(Universitas Negeri Malang, Indonesia)(2) Felix Andika Dwiyanto
(Universitas Negeri Malang, Indonesia)(3) Agung Bella Putra Utama
(Universitas Negeri Malang, Indonesia)*corresponding author
AbstractComputational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues.
KeywordsComputational method; Vocational curriculum; Apprenticeship; Labor market; Enrollment
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DOIhttps://doi.org/10.26555/ijain.v6i3.581 |
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References
[1] J. S. Tripney and J. G. Hombrados, “Technical and vocational education and training (TVET) for young people in low- and middle-income countries: a systematic review and meta-analysis,” Empir. Res. Vocat. Educ. Train., vol. 5, no. 3, Dec. 2013, doi: 10.1186/1877-6345-5-3.
[2] A. Wolf, “Review of vocational education,” London, 2011, Available at: Google Scholar
[3] Y. Po, “Understanding Vocational Education Market in China,” No. 6, 2014, Available at: Google Scholar
[4] T. Schröder, “A regional approach for the development of TVET systems in the light of the 4th industrial revolution: the regional association of vocational and technical education in Asia,” Int. J. Train. Res., vol. 17, no. sup1, pp. 83–95, Jul. 2019, doi: 10.1080/14480220.2019.1629728.
[5] E. A. Hanushek, L. Woessmann, and L. Zhang, “General Education, Vocational Education, and Labor-Market Outcomes over the Life-Cycle,” No. 17504, 2011, doi: 10.3368/jhr.52.1.0415-7074R
[6] T. Agrawal, “Vocational education and training programs (VET): An Asian perspective,” Asia-Pacific J. Coop. Educ., 2013, Available at: Google Scholar
[7] B. Panth and R. B. Caoli-Rodriguez, Competence-based Training in South Asia, 2017, Competence., vol. 23, doi: 10.1007/978-3-319-41713-4.
[8] S. Mehrotra, “Technical and Vocational Education in Asia: What can South Asia Learn from East/South East Asia?,” Indian J. Labour Econ., vol. 59, no. 4, pp. 529–552, Dec. 2016, doi: 10.1007/s41027-017-0079-y.
[9] A. Meiriza, E. Lestari, P. Putra, A. Monaputri, and D. A. Lestari, “Prediction Graduate Student Use Naive Bayes Classifier,” in Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN), 2020, vol. 172, pp. 370–375, doi: 10.2991/aisr.k.200424.056.
[10] J. C. Alejandrino, A. J. P. Delima, and R. N. Vilchez, “IT Students Selection and Admission Analysis Using Naïve Bayes and C4.5 Algorithm,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 1, pp. 759–765, 2020, doi: 10.30534/ijatcse/2020/108912020.
[11] C. Márquez-Vera, C. Romero Morales, and S. Ventura Soto, “Predicting school failure and dropout by using data mining techniques,” Rev. Iberoam. Tecnol. del Aprendiz., vol. 8, no. 1, pp. 7–14, 2019, doi: 10.1109/RITA.2013.2244695.
[12] O. M. Musau, K. Omieno, and R. Angulu, “Towards Prediction of Students’ Academic Performance in Secondary School Using Decision Trees,” Int. J. Res. Innov. Appl. Sci., vol. IV, no. X, pp. 85–89, 2019, Available at: Google Scholar
[13] T. Pattiasina and D. Rosiyadi, “Comparison of Data Mining Classification Algorithm for Predicting The Performance of High School Students,” J. Techno Nusa Mandiri, vol. 17, no. 1, pp. 22–30, Mar. 2020, doi: 10.33480/techno.v17i1.1226.
[14] B. Khan, M. Sikandar Hayat Khiyal, and M. Daud Khattak, “Final Grade Prediction of Secondary School Student using Decision Tree,” Int. J. Comput. Appl., vol. 115, no. 21, pp. 32–36, 2019, doi: 10.5120/20278-2712.
[15] I. Oliveira, “Classifying: Comprehension Of Students And Teachers Of Primary School,” nternational Congr. Math. Educ., no. January 2019, 2019, Available at: Google Scholar
[16] M. Singh, H. Nagar, and A. Sant, “Using Data Mining to Predict Primary School Student Performance,” IJARIIE, vol. 2, no. 1, pp. 43–46, 2019, Available at: Google Scholar
[17] B. Fauth, J. Decristan, S. Rieser, E. Klieme, and G. Büttner, “Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes,” Learn. Instr., vol. 29, pp. 1–9, 2019, doi: 10.1016/j.learninstruc.2013.07.001.
[18] G. Supriyanto, I. Widiaty, A. G. Abdullah, and Y. R. Yustiana, “Application expert system career guidance for students,” J. Phys. Conf. Ser., vol. 1402, no. 6, p. 066031, Dec. 2019, doi: 10.1088/1742-6596/1402/6/066031.
[19] A. Yağci and M. Çevik, “Prediction of academic achievements of vocational and technical high school (VTS) students in science courses through artificial neural networks (comparison of Turkey and Malaysia),” Educ. Inf. Technol., vol. 24, no. 5, pp. 2741–2761, Sep. 2019, doi: 10.1007/s10639-019-09885-4.
[20] A. Sucipto and J. Minardi, “Neural Network Model for Mathematic Scores Prediction: Case Study in SMK Negeri Pakis Aji, Jepara, Indonesia,” EKSAKTA J. Ilmu-ilmu MIPA, vol. 20, no. 1, pp. 28–35, Jan. 2020, doi: 10.20885/EKSAKTA.vol1.iss1.art5.
[21] I. G. P. Christyaditama, I. M. Candiasa, and I. G. A. Gunadi, “Optimization of artificial neural networks to improve accuracy of vocational competence selection of vocational school students using nguyen-widrow,” J. Phys. Conf. Ser., vol. 1516, no. 1, p. 012052, Apr. 2020, doi: 10.1088/1742-6596/1516/1/012052.
[22] A. Yağcı and M. Çevik, “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 Conf., vol. 37, p. 01057, Aug. 2017, doi: 10.1051/shsconf/20173701057.
[23] B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering; Technical report EBSE-2007-01; EBSE: UK, Durham,” 2007, Available at: Google Scholar
[24] D. Denyer and D. Tranfield, “Producing a systematic review.,” in The Sage handbook of organizational research methods., Thousand Oaks, CA: Sage Publications Ltd, 2009, pp. 671–689, Available at: Google Scholar
[25] E. El Haji, A. Azmani, and M. El Harzli, “Expert system design for educational and vocational guidance, using a multi-agent system,” in 2014 International Conference on Multimedia Computing and Systems (ICMCS), 2014, pp. 1018–1024, doi: 10.1109/ICMCS.2014.6911256.
[26] A. Çetinkaya and Ö. K. Baykan, “Prediction of middle school students’ programming talent using artificial neural networks,” Eng. Sci. Technol. an Int. J., no. xxxx, Aug. 2020, doi: 10.1016/j.jestch.2020.07.005.
[27] A. A. J. Permana, L. J. E. Dewi, and K. Setemen, “Recommendation System for Selection of Majors and Apprenticeship on Vocational and Training Education Based on Competency to Produce Demand Driven Graduates,” in Proceedings of the 2nd International Conference on Innovative Research Across Disciplines (ICIRAD 2017), 2017, no. January, doi: 10.2991/icirad-17.2017.29.
[28] U. B. Özkan, H. Cigdem, and T. Erdogan, “Artificial Neural Network Approach to Predict LMS Acceptance of Vocational School Students,” Turkish Online J. Distance Educ., vol. 21, no. 3, pp. 156–169, Jul. 2020, doi: 10.17718/tojde.762045.
[29] R. Damiaza and D. Fitrianah, “Prediction Analysis of Kartu Jakarta Pintar (KJP) Awardees in Vocational High School XYZ Using C4.5 Algorithm,” Int. J. Mach. Learn. Comput., vol. 10, no. 1, pp. 44–50, Jan. 2020, doi: 10.18178/ijmlc.2020.10.1.896.
[30] R. Marlina, Adrianto, W. Amaldi, and M. J. Budiman, “Optimization of Algorithm C4.5, Naive Bayes With Particle Swarm Optimization in Predicting Career Suitability of Vocational High School Students: Case Study of SMKN 1 Rangkasbitung,” Int. J. Comput. Tech., vol. 5, no. 5, pp. 146–153, 2018, doi: 10.13140/RG.2.2.26994.45764.
[31] F. Nasution and E. Muiza Zamzami, “Prediction of Vocational Students Behaviour using The k-Nearest Neighbor Algorithm,” J. Phys. Conf. Ser., vol. 1566, no. 1, p. 012046, Jun. 2020, doi: 10.1088/1742-6596/1566/1/012046.
[32] V. Aarkog, B. Wahlgren, C. H. Larsen, K. M. Andrson, and S. Gottlieb, “Decision-Making Processes Among Potential Dropouts in Vocational Education and Training and Adult Learning,” Int. J. Res. Vocat. Educ. Train., vol. 5, no. 2, 2018, doi: 10.13152/IJRVET.5.2.2.
[33] R. Harimurti, Y. Yamasari, Ekohariadi, Munoto, and B. I. G. P. Asto, “Predicting student’s psychomotor domain on the vocational senior high school using linear regression,” in 2018 International Conference on Information and Communications Technology (ICOIACT), 2018, vol. 2018-Janua, pp. 448–453, doi: 10.1109/ICOIACT.2018.8350768.
[34] J. Diedrich, A. C. Neubauer, and A. Ortner, “The Prediction of Professional Success in Apprenticeship: The Role of Cognitive and Non-Cognitive Abilities, of Interests and Personality,” Int. J. Res. Vocat. Educ. Train., vol. 5, no. 2, pp. 82–110, Aug. 2018, doi: 10.13152/IJRVET.5.2.1.
[35] J. Zhang, J. T. Du, and F. Xu, “Application of Data Mining in MOOCs for Developing Vocational Education: A Review and Future Research Directions,” Int. J. Inf. Educ. Technol., vol. 8, no. 6, pp. 411–417, 2018, doi: 10.18178/ijiet.2018.8.6.1073.
[36] L. Lannegrand-Willems, O. Cosnefroy, and A. Lecigne, “Prediction of various degrees of vocational secondary school absenteeism: Importance of the organization of the educational system,” Sch. Psychol. Int., vol. 33, no. 3, pp. 294–307, Jun. 2012, doi: 10.1177/0143034311418912.
[37] C. Verma, A. S. Tarawneh, Z. Illes, V. Stoffova, and M. Singh, “National Identity Predictive Models for the Real Time Prediction of European School’s Students: Preliminary Results,” in 2019 International Conference on Automation, Computational and Technology Management (ICACTM), 2019, pp. 418–423, doi: 10.1109/ICACTM.2019.8776842.
[38] R. Agustina, Y. S. Dwanoko, G. Susanto, W. Kuswinardi, H. L. Purwanto, and D. Suprianto, “Decision making system vocational high school election using promethee method,” J. Phys. Conf. Ser., vol. 1375, no. 1, p. 012039, Nov. 2019, doi: 10.1088/1742-6596/1375/1/012039.
[39] R. J. Salaki, C. R. Kawet, R. Manoppo, and F. Tumimomor, “Decision Support Systems Major Selection Vocational High School in Using Fuzzy Logic Android-Based,” Int. Conf. Electr. Eng. Informatics, Its Educ. 2015, no. October 2015, 2017, Available at: Google Scholar
[40] J. N. Purwaningsih and Y. Suwarno, “Predicting students achievement based on motivation in vocational school using data mining approach,” in 2016 4th International Conference on Information and Communication Technology (ICoICT), 2016, vol. 4, no. c, pp. 1–5, doi: 10.1109/ICoICT.2016.7571880.
[41] H. I. Bulbul and O. Unsal, “Determination of Vocational Fields with Machine Learning Algorithm,” in 2010 Ninth International Conference on Machine Learning and Applications, 2010, no. December 2010, pp. 710–713, doi: 10.1109/ICMLA.2010.109.
[42] D. M. Khairina, F. Ramadhani, S. Maharani, and H. R. Hatta, “Department recommendations for prospective students Vocational High School of information technology with Naïve Bayes method,” in 2015 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 2015, pp. 92–96, doi: 10.1109/ICITACEE.2015.7437777.
[43] L. Melian and A. Nursikuwagus, “Prediction Student Eligibility in Vocation School with Naïve-Byes Decision Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 407, no. 1, p. 012140, Sep. 2018, doi: 10.1088/1757-899X/407/1/012140.
[44] N. L. Cintya Dewi, A. Prasteya Wibawa, and U. Pujianto, “Technology Acceptance Model on Internship Placement Recommendation System Based on Naïve Bayes,” in 2018 International Conference on Sustainable Information Engineering and Technology (SIET), 2018, pp. 151–155, doi: 10.1109/SIET.2018.8693199.
[45] A. D. Herlambang, S. H. Wijoyo, and A. Rachmadi, “Intelligent Computing System to Predict Vocational High School Student Learning Achievement Using Naïve Bayes Algorithm,” J. Inf. Technol. Comput. Sci., vol. 4, no. 1, p. 15, Jun. 2019, doi: 10.25126/jitecs.20194169.
[46] W. K. Dewanto, K. Agustianto, and B. E. Sari, “Developing thinking skill system for modelling creative thinking and critical thinking of vocational high school student,” J. Phys. Conf. Ser., vol. 953, no. 1, p. 012115, Jan. 2018, doi: 10.1088/1742-6596/953/1/012115.
[47] S. Wongpun and A. Srivihok, “Comparison of attribute selection techniques and algorithms in classifying bad behaviors of vocational education students,” in 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, 2008, pp. 526–531, doi: 10.1109/DEST.2008.4635213.
[48] T.-T. Tran, “Forecasting strategies and analyzing the numbers of incoming students: Case in Taiwanese vocational schools,” Int. J. Adv. Appl. Sci., vol. 4, no. 9, pp. 86–95, Sep. 2017, doi: 10.21833/ijaas.2017.09.011.
[49] O. Deperlioglu and F. S. Birtil, “Analysis of Girls Vocational High School Students’ Academic Failure Causes with Data Mining Techniques,” Anthropol., vol. 23, no. 3, pp. 505–512, Mar. 2016, doi: 10.1080/09720073.2014.11891970.
[50] S. Abadi et al., “Application model of k-means clustering: insights into promotion strategy of vocational high school,” Int. J. Eng. Technol., vol. 7, no. 2.27, p. 182, Aug. 2018, doi: 10.14419/ijet.v7i2.11491.
[51] R. J. Salaki, C. R. Kawet, R. Manoppo, and F. Tumimomor, “Decision Support Systems Major Selection Vocational High School in Using Fuzzy Logic Android-Based,” Int. Conf. Electr. Eng. Informatics, Its Educ. 2015, no. October 2015, 2015, Available at: Google Scholar
[52] M. Pilz, J. Li, R. Canning, and S. Minty, “Modularisation approaches in Initial Vocational Education: evidence for policy convergence in Europe?,” J. Vocat. Educ. Train., vol. 70, no. 1, pp. 1–26, Jan. 2018, doi: 10.1080/13636820.2017.1392994.
[53] J. B. G. Tilak, “Vocational Education and Training in Asia,” 2018, pp. 203–220, doi: 10.1007/978-981-13-0250-3_5.
[54] H. Sakai and M. Nakata, “Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination,” CAAI Trans. Intell. Technol., vol. 4, no. 4, pp. 203–213, Dec. 2019, doi: 10.1049/trit.2019.0001.
[55] D. Hartama, A. Perdana Windarto, and A. Wanto, “The Application of Data Mining in Determining Patterns of Interest of High School Graduates,” J. Phys. Conf. Ser., vol. 1339, p. 012042, Dec. 2019, doi: 10.1088/1742-6596/1339/1/012042.
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