Development of machine learning implementation in engineering education: A literature review

Open

F. Sasmita, B. Mulyanti

2020 IOP Conference Series: Materials Science and Engineering Vol. 830 Issue 3 Conference paper Cited by 2 SDG 4 Quartile

Abstract

This study has aims to determine the development of implementing machine learning in several engineering majors. The used method was a literature study, and secondary data was used from reputable international journals and published in 2015 to 2019 from each publisher, which is IEEEXplore, Springer Link, Science Direct, ERIC, and Google Scholar. The author was summarized and analysed articles obtained based on the year of publication and the context of the article. Results show that machine learning has been widely applied in engineering education through fourteen contexts, one of which is Prediction Student Academic Performance, which has continuous development from 2013 to 2019. And the total number of engineering majors that are implementing machine learning was 13 majors. This research was expected to be an illustration, reference, and consideration for technicians in engineering education to give more attention and can be applied in schools, universities, and other engineering institutions in Indonesia country. © Published under licence by IOP Publishing Ltd.

Affiliations

Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Kec. Sukasari, Jawa Barat, Isola, Bandung, 40154, Indonesia

Research at a Glance

Premium content — register to unlock

Research at a Glance

Register to unlock

Topics & SDG Alignment

Premium content — register to unlock

Topics & SDG Alignment

Register to unlock

Collaboration

Premium content — register to unlock

Collaboration

Register to unlock

Author Profile (Selected)

Premium content — register to unlock

Author Profile (Selected)

Register to unlock

References Overview

Premium content — register to unlock

References Overview

Register to unlock

Journal & Source

Premium content — register to unlock

Journal & Source

Register to unlock

Metadata & Integrity

Premium content — register to unlock

Metadata & Integrity

Register to unlock