Proceedings of the 5th International Conference on Current Issues in Education (ICCIE 2021)

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Predicting Engineering Students’ Grade on Introductory Physics Using Machine Learning

Authors
Purwoko Haryadi Santoso1, 2, *, Syamsul Bahri3, Wahyudi4, Johan Syahbrudin1
1Educational Research and Evaluation, Postgraduate Program, Universitas Negeri Yogyakarta, Indonesia
2Physics Education, Faculty of Teacher Training and Education, Universitas Sulawesi Barat, Indonesia
3Physics Education, Faculty of Teacher Training and Education, Universitas Musamus, Indonesia
4SMK N 1 Budong-Budong, Central Mamuju
*Corresponding author. Email: purwokoharyadi.2021@student.uny.ac.id
Corresponding Author
Purwoko Haryadi Santoso
Available Online 31 January 2022.
DOI
10.2991/assehr.k.220129.018How to use a DOI?
Keywords
Course; Machine learning; Physics; Predition
Abstract

Introductory physics is a compulsory course for the first-year engineering college for providing students the underlying concepts of the future course throughout their study. A sudden shift of distance learning during the disruption of COVID-19 in the middle of 2021 has generated an extensive collection of educational data that can potentially be mined for educational purposes. Educational data mining (EDM), a branch of machine learning research, has offered some tools to perform this task. In this study, a logistic regression classifier was employed to early identify students’ performance in introductory physics courses for engineering majors. Data were collected at a public university (N=180) through a learning management system engaged throughout a semester. This study successfully trained the model with an 80% identification rate to predict the low-performing student in the course. The finding is necessary for the educator to do the review and give feedback to their class for providing some help, particularly for the low-performing student. It is suggested for the further development of the model to make prediction more accurate with another model ensemble that has been proven decisive in the recent study of machine learning.

Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 5th International Conference on Current Issues in Education (ICCIE 2021)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
31 January 2022
ISBN
978-94-6239-525-1
ISSN
2352-5398
DOI
10.2991/assehr.k.220129.018How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Purwoko Haryadi Santoso
AU  - Syamsul Bahri
AU  - Wahyudi
AU  - Johan Syahbrudin
PY  - 2022
DA  - 2022/01/31
TI  - Predicting Engineering Students’ Grade on Introductory Physics Using Machine Learning
BT  - Proceedings of the 5th International Conference on Current Issues in Education (ICCIE 2021)
PB  - Atlantis Press
SP  - 97
EP  - 102
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220129.018
DO  - 10.2991/assehr.k.220129.018
ID  - Santoso2022
ER  -