International Journal of Reciprocal Symmetry and Theoretical Physics

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.

 

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