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Application of Fuzzy Association Rule Mining for Analysing Students Academic Performance

Oladipupo, O. O. and Oyelade, O. J. and Aborisade, D. O. (2012) Application of Fuzzy Association Rule Mining for Analysing Students Academic Performance. International Journal of Computer Science Issues, 9 (3). pp. 216-223. ISSN (Online): 1694-0814

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This study examines the relationship between students’ preadmission academic profile and academic performance. Data sample of students in the Department of Computer Science in one of Nigeria private Universities was used. The preadmission academic profile considered includes ‘O’ level grades, University Matriculation Examination (UME) scores, and Post-UME scores. The academic performance is defined using students’ Grade Point Average (GPA) at the end of a particular session. Fuzzy Association Rule Mining (FARM) was used to identify the hidden relationships that exist between students’ pre-admission profile and academic performance. This study hopes to determine the academic profile of students who are most admitted in the session. It determines students’ performance ratings as against their pre-admission academic profile. This can serve as a predictor for admission committee to enhance the quality of the new in-take and guide for the academic adviser

Item Type: Article
Uncontrolled Keywords: Data mining, Fuzzy Association Rule Mining, Student’s Pre-admission academic profile, Academic Performance, Grade Point Average
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 27 Apr 2017 13:31
Last Modified: 15 Jun 2017 17:53

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