Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/16039
Title: Academic Performance Prediction for Success Rate Improvement in Higher Institutions of Learning: An Application of Data Mining Classification Algorithms
Authors: Oyefolahan, I.O.
Idris, S.
Etuk, S.O.
Alabi, I.O.
Keywords: Data Mining
Students’ academic performance
Classification models
Higher institution of learning
WEKA
Issue Date: Jul-2018
Publisher: International Journal of Applied Information Systems (IJAIS)
Citation: 3. Oyefolahan, I. O., Idris, S., S. O. Etuk, & Alabi, I. O. (2018). Academic performance prediction for success rate improvement in higher institutions of learning: An application for data mining classification algorithms. International journal of applied information systems, 12 (14), 1-8. Available online at: ijais.org.
Series/Report no.: Foundation of Computer Science FCS, New York, USA;Volume 12 – No. 14,
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.
URI: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/16039
ISSN: ISSN : 2249-0868
Appears in Collections:Information and Media Technology

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