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Peer Reviewed Article

Vol. 6 (2019)

Expansion of Machine Learning Employment in Engineering Learning: A Review of Selected Literature

Published
2019-03-01

Abstract

Articles were summarized and analyzed by the author based on the year of publication and the context in which the article was published, among other factors. The purpose of this study is to determine the progress made in the implementation of machine learning in a variety of engineering fields. The research approach employed was a literature review, and secondary data was gathered from renowned international journals that were indexed by Google Scholar during the process. As a result of the findings, machine learning has been widely implemented in engineering education across fourteen domains, with one of the most significant being Prediction Student Academic Performance, which has shown constant progress from 2013 to 2018. Furthermore, the total number of engineering majors who are implementing machine learning is thirteen majors in total. According to the researchers' expectations, this research will serve as an illustration, reference, and consideration for technicians in engineering education to pay greater attention to, and it will be applicable in schools, universities, and other engineering institutions throughout Indonesia.

 

References

  1. Pasupuleti, M. B. (2016b). Data Scientist Careers: Applied Orientation for the Beginners. Global Disclosure of Economics and Business, 5(2), 125-132. https://doi.org/10.18034/gdeb.v5i2.617.
  2. Onar S C, Ustundag A, Kadaifci Ç and Oztaysi B 2018 The changing role of engineering education in industry 4.0 Era In Industry 4.0: Managing The Digital Transformation 137-151
  3. Pasupuleti, M. B. (2015b). Problems from the Past, Problems from the Future, and Data Science Solutions. ABC Journal of Advanced Research, 4(2), 153-160. https://doi.org/10.18034/abcjar.v4i2.614
  4. Pasupuleti, M. B. (2015c). Stimulating Statistics in the Epoch of Data-Driven Innovations and Data Science. Asian Journal of Applied Science and Engineering, 4, 251–254. Retrieved from https://upright.pub/index.php/ajase/article/view/55
  5. Jiang Y, Baker R S, Paquette L, San Pedro M and Heffernan N T 2015 Learning, moment-by-moment and over the long term In International Conference on Artificial Intelligence in Education 654-657
  6. Samuel A L 1959 Some studies in machine learning using the game of checkers IBM Journal of research and development 3(3) 210-229
  7. Adusumalli, H. P., & Pasupuleti, M. B. (2017). Applications and Practices of Big Data for Development. Asian Business Review, 7(3), 111-116. https://doi.org/10.18034/abr.v7i3.597
  8. Adusumalli, H. P. (2017b). Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives. ABC Journal of Advanced Research, 6(2), 121-126. https://doi.org/10.18034/abcjar.v6i2.618
  9. Pasupuleti, M. B. (2015a). Data Science: The Sexiest Job in this Century. International Journal of Reciprocal Symmetry and Physical Sciences, 2, 8–11. Retrieved from https://upright.pub/index.php/ijrsps/article/view/56
  10. Pasupuleti, M. B., & Adusumalli, H. P. (2018). Digital Transformation of the High-Technology Manufacturing: An Overview of Main Blockades. American Journal of Trade and Policy, 5(3), 139-142. https://doi.org/10.18034/ajtp.v5i3.599
  11. Chrysafiadi K and Virvou M 2013 Student modeling approaches: A literature review for the last decade Expert Systems with Applications 40(11), 4715-4729.
  12. Pasupuleti, M. B. (2016a). The Use of Big Data Analytics in Medical Applications. Malaysian Journal of Medical and Biological Research, 3(2), 111-116. https://doi.org/10.18034/mjmbr.v3i2.615
  13. Adusumalli, H. P. (2017a). Mobile Application Development through Design-based Investigation. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 14–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/58
  14. Bacos C A 2018 Machine Learning and Education in the Human Age: A Review of Emerging Technologies (Springer International Publishing)
  15. Adusumalli, H. P. (2016b). How Big Data is Driving Digital Transformation?. ABC Journal of Advanced Research, 5(2), 131-138. https://doi.org/10.18034/abcjar.v5i2.616
  16. Kučak D, Juričić V and Đambić G 2018 MACHINE LEARNING IN EDUCATION-A SURVEY OF CURRENT RESEARCH TRENDS Annals of DAAAM & Proceedings 29
  17. Adusumalli, H. P. (2016a). Digitization in Production: A Timely Opportunity. Engineering International, 4(2), 73-78. https://doi.org/10.18034/ei.v4i2.595
  18. Lau F and Kuziemsky C 2016 Chapter 9 Methods for Literature Reviews in Handbook of eHealth Evaluation: An Evidence-based Approach (Canada: University of Victoria)
  19. Heredia D, Amaya Y and Barrientos E 2015 Student dropout predictive model using data mining techniques IEEE Latin America Transactions 13(9) 3127-3134
  20. Passey D 2017 Computer science (CS) in the compulsory education curriculum: Implications for future research Education and Information Technologies 22(2) 421-443
  21. Asif R, Merceron A, Ali S A and Haider N G 2017 Analyzing undergraduate students' performance using educational data mining Computers & Education 113 177-194
  22. Adusumalli, H. P. (2018). Digitization in Agriculture: A Timely Challenge for Ecological Perspectives. Asia Pacific Journal of Energy and Environment, 5(2), 97-102. https://doi.org/10.18034/apjee.v5i2.619
  23. Yildiz O, Bal A and Gulsecen S 2013 Improved fuzzy modelling to predict the academic performance of distance education students The international review of research in open and distributed learning 14(5)
  24. Fadziso, T., Adusumalli, H. P., & Pasupuleti, M. B. (2018). Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews. Asian Journal of Applied Science and Engineering, 7, 85–92. Retrieved from https://upright.pub/index.php/ajase/article/view/63