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August Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the August 2021 Edition of the journal. The last date of research paper submission is July 15, 2021.

Academic Performance Prediction for Success Rate Improvement in Higher Institutions of Learning: An Application of Data Mining Classification Algorithms

Ishaq Oyebisi Oyefolahan, Suleiman Idris, Stella Oluyemi Etuk, Isiaq Oludare Alabi in Data Mining

International Journal of Applied Information Systems
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Ishaq Oyebisi Oyefolahan, Suleiman Idris, Stella Oluyemi Etuk, Isiaq Oludare Alabi
10.5120/ijais2018451763
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  1. Ishaq Oyebisi Oyefolahan, Suleiman Idris, Stella Oluyemi Etuk and Isiaq Oludare Alabi. Academic Performance Prediction for Success Rate Improvement in Higher Institutions of Learning: An Application of Data Mining Classification Algorithms. International Journal of Applied Information Systems 12(14):0-8, July 2018. URL, DOI BibTeX

    @article{10.5120/ijais2018451763,
    	author = "Ishaq Oyebisi Oyefolahan and Suleiman Idris and Stella Oluyemi Etuk and Isiaq Oludare Alabi",
    	title = "Academic Performance Prediction for Success Rate Improvement in Higher Institutions of Learning: An Application of Data Mining Classification Algorithms",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "July 2018",
    	volume = 12,
    	number = 14,
    	month = "July",
    	year = 2018,
    	issn = "2249-0868",
    	pages = "0-8",
    	url = "http://www.ijais.org/archives/volume12/number14/1034-2018451763",
    	doi = "10.5120/ijais2018451763",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

The abolition of pass grade for any degree course and the consequent change in cumulative grade point for any student to remain within an academic system at University level in Nigeria has led to withdrawal of many students. Thus, it becomes imperative for academic institutions managements to ensure that all necessary steps are taken to enable student graduate successfully. This study explores the usefulness of data mining in unravelling hidden knowledge in students’ academic record, particularly the students’ specific characteristics which managements or decision makers can leverage upon to ensure improvement in academic success rate of the students. In addition, the study provides a guide through which predicting algorithms can be used by senior academics to predict the performances of students in their respective classes. The conclusion of the study advocates for the use of data mining as decision making tool in academic institutions.

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Keywords

Data Mining, Students’ academic performance, Classification models, Higher institution of learning, WEKA