Classification model and analysis on students’ performance

Abstract

The purpose of this paper is to propose a classification model for classifying students’
performance in SijilPelajaran Malaysia in order to help teachers plan suitable teaching
activities for their students based on the students’ performance. Five classifier algorithms have been used during the process which are Naïve Bayes, Random Tree, Multi Class Classifier, Conjunctive Rule and Nearest Neighbour. Data was collected from MaktabRendahSains MARA Kuala Berang, Terengganu, Malaysia starting May 2011 until December 2014. The students’ performance was evaluated based on the category of students according to their SPM Results. Parameters that contribute to students’ performance such as stream, state, gender and hometown are also investigated along with the examination data.This research shows that first semester results can be used to identify students’ performance.

Keywords: educational data mining; classification model; feature selection