CLASSIFICATION OF UNIFIED TERTIARY MATRICULATION EXAMINATION (UTME) STUDENTS USING NAÏVE BAYESIAN ALGORITHM

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Published: 2015-11-20

Page: 312-317


JACKSON AKPOJARO *

Department of Mathematical and Physical Sciences, College of Basic and Applied Sciences, Samuel Adegboyega University, Ogwa, Edo State, Nigeria.

PRINCEWILL AIGBE

Department of Mathematics and Computer Science, College of Natural and Applied Sciences, Western Delta University, Oghara, Delta State, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

Mining education data and making classifications based on processed dataset are essential part of scientific field of enquiry. In this paper, we study the data collected from UTME students’ scores. The collected data was pre-processed to remove unwanted and less meaningful attributes. Thereafter, we classify the students into three categories – excellent, average and weak - using the Naïve Bayesian algorithm. The process was carried out using WEKA (Waikato Environment for knowledge Analysis) data miming tool. Experimental results show that the Naïve Bayesian algorithm correctly classified the dataset of 300 UTME students with 93.33% level of confidence. The results can be used by the Joint Admission and Matriculation Board (JAMB) to enhance the process of decision making such as admission cut-off points, placements of students in the tertiary institutions in Nigeria.

Keywords: Education data, dataset, vocation, cut-off points, predicting algorithm


How to Cite

AKPOJARO, J., & AIGBE, P. (2015). CLASSIFICATION OF UNIFIED TERTIARY MATRICULATION EXAMINATION (UTME) STUDENTS USING NAÏVE BAYESIAN ALGORITHM. Asian Journal of Mathematics and Computer Research, 9(4), 312–317. Retrieved from https://ikprress.org/index.php/AJOMCOR/article/view/203

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