A bayesian network approach to identify factors affecting learning of Additional Mathematics

Abstract

Additional Mathematics is an elective subject in Sijil Pelajaran Malaysia (SPM). However, it is treated as a core subject for almost all the science stream students. Many students who can perform well in Modern Mathematics since primary school cannot master the Additional Mathematics. They fail to understand the concepts in Additional Mathematics. This study seeks to identify the factors that affect students in mastering Additional Mathematics at five schools in an urban area. Bayesian network is used to identify the relationship between the factors in the study and to analyze the data as it is able to represent the variables as nodes and the relationships as directed arcs. Constraint-based algorithms and score-based algorithms are used to generate the networks into several categories to compare and identify the strong relationships among the factors that affect the students’ learning of the subject. It is concluded that the new symbols and sign learned in Additional Mathematics affects the students in mastering the subject.