Students’ Performance Prediction Using Classsification Algorithms
Babajide Olakunle Afeni *
Department of Computer Science, Joseph Ayo Babalola University, Ikeji - Arakeji, Nigeria.
Iyanuoluwa Ayomide Oloyede
Department of Computer Science, Joseph Ayo Babalola University, Ikeji - Arakeji, Nigeria.
Damilola Okurinboye
Department of Computer Science, Joseph Ayo Babalola University, Ikeji - Arakeji, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
It is imperative to analyze educational data especially as it relates to students’ performance. Educational institutions need to have a fairly accurate knowledge of admitted students’ prior academic ability to predict their future academic performance. This helps to identify the good students and also provides an opportunity to pay attention to and improve those who would possibly not perform too well. As a solution, this paper proposed a system which can predict the performance of students from their previous academic record using concepts of data mining techniques under Classification. The dataset contains information about students, such as gender, age, SSCE grade, UTME score, post UTME score and grade in students first year. ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms was applied on the data to predict the academic performance of students in future examinations.
Keywords: Classification, decision tree, data mining, academic performance, machine learning