Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection

Yulita, Intan Nurma; Hariz, Fauzan Akmal; Suryana, Ino; Prabuwono, Anton Satria

doi:10.3390/educsci13020194

Open AccessArticle

Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection

by

Intan Nurma Yulita

^1,*

,

Fauzan Akmal Hariz

²,

Ino Suryana

² and

Anton Satria Prabuwono

³

¹

Research Center for Artificial Intelligence and Big Data, Universitas Padjadjaran, Bandung 40132, Indonesia

²

Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia

³

Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Rabigh 21911, Saudi Arabia

^*

Author to whom correspondence should be addressed.

Educ. Sci. 2023, 13(2), 194; https://doi.org/10.3390/educsci13020194

Submission received: 28 December 2022 / Revised: 5 February 2023 / Accepted: 9 February 2023 / Published: 12 February 2023

(This article belongs to the Special Issue The Role of Technology in Teaching, Learning, and Assessment during and Post-COVID-19: Opportunities for Innovation and Challenges)

Download

Browse Figures

Versions Notes

Abstract

Because the COVID-19 epidemic has limited human activities, it has touched almost every sector. Education is one of the most affected areas. To prevent physical touch between students, schools and campuses must adapt their complete learning system to an online environment. The difficulty with this technique arises when the teachers or lecturers administer exams. It is difficult to oversee pupils one by one online. This research proposes the development of a computer program to aid in this effort. By applying deep learning models, this program can detect a person’s activities during an online exam based on a web camera. The reliability of this system is 84.52% based on the parameter F1-score. This study built an Indonesian-language web-based application. Teachers and lecturers in Indonesia can use this tool to evaluate whether students are cheating on online exams. Unquestionably, this application is a tool that may be utilized to develop distance learning educational technology in Indonesia.

Keywords: COVID-19; deep learning; web-based application; online exams

Share and Cite

MDPI and ACS Style

Yulita, I.N.; Hariz, F.A.; Suryana, I.; Prabuwono, A.S. Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection. Educ. Sci. 2023, 13, 194. https://doi.org/10.3390/educsci13020194

AMA Style

Yulita IN, Hariz FA, Suryana I, Prabuwono AS. Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection. Education Sciences. 2023; 13(2):194. https://doi.org/10.3390/educsci13020194

Chicago/Turabian Style

Yulita, Intan Nurma, Fauzan Akmal Hariz, Ino Suryana, and Anton Satria Prabuwono. 2023. "Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection" Education Sciences 13, no. 2: 194. https://doi.org/10.3390/educsci13020194

APA Style

Yulita, I. N., Hariz, F. A., Suryana, I., & Prabuwono, A. S. (2023). Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection. Education Sciences, 13(2), 194. https://doi.org/10.3390/educsci13020194

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI