Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/161797
Authors: 
Jishan, Syed
Rashu, Raisul
Haque, Naheena
Rahman, Rashedur M.
Year of Publication: 
2015
Citation: 
[Journal:] Decision Analytics [ISSN:] 2193-8636 [Volume:] 2 [Year:] 2015 [Issue:] 1 [Pages:] 1-25
Abstract: 
There is a perpetual elevation in demand for higher education in the last decade all over the world; therefore, the need for improving the education system is imminent. Educational data mining is a newly-visible area in the field of data mining and it can be applied to better understanding the educational systems in Bangladesh. In this research, we present how data can be preprocessed using a discretization method called the Optimal Equal Width Binning and an over-sampling technique known as the Synthetic Minority Over-Sampling (SMOTE) to improve the accuracy of the students' final grade prediction model for a particular course. In order to validate our method we have used data from a course offered at North South University, Bangladesh. The result obtained from the experiment gives a clear indication that the accuracy of the prediction model improves significantly when the discretization and over-sampling methods are applied.
Subjects: 
Educational data mining (EDM)
Classification
Naive
Decision tree
Neural network
Discretization
Equal width binning
Over-sampling
SMOTE
Class imbalance
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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