DECISION TREE ACADEMIC PERFORMANCE MODEL FOR PRIMARY SCHOOL STUDENTS

Abstract

Decision trees classifiers are easy and prompt data classifiers as supervised learning. Usually used in data mining to study the data and generate the tree and its rules that will be used to originate predictions. This study represents an implementation of a J48 algorithm on data collected from primary student. The aim of this study is developing a decision tree model to learning classification rules for primary students’ data. This study is an attempt to use the data mining processes, particularly classification, to help in enhancing the quality of the primary educational system by evaluating student data to study the main attributes that may affect the student performance in primary classes. For this purpose, the data mining is used for mining student related academic data over the previous year. The classification rule generation process is based on J48 classifier.