PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

 

e-ISSN 2231-8526
ISSN 0128-7680

Home / Regular Issue / JST Vol. 31 (1) Jan. 2023 / JST-3520-2022

 

Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Chia Ming Hong, Chee Keong Ch’ng and Teh Raihana Nazirah Roslan

Pertanika Journal of Science & Technology, Volume 31, Issue 1, January 2023

DOI: https://doi.org/10.47836/pjst.31.1.28

Keywords: Decision tree, logistic regression, Naïve Bayes, neural network, technical and vocational education and training

Published on: 3 January 2023

Technical and Vocational Education and Training (TVET) is an education system that delivers necessary information, skills, and attitudes related to work or self-employment. However, the TVET program is not preferred by most Malaysian students due to several factors such as students’ interest, parental influence, employers’ negative impression, facility in vocational institutions, inexperienced TVET instructors, and society’s negative perception. Consequently, it raises the issue of skilled workers shortage. The gravest threat will be far-reaching, pushing our economy into depreciation. Therefore, it is important to identify the students’ traits and interests before conducting further investigation to turn and thrive in this phenomenon. This study aims to utilise several classifiers (Decision Tree, Neural Network, Logistic Regression and Naïve Bayes) to predict students’ inclination to join TVET programmes. A total of 428 secondary school students from Kedah, Malaysia, are chosen as our survey respondents. The best classifier is determined according to the lowest misclassification rate. The findings revealed that the Decision Tree-based Gini Index with three branches prevail against other classifiers with a misclassification rate of 0.1938. Therefore, the classifier could act as a steer for the Kedah Department of Education (DOE), related parties, and the TVET agency in implementing effective strategies to enliven and inspire students to join TVET programs.

  • Abdul-Aziz, S. N., Zulkifli, N., Nashir, I. M., & Karim, N. A. H. (2020). Pull and push factors of students’ enrolment in the TVET programme at community colleges in Malaysia. Journal of Technical Education and Training, 12(1), 68-75. https://doi.org/10.30880/jtet.2020.12.01.007

  • Amedorme, S., & Fiagbe, Y. (2013). Challenges facing technical and vocational education in Ghana. International Journal of Scientific & Technology Research, 2(6), 253-255.

  • Amin, J. M. (2016). Quality assurance of the qualification process in TVET: Malaysia country. TVET Online Asia. http://tvet-online.asia/wp-content/uploads/2020/03/mohd-amin_tvet7.pdf

  • Assunção, M. V. D., Araújo, A. G., & Almeida, M. R. (2019). The influence of family background on the access to technical and vocational education. Journal of Public Administration, 53(3), 542-559. https://doi.org/10.1590/0034-761220170352x

  • Aziz, A. (2019, July 24). Govt struggles to overcome vocational education misconception. The Malaysian Reserved. https://themalaysianreserve.com/2019/07/24/govt-struggles-to-overcome-vocational-education-misconception/

  • Azizi, N. A. (2018, Jun 29). Ubah stigma terhadap TVET [Change the stigma against TVET]. Berita Harian Online. https://www.bharian.com.my/berita/wilayah/2018/06/443161/ubah-stigma-terhadap-tvet

  • Babu, C., Varghese, R., & Manimozhi. (2020). Predicting student’s performance using educational data mining. International Journal of Scientific Research in Computer Science Applications and Management Studies, 9(1), 1-4.

  • Bakar, A. R. (2011). Roles of technical and vocational education and training (TVET). UPM Press.

  • Berawi, M. A. (2019). The role of industry 4.0 in achieving sustainable development goals. International Journal of Technology, 10(4), 644-647. https://doi.org/10.14716/ijtech.v10i4.3341

  • Berrar, D. (2018). Bayes’ theorem and naïve bayes classifier. Encyclopedia of Bioinformatics and Computational Biology, 1, 403-412.

  • Breiman, L. (1996). Technical note: Some properties of splitting criteria. Machine Learning, 24, 41-47.

  • Chan, Y. S. (2018, November 23). We need to change perception of TVET. The Star Online. https://www.thestar.com.my/opinion/letters/2018/11/23/we-need-to-change-perception-of-tvet

  • Cheong, K., & Lee, K. (2016). Malaysia’s education crisis- Can TVET help? Malaysian Journal of Economic Studies, 53(1), 115-134. https://doi.org/10.1787/888933003668

  • Gandhi, R. (2018). Naïve Bayes classifier. Towards Data Science. https://towardsdatascience.com/naive-bayes-classifier-81d512f50a7c

  • Herlambang, A. D., Wijoyo, S. H., & Rachmadi, A. (2019). Intelligent computing system to predict vocational high school student learning achievement using naive bayes algorithm. Journal of Information Technology and Computer Science, 4(1), 15-25. https://doi.org/10.25126/jitecs.20194169

  • Hong, C. M., Ch’ng, C. K., & Roslan, T. R. N. (2021). Students’ tendencies in choosing technical and vocational education and training (TVET): Analysis of the influential factors using analytic hierarchy process. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 2608-2615. https://doi.org/10.17762/turcomat.v12i3.1262

  • Hung, H. C., Liu, I. F., Liang, C. T., & Su, Y. S. (2020). Applying educational data mining to explore students’ learning patterns in the flipped learning approach for coding education. Symmetry, 12(2), Article 213. https://doi.org/10.3390/sym12020213

  • Hussin, A., Mohamad, M., Hassan, R., & Omar, A. (2017). Technical vocational education training branding from perspective of stakeholder (parent) in Malaysia. Advanced Science Letters, 23(2), 1216-1219. https://doi.org/10.1166/asl.2017.7543

  • Imaz, M., & Sheinbaum, C. (2017). Science and technology in the framework of the sustainable development goals. World Journal of Science, Technology and Sustainable Development, 14(1), 2-17. https://doi.org/10.1108/WJSTSD-04-2016-0030

  • Islam, M., Chen, G. R., & Jin, S. Z. (2019). An overview of neural network. American Journal of Neural Networks and Applications, 5(1), 7-11. https://doi.org/10.11648/j.ajnna.20190501.12

  • Ismail, A., & Hassan, R. (2013). Issues and challenges of technical and vocational education & training in Malaysia for knowledge worker driven. In National Conference on Engineering Technology (pp. 1-11). ResearchGate. https://doi.org/10.13140/2.1.4555.2961

  • Ismail, J., Chik, C. T., & Hemdi, M. A. (2021). TVET graduate employability: Mismatching traits between supply and demand. International Journal of Academic Research in Business and Social Sciences, 11(13), 223-243. https://doi.org/10.6007/IJARBSS/v11-i13/8522

  • Ismail, K., Nopiah, Z. M., Rasul, M. S., & Leong, P. C. (2017). Malaysian teachers’ competency in technical vocational education and training: A review. In A. G. Abdullah, T. Aryanti, A. Setiawan & M. Alias (Eds.), Regionalization and Harmonization in TVET (pp. 59-64). Taylor & Francis Group.

  • Karim, M. A. (2018, September 5). TVET, a relevant choice. New Straits Times. https://www.nst.com.my/education/2018/09/408470/tvet-relevant-choice

  • Karim, Z. I. A., & Maat, S. M. (2019). Employability skills model for engineering technology students. Journal of Technical Education and Training, 11(2), 79-87. https://doi.org/10.30880/jtet.2019.11.02.008

  • Kelvin, G. (1997). An introduction to neural networks. UCL Press

  • Khan, I. A., & Choi, J. T. (2014). An application of educational data mining (EDM) technique for scholarship prediction. International Journal of Software Engineering and Its Applications, 8(12), 31-42.

  • Koya, Z. (2019, July 4). TVET courses are not for those who are academically weak, Kula tells parents. The Star Online. https://www.thestar.com.my/news/nation/2019/07/04/tvet-courses-are-not-for-those-who-are-academically-weak-kula-tells-parents

  • KRI. (2018). The school-to-work transition survey of young Malaysians. Khazanah Research Institute. http://www.krinstitute.org/assets/contentMS/img/template/editor/20181205_SWTS_Main%20Book.pdf

  • Kukreja, H., Bharath, N., Siddesh, C. S., & Kuldeep, S. (2016). An introduction to artificial neural network. International Journal of Advance Research And Innovative Ideas In Education, 1(5), 27-30.

  • Liu, H., & Cocea, M. (2017). Semi-random partitioning of data into training and test sets in granular computing context. Granula Computing, 2, 357-386. https://doi.org/10.1007/s41066-017-0049-2

  • Matei, M. M. M., Mocanu, C., & Zamfir, A. M. (2018). Educational paths in Romania: Choosing general or vocational education. HOLISTICA, 9(2), 127-136. https://doi.org/10.2478/hjbpa-2018-0016

  • Messele, A., & Addisu, M. (2020). A model to determine factors affecting students academic performance: The case of Amhara region agency of competency, Ethiopia. International Research Journal of Science and Technology, 1(2), 75-87. https://doi.org/10.46378/irjst.2020.010202

  • Mustapha, R. B. (2015). Green and sustainable development for TVET in Asia. The International Journal of Technical and Vocational Education, 11(2), 133-142.

  • Nguyen, Q, H., Ly, H. B., Ho, L. S., Al-Ansari, N., Le, H. V., Tran, V. Q., Prakash, I., & Pham, B. T. (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021, Article 4832864. https://doi.org/10.1155/2021/4832864

  • Ozkan, U. B., Cigdem, H., & Erdogan, T. (2020). Artificial neural network approach to predict lms acceptance of vocational school students. Turkish Online Journal of Distance Education, 21(3), 156-169. https://doi.org/10.17718/tojde.762045

  • Rajaendram, R. (2020, November 6). Budget 2021: Association welcomes bigger allocation for TVET sector. The Star Online. https://www.thestar.com.my/news/nation/2020/11/06/budget-2021-association-welcomes-bigger-allocation-for-tvet-sector

  • Raju, D., & Schumacker, R. (2015). Exploring student characteristics of retention that lead to graduation in higher education using data mining models. Journal of College Student Retention: Research, Theory & Practice, 16(4), 563-591. https://doi.org/10.2190/CS.16.4.e

  • Rokach, L., & Maimon, O. (2015). Data mining with decision trees theory and applications. World Scientific Publishing Co. Pte. Ltd.

  • Sabang, A, (2017, September 2). Kesedaran TVET perlu dipertingkat [Awareness of TVET needs to be improved]. Utusan Borneo Online. https://www.utusanborneo.com.my/2017/09/02/kesedaran-tvet-perlu-dipertingkat

  • Sahu, H., Shrma, S., & Gondhalakar, S. (2011). A brief overview on data mining survey. International Journal of Computer Technology and Electronics Engineering, 1(3), 114-121.

  • Salal, Y. K., Abdullaev, S. M., & Kumar, M. (2019). Educational data mining: Student performance prediction in academic. International Journal of Engineering and Advanced Technology, 8(4C), 54-59.

  • Sivanandam, H., Rahim, R., Carvalho, M., & Tan, T. (2019, October 11). Budget 2020: Every single sen for education will be used properly, says Maszlee. The Star Online. https://www.thestar.com.my/news/nation/2019/10/11/budget-2020-every-single-sen-for-education-will-be-used-properly-says-maszlee

  • Sulaiman, N. L., & Salleh, K. M. (2016). The development of technical and vocational education and training (tvet) profiling for workforce management in Malaysia: Ensuring the validity and reliability of TVET data. Man In India, 96, 2825-2835.

  • TheStart. (2019, October 12). Thanks, but RM5.9bil not enough for TVET. The Star Online. https://www.thestar.com.my/news/nation/2019/10/12/thanks-but-rm59bil-not-enough-for-tvet

  • Trudis, H. (2014). Secondary school students’ perceptions of vocational education in Barbados. https://docslib.org/doc/7204149/secondary-school-students-perceptions-of-vocational-education-in-barbados

  • Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679-686. https://doi.org/10.1016/S2212-5671(15)00123-9

  • Yaakob, H. (2017). Technical and vocational education & training (TVET) institutions towards statutory body: Case study of Malaysian polytechnic. Advanced Journal of Technical and Vocational Education, 1(2), 07-13. https://doi.org/10.26666/rmp.ajtve.2017.2.2

  • Yağcı, A., & Ḉevik, M. (2017). Predictions of academic achievements of vocational and technical high school students with artificial neural networks in science courses (physics, chemistry and biology) in Turkey and measures to be taken for their failures. SHS Web Conference, 37, Article 1057. https://doi.org/10.1051/shsconf/20173701057

  • Yamini, O., & Ramakrishna, S. (2015). A study on advantages of data mining classification techniques. International Journal of Engineering Research & Technology, 4(9), 969-972.

  • Zakaria, M., AL-Shebany, M., & Sarhan, S. (2014). Artificial neural network: A brief overview. International Journal of Engineering Research and Applications, 4(2), 7-12.

ISSN 0128-7680

e-ISSN 2231-8526

Article ID

JST-3520-2022

Download Full Article PDF

Share this article

Related Articles