I.J. Modern Education and Computer Science, 2019, 10, 14-24 Published Online October 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2019.10.02 Copyright © 2019 MECS I.J. Modern Education and Computer Science, 2019, 10, 14-24 Identification of Trainees Enrollment Behavior and Course Selection Variables in Technical and Vocational Education Training (TVET) Program Using Education Data Mining Rana Hammad Hassan School of Systems and Technology, University of Management and Technology, Lahore – Pakistan Email: rana_hammad@live.com / S2017288002@umt.edu.pk Shahid Mahmood Awan School of Systems and Technology, University of Management and Technology, Lahore – Pakistan Email: shahid.awan@umt.edu.pk Received: 01 August 2019; Accepted: 01 September 2019; Published: 08 October 2019 Abstract—Producing skilled workforce according to industry required skills is quite challenging. Knowledge of trainee’s enrollment behavior and trainee’s course selection variables can help to address this issue. Prior knowledge of both can help to plan and target right geographic locations and right audience to produce industry required skilled workforce. Globally Technical and Vocational Education Training (TVET) is used to provide skilled workforce for the industry. TVET is an educational stream which focus learning through more practicing with less theory knowledge. In this article, we have analyzed TVET actual enrollment data of 2017 – 2018 session from a TVET training provider organization of Punjab, Pakistan. The purpose of this analysis is to understand trainee’s enrollment behavior and course selection variables which plays an important role in TVET course selection by the trainees. This enrollment behavior and course selection variables can be used to monitor and control industry required and produced skilled TVET workforce. We developed a framework which contain series of steps to perform this analysis to extract knowledge. We used educational data mining techniques of association, clustering and classification to extract knowledge. The analysis reveals that central Punjab youth is getting more TVET education as compare to south and north Punjab, Pakistan. Similarly, trainee’s ‘age group’, ‘qualification’, ‘gender’, ‘religion’ and ‘marital status’ are potential variables which can play important role in TVET course selection. By controlling these variables and integrating TVET training provider institutes, funding agencies and industry, we can smartly produce TVET skilled workforce required for industry nationally and internationally. Index Terms—TVET Data Mining, Educational Data Mining, TVET Planning & forecasting, TVET Data Analytics I. INTRODUCTION The Technical and Vocational Education Training (TVET) is formal or informal education and training which enable trainee’s to get employable skill to get a job or to start small business as an entrepreneur. TVET is an important education stream like school education and higher education but it is different from both with respect to it provide employable skills. TVET courses are normally based on 80 % for practical work and 20 % for theoretical knowledge [1]. The proportion of this equation is due to the fact that most labor work is done by hand and tools. This equation is entirely different from traditional education. TVET plays an important role in poverty alleviation and sustainable developing because it not only enhance pathways of career growth but also produces required skilled workforce for the industry [2]. Economic growth is based on multiple factors like human resource, natural resources, capital formation, technological development and social and political factors. The quality of human capital which is most important, is measured by the years of schooling, expertise and level of education [3]. Economic development cannot take place without the development of human resources therefore well-qualified professionals must be trained through TVET to raise competitiveness of companies, countries and regions [4]. Pakistan is the 5th largest young country, comprising 53% population aged between 15 and 33 years [5]. Providing education and jobs opportunities to youth is a