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|Title||Prediction of Student's Performance Using Modified KNN Classifiers|
The Educational field research using Data Mining techniques increased rapidly. The aim of Data Mining on Educational data is to discover the hidden patterns and knowledge about student's performance. This study focus on predicting the student's performance according to their marks through classification using modified KNN classifiers such as Cosine KNN, Cubic KNN, and Weighted KNN. The Dataset of the students of the 11 th Grade of scientific branch at Gaza Strip secondary schools contains 13 parameters, 11 parameters of the subject's marks, average parameter and Grade parameter. The classifiers should predict the performance (Grade) the student will gain depending on the marks of two subjects. The early prediction of the student's grade can help the principals to take decisions in order to help schools to initially identify students with low academic achievement and find ways to support them.
|Published in||The First International Conference on Engineering and Future Technology (ICEFT 2018)|
|Publisher||University of Palestine|
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