Big Data Analytics: Student Performance Prediction Using Feature Selection and Machine Learning on Microsoft Azure Platform
Keywords:Big Data Analytics, Feature Selection, Microsoft Azure, Student Prediction,
AbstractIn recent years, big data analytics has been a new growing research area and the essence of cloud computing is used to support a shared pool of resources. In educational mining, the huge volume of student data needs analytics technologies to extract valuable knowledge. It has been recognized that a high performance accuracy of student prediction model will be helpful for student and stakeholders. In this experiment, feature selection methods were proposed to identify the most significant and intrinsic features before classification methods were used. Experiment was conducted to evaluate the performance of the prediction model. The result of the experiment showed that mutual information in feature selection method with neural network classifier gave the best overall accuracy at 90.60% for student’s data at Rajamangala University of Technology Thanyaburi. This experiment is extremely useful for students, teachers and management to find useful knowledge not only in identifying the problem areas and reasons that affect student’s performance, but also in understanding the feature selection and classification methods, which are the most effective way to analyze student’s performance on a cloud computing environment.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.