Supervised and Unsupervised Data Mining Techniques on Employability of Public Higher Learning Institute Graduates in Malaysia

Abstract

Abstract It is the students’ dream to secure a job right after graduation. However, there are factors that hinder their employability. This study aims to predict Malaysian graduates’ employment status based on employability factors and to profile the graduates’ satisfaction towards their curricular activities and information and communications technology (ICT) skills. A total of 375,507 student records were obtained based on tracer studies conducted by the Malaysian Ministry of Higher Education between 2015 and 2018. Due to the large amount of data with various categories, supervised and unsupervised data mining techniques were used to unmask the underlying variables and reveal hidden information about graduates’ employability for better tracing the employment status of graduates. Various types of consolidation techniques were also used to reduce the number of levels for categorical inputs in the dataset, namely, classifiers without consolidation, with manual consolidation, and with tree consolidation. Three types of data mining variable selections were used to improve the performance of the classifiers in predicting employment status. The results show that logistic regression (LR) without variable selection is the best classifier for data without consolidation, while LR using variable selection with LR stepwise is the best classifier for data with manual and tree consolidations. In profiling the satisfaction of graduates, K-Means Clustering was used, which revealed seven clusters. The most prominent cluster consisted of graduates who were highly satisfied with their ICT skills but less satisfied with their curricular activities. These data mining techniques were able to trace graduates’ employment status and identify the success factors of graduates’ employability.