tree algorithm is found to be the best suitable algorithm for model construction. Cross validation method and percentage split method were used to evaluate the efficiency of the different algorithms. The traditional KDD process has been used as a methodology. The WEKA (Waikato Environment for Knowledge Analysis) tool was used for analysis and prediction. . Results obtained in the present study may be helpful for identifying the weak students so that management could take appropriate actions, and success rate of students could be increased sufficiently.

Student performance prediction is a great area of concern for educational institutions to prevent their students from failure by providing necessary support and counseling to complete their degree successfully. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the student's academic performance, particularly for four year engineering graduate program. To this end, five ensemble techniques based on four representative learning algorithms, namely Adaboost, Bagging, Random Forest and Rotation Forest have been used to construct and combine different number of ensembles. These four algorithms have been compared for the same number (ten) of base classifiers and the Rotation Forest is found to be the best ensemble classifiers for predicting the student performance at the initial stages of the degree program.

No abstract

No abstract