Shahid Mahmood Awan

Work place: School of Systems and Technology, University of Management and Technology, Lahore – Pakistan

E-mail: shahid.awan@umt.edu.pk

Website:

Research Interests: Computational Learning Theory, Natural Language Processing, Data Structures and Algorithms, Programming Language Theory

Biography

Shahid Mahmood Awan has received Ph.D. in Computer Science (Machine Learning) from University of Engineering and Technology, Lahore in 2015. He has 14 years of research, teaching and software development experience. His research interests include: Big Data Analytics, Machine Learning, Deep Learning, Natural Language Processing, and Smart Environments. He is currently working as Assistant Professor at University of Management and Technology, Lahore. He is active member of IEEE Computer Society and Industrial Electronics Society.

Author Articles
Identification of Trainees Enrollment Behavior and Course Selection Variables in Technical and Vocational Education Training (TVET) Program Using Education Data Mining

By Rana Hammad Hassan Shahid Mahmood Awan

DOI: https://doi.org/10.5815/ijmecs.2019.10.02, Pub. Date: 8 Oct. 2019

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.

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