A Predictive Student Performance Analytics Scheme using Auto-Adjust Apriori Algorithm | Zendy

Himanshu Maniar | Zendy; S. O. | Zendy

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A Predictive Student Performance Analytics Scheme using Auto-Adjust Apriori Algorithm

Author(s) -

Himanshu Maniar,

S. O.

Publication year - 2017

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/ijca2017911729

Subject(s) - computer science , apriori algorithm , analytics , scheme (mathematics) , predictive analytics , a priori and a posteriori , data mining , algorithm , machine learning , association rule learning , mathematical analysis , philosophy , mathematics , epistemology

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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