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A Predictive Student Performance Analytics Scheme using Auto-Adjust Apriori Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 157 - Number 6 |
| Year of Publication: 2017 |
| Authors: Himanshu Maniar, S. O. Khanna |
10.5120/ijca2017911729
|
Himanshu Maniar, S. O. Khanna . A Predictive Student Performance Analytics Scheme using Auto-Adjust Apriori Algorithm. International Journal of Computer Applications. 157, 6 ( Jan 2017), 1-4. DOI=10.5120/ijca2017911729
Abstract
Every academic organization needs to analyze student performance to find its overall strengths and weaknesses. At the same time, analysis helps to find out strengths and weaknesses of students along with their interests and dislikes. Any large organization with a large number of students has a large amount of result data. This data needs to be processed to find information related to student’s performance. This paper presents Auto Adjust Apriori based student’s results analysis scheme to predicate student’s future performance. In any course, certain courses are interrelated with each other. Using this scheme, students and teachers can able to find which subjects will be more difficult in future based on student’s performance in current subjects. The scheme has been implemented under .Net technology.
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