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

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Rana Hammad Hassan 1,* Shahid Mahmood Awan 1

1. School of Systems and Technology, University of Management and Technology, Lahore – Pakistan

* Corresponding author.


Received: 1 Aug. 2019 / Revised: 16 Aug. 2019 / Accepted: 1 Sep. 2019 / Published: 8 Oct. 2019

Index Terms

TVET Data Mining, Educational Data Mining, TVET Planning & forecasting, TVET Data Analytics


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.

Cite This Paper

Rana Hammad Hassan, Shahid Mahmood Awan, " Identification of Trainees Enrollment Behavior and Course Selection Variables in Technical and Vocational Education Training (TVET) Program Using Education Data Mining", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.10, pp. 14-24, 2019. DOI:10.5815/ijmecs.2019.10.02


[1]O. Q. Essel, E. Agyarkoh, M. S. Sumaila, and P. D. Yankson, “TVET stigmatization in developing countries: reality or fallacy,” European Journal of Training and Development Studies, vol. 1, no. 1, pp. 27–42, 2014.
[2]UNESCO-UNEVOC, “Technical and Vocational Education and Training (TVET) Challenges and Priorities in Developing Countries - Google Search,” Mar-2018. [Online]. Available: [Accessed: 22-Mar-2018].
[3]P. Descy and M. Tessaring, The value of learning: evaluation and impact of education and training: third report on vocational training research in Europe: synthesis report. Office for official publications of the European Communities, 2005.
[4]“GIZ Expertise. Start.” [Online]. Available: [Accessed: 23-Apr-2018].
[5]R. Carr and K. Scarim, “Delivering a world where every pregnancy is wanted every childbirth is safe and every young person’s potential is fulfilled,” p. 52.
[6]S. Nooruddin, “Technical and Vocational Education and Training for Economic Growth in Pakistan,” Journal of Education and Educational Development, vol. 4, no. 1, pp. 130–141, May 2017.
[7]“Navttc – National Vocational & Technical Training Commission (NAVTTC), Pakistan,” 2018. .
[8]“Comparative Analysis of TVET Sector in Pakistan.” [Online]. Available: [Accessed: 29-Mar-2019].
[9]R. A. Huebner, “A Survey of Educational Data-Mining Research.,” Research in higher education journal, vol. 19, 2013.
[10]J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
[11]B. Bakhshinategh, O. R. Zaiane, S. ElAtia, and D. Ipperciel, “Educational data mining applications and tasks: A survey of the last 10 years,” Educ Inf Technol, vol. 23, no. 1, pp. 537–553, Jan. 2018.
[12]G. Shmueli, P. C. Bruce, I. Yahav, N. R. Patel, and K. C. Lichtendahl Jr, Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons, 2017.
[13]M. Berland, R. S. Baker, and P. Blikstein, “Educational Data Mining and Learning Analytics: Applications to Constructionist Research,” Tech Know Learn, vol. 19, no. 1–2, pp. 205–220, Jul. 2014.
[14]A. El-Halees, “Mining students data to analyze e-Learning behavior: A Case Study,” 2009.
[15]M. Agaoglu, “Predicting instructor performance using data mining techniques in higher education,” IEEE Access, vol. 4, pp. 2379–2387, 2016.
[16]V. Gramoli et al., “Mining autograding data in computer science education,” in Proceedings of the Australasian Computer Science Week Multiconference, 2016, p. 1.
[17]H. Aldowah, H. Al-Samarraie, and W. M. Fauzy, “Educational data mining and learning analytics for 21st century higher education: A review and synthesis,” Telematics and Informatics, vol. 37, pp. 13–49, Apr. 2019.
[18]C. Márquez-Vera, C. R. Morales, and S. V. Soto, “Predicting school failure and dropout by using data mining techniques,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 8, no. 1, pp. 7–14, 2013.
[19]P. N. Marra, “Identifying educational needs by rationalising TVET industry clusters in Brazil,” p. 19.
[20]O. Deperlioglu and F. S. Birtil, “Analysis of Girls Vocational High School Students’ Academic Failure Causes with Data Mining Techniques,” The Anthropologist, vol. 23, no. 3, pp. 505–512, Mar. 2016.
[21]R. Wirth, “CRISP-DM: Towards a standard process model for data mining,” in Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, 2000, pp. 29–39.
[22]PVTC MIS Department, “PVTC MIS System,” PVTC MIS System, Mar-2018. [Online]. Available: [Accessed: 20-Mar-2018].
[23]PVTC, “PVTC Official Website,” Punjab Vocational Training Council Official Website, Mar-2018. [Online]. Available: [Accessed: 20-Mar-2018].
[24]“Districts | Punjab Portal.” [Online]. Available: [Accessed: 28-Aug-2019].
[25]J. Demšar et al., “Orange: data mining toolbox in Python,” The Journal of Machine Learning Research, vol. 14, no. 1, pp. 2349–2353, 2013.
[26]R. Srikant and R. Agrawal, “Mining quantitative association rules in large relational tables,” in Acm Sigmod Record, 1996, vol. 25, pp. 1–12.
[27]S. H. Park, S. Y. Jang, H. Kim, and S. W. Lee, “An association rule mining-based framework for understanding lifestyle risk behaviors,” PloS one, vol. 9, no. 2, p. e88859, 2014.
[28]M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning. MIT press, 2018.
[29]P. Berkhin, “A Survey of Clustering Data Mining Techniques,” in Grouping Multidimensional Data, Springer, Berlin, Heidelberg, 2006, pp. 25–71.
[30]Y. Rani and D. H. Rohil, “A Study of Hierarchical Clustering Algorithm,” p. 8.
[31]S. Jain, R. Raghuvanshi, and M. Ilyas, “A Survey Paper on Overview of Basic Data Mining Tasks,” 2017.
[32]K. H. Zou, A. Liu, A. I. Bandos, L. Ohno-Machado, and H. E. Rockette, Statistical evaluation of diagnostic performance: topics in ROC analysis. Chapman and Hall/CRC, 2016.
[33]“An improved method to construct basic probability assignment based on the confusion matrix for classification problem - ScienceDirect.” [Online]. Available: [Accessed: 01-Aug-2019].