Predicting Engineering Students’ Grade on Introductory Physics Using Machine Learning
- 10.2991/assehr.k.220129.018How to use a DOI?
- Course; Machine learning; Physics; Predition
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.
- © 2022 The Authors. Published by Atlantis Press SARL.
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Cite this article
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 -