Application of Decision Tree in Classifying Secondary School Students’ Tendencies to Choose TVET in Malaysia

Abstract

The wave of Industry Revolution (IR 4.0) highlights the importance of technology in our life. The demand for technologist and skilled workers in Technical and Vocational Education and Training (TVET) are increasing day by day due to their expertise. TVET provides a platform for formal and non-formal learning to equip the youngsters in contributing to the development of a prosperous and inclusive nation. Moreover, TVET promises bright job prospects especially in fulfilling the manpower demand of IR 4.0. However, students in Malaysia currently are not fully aware of the existence of TVET, since the number of students who join TVET are still below expectation. Therefore, the main objective in this study is to develop the best TVET model to classify the students’ tendency in choosing TVET after completing secondary school. From the literature, five main factors that hinder students’ interest in joining TVET are recognized, namely students’ interest, parents, society, TVET instructors and employers. In this study, 428 secondary school students from Kedah (Malaysia) are involved as respondents. Different types of decision tree models are developed based on the algorithms and the splitting criteria. Altogether, there are 15 variables derived from 5 main affecting factors mentioned above to determine the tendency of joining TVET. Consequently, the best TVET classifier with the misclassification rate of 0.2919 is selected, to predict the tendency of students who will be joining TVET in future. Our findings revealed that the variable of “Stream” plays as the primary and trifling roles. This classifier is beneficial in assisting the government to achieve the aim of upholding TVET in Malaysia.