Classification techniques for predicting graduate employability

Zalinda Othman, Soo Wui Shan, Ishak Yusoff, Peng Kee Chang

Research output: Contribution to journalArticle

Abstract

Unemployment is a current issue that happens globally and brings adverse impacts on worldwide. Thus, graduate employability is one of the significant elements to be highlighted in unemployment issue. There are several factors affecting graduate employability, traditionally, excellent academic performance (i.e., cumulative grade point average, CGPA) has been the most dominant element in determining an individual's employment status. However, researches have shown that not only CGPA determines the graduate employability; in fact other factors may influence the graduate achievement in getting a job. In this work data mining techniques are used to determine what are the factors that affecting the graduates. Therefore, the objective of this study is to identify factors that influence graduates employability. Seven years of data (from 2011 to 2017) are collected through the Malaysia's Ministry of Education tracer study. Total number of 43863 data instances involved in this employability class model development. Three classification algorithms, Decision Tree, Support Vector Machines and Artificial Neural Networks are used and being compared for the best models. The results show decision tree J48 produces higher accuracy compared to other techniques with classification accuracy of 66.0651% and it increased to 66.1824% after the parameter tuning. Besides, the algorithm is easily interpreted, and time to build the model is small which is 0.22 seconds. This paper identified seven factors affecting graduate employability, namely age, faculty, field of study, co-curriculum, marital status, industrial internship and English skill. Among these factors, attribute age, industrial internship and faculty contain the most information and affect the final class, i.e. employability status. Therefore, the results of this study will help higher education institutions in Malaysia to prepare their graduates with necessary skills before entering the job market.

Original languageEnglish
Pages (from-to)1712-1720
Number of pages9
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume8
Issue number4-2
Publication statusPublished - 1 Jan 2018

Fingerprint

Decision Trees
Unemployment
Malaysia
Internship and Residency
unemployment
Decision trees
Education
Data Mining
Age Factors
Marital Status
Curriculum
educational institutions
academic achievement
labor market
marital status
higher education
curriculum
Curricula
neural networks
Support vector machines

Keywords

  • Classification
  • Decision tree
  • Graduate employability
  • Multilayer perceptron
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

Classification techniques for predicting graduate employability. / Othman, Zalinda; Shan, Soo Wui; Yusoff, Ishak; Chang, Peng Kee.

In: International Journal on Advanced Science, Engineering and Information Technology, Vol. 8, No. 4-2, 01.01.2018, p. 1712-1720.

Research output: Contribution to journalArticle

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