STM Article Repository

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.