E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score
Muhammad Usman 1, Muhammad Munwar Iqbal 1 * , Zeshan Iqbal 1, Muhammad Umar Chaudhry 2, Muhammad Farhan 3, Muhammad Ashraf 2
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1 University of Engineering and Technology, Taxila, Pakistan
2 Sungkyunkwan University, Suwon, South Korea
3 COMSATS Institute of Information Technology, Sahiwal, Pakistan
* Corresponding Author

Abstract

Machine Learning is a scientific discipline that addresses learning in context is not learning by heart but recognizing complex patterns and makes intelligent decisions based on data. Currently, students have to face the problem of selecting the best suitable university for admission in engineering. There is no predictor system that recommends the students to select the specific category which is best to its academic career. Students have to first appear in the entry test and can’t predict whether he/she can pass the entry test to get admitted in University. To tackle this problem the field of Machine Learning develops algorithms that discover knowledge from specific data and experience, based on sound statistical and computational principles. After going through the entry test students have to face problems for selecting the preferences among different categories due to the lack of knowledge of intake merits of preceding years. Another problem arises when students are waiting for admission in specific university, meanwhile, other universities finish their admission processes and select the students, but some students can’t take admission in any university due to no prediction system for admission in universities. In this work, we would like to develop an E-Assessment and Computer-Aided Prediction online system that enables the student to predict the entry test numbers by giving the Metric and Intermediate marks and other academic numbers. The suggested scheme has been demonstrated to perform at the maximum speed under MATLAB setup.

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.12973/eurasia.2017.00939a

EURASIA J Math Sci Tech Ed, 2017 - Volume 13 Issue 8, pp. 5499-5517

Publication date: 10 Jul 2017

Article Views: 512

Article Downloads: 139

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