College Students’ Perception and Concerns regarding Online Examination amid COVID-19

HeeJeong Jasmine Lee, Mee Hong Ling, Kok‐Lim Alvin Yau


Growing concerns about online examinations have led to various investigations of techniques for improvement. With most higher education institutions shifting to online learning and examination amid COVID-19, these concerns, including the academic dishonesty, validity, reliability, and anxiety of online examination, are more critical than ever. This paper presents the outcomes of the survey to elicit the perceptions of undergraduate students from two universities in South Korea and Malaysia towards undertaking online exams and the associated concerns. Additionally, the study explores the potential of artificial intelligence (AI) in addressing these concerns. There are three main research questions: 1) How has AI been adopted to tackle the four main concerns in online exams? 2) What are the students’ perceptions regarding these concerns? Are there any differences between South Korean and Malaysian students? 3) What is the extent of the stress level when webcam proctoring and timers are implemented during online exams? The survey results show that both South Korean and Malaysian students agree that online exams make cheating more accessible than in-person exams. They also suggest that selecting questions randomly from a question bank could discourage cheating. Moreover, the study highlights that both groups of students experience moderate stress levels when webcam proctoring is used over Zoom during online exams, and they experience a high-stress level when timers are set for each question.


Artificial intelligence; higher education; academic dishonesty; online examination; online assessments

Full Text:



M. A. Almaiah, A. Al-Khasawneh, and A. Althunibat, “Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic,†Educ. Inf. Technol., vol. 25, pp. 5261–5280, 2020.

O. B. Adedoyin and E. Soykan, “Covid-19 pandemic and online learning: the challenges and opportunities,†Interact. Learn. Environ., pp. 1–13, 2020.

N. Rajeh Alsalhi, A. Darweesh Qusef, S. Sulieman Al-Qatawneh, and M. Elmagzoub Eltahir, “Students’ Perspective on Online Assessment during the COVID-19 Pandemic in Higher Education Institutions,†Inf. Sci. Lett., vol. 11, no. 1, p. 10, 2022.

M. Garg and A. Goel, “A systematic literature review on online assessment security: Current challenges and integrity strategies,†Comput. Secur., vol. 113, p. 102544, 2022.

S. Ampuni, N. Kautsari, M. Maharani, S. Kuswardani, and S. B. S. Buwono, “Academic dishonesty in Indonesian college students: An investigation from a moral psychology perspective,†J. Acad. Ethics, vol. 18, no. 4, pp. 395–417, 2020.

F. Choo and K. Tan, “Abrupt academic dishonesty: Pressure, opportunity, and deterrence,†Int. J. Manag. Educ., vol. 21, no. 2, p. 100815, 2023.

R. Awdry and A. Groves, “Why they do and why they don’t: a combined criminological approach to understanding assignment outsourcing in higher education,†Int. J. Educ. Integr., vol. 19, no. 1, p. 7, 2023.

Y. K. Dwivedi et al., “‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,†Int. J. Inf. Manage., vol. 71, p. 102642, 2023.

J.-J. Choi, C. A. Robb, M. Mifli, and Z. Zainuddin, “University students’ perception to online class delivery methods during the COVID-19 pandemic: A focus on hospitality education in Korea and Malaysia,†J. Hosp. Leis. Sport Tour. Educ., vol. 29, p. 100336, 2021.

C. S. González-González, A. Infante-Moro, and J. C. Infante-Moro, “Implementation of E-proctoring in Online Teaching: A Study About Motivational Factors,†Sustainability, vol. 12, no. 8, p. 3488, 2020.

H. Corrigan-Gibbs, N. Gupta, C. Northcutt, E. Cutrell, and W. Thies, “Deterring cheating in online environments,†ACM Trans. Comput. Interact., vol. 22, no. 6, pp. 1–23, 2015.

H. Li and H. Wang, “Research on the application of artificial intelligence in education,†in 2020 15th International Conference on Computer Science & Education (ICCSE), 2020, pp. 589–591.

L. Lin, H. Liu, W. Zhang, F. Liu, and Z. Lai, “Finger Vein Verification using Intrinsic and Extrinsic Features,†in 2021 IEEE International Joint Conference on Biometrics (IJCB), 2021, pp. 1–7.

K. H. Teoh, R. C. Ismail, S. Z. M. Naziri, R. Hussin, M. N. M. Isa, and M. Basir, “Face Recognition and Identification using Deep Learning Approach,†in Journal of Physics: Conference Series, 2021, vol. 1755, no. 1, p. 12006.

R. S. Kuzu, E. Maiorana, and P. Campisi, “Vein-Based Biometric Verification Using Densely-Connected Convolutional Autoencoder,†IEEE Signal Process. Lett., vol. 27, pp. 1869–1873, 2020.

M. Ghizlane, B. Hicham, and F. H. Reda, “A New Model of Automatic and Continuous Online Exam Monitoring,†in 2019 International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), 2019, pp. 1–5.

H. S. G. Asep and Y. Bandung, “A Design of Continuous User Verification for Online Exam Proctoring on M-Learning,†in 2019 International Conference on Electrical Engineering and Informatics (ICEEI), 2019, pp. 284–289.

K. Garg, K. Verma, K. Patidar, and N. Tejra, “Convolutional Neural Network based Virtual Exam Controller,†in 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 2020, pp. 895–899.

M. Ghizlane, F. H. Reda, and B. Hicham, “A Smart Card Digital Identity Check Model for University Services Access,†in Proceedings of the 2Nd International Conference on Networking, Information Systems & Security, 2019, pp. 1–4.

F. Sabrina, S. Azad, S. Sohail, and S. Thakur, “Ensuring Academic Integrity in Online Assessments: A Literature Review and Recommendations,†Int. J. Inf. Educ. Technol., vol. 12, no. 1, 2022.

S. Singh, A. Inamdar, A. Kore, and A. Pawar, “Analysis of Algorithms for User Authentication using Keystroke Dynamics,†in 2020 International Conference on Communication and Signal Processing (ICCSP), 2020, pp. 337–341.

P. Maharjan et al., “Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence,†Adv. Sci., p. 2100711, 2021.

S. Mihalache, I.-A. Ivanov, and D. Burileanu, “Deep Neural Networks for Voice Activity Detection,†in 2021 44th International Conference on Telecommunications and Signal Processing (TSP), 2021, pp. 191–194.

A. R. Naini, M. Satyapriya, and P. K. Ghosh, “Whisper Activity Detection Using CNN-LSTM Based Attention Pooling Network Trained for a Speaker Identification Task.,†in INTERSPEECH, 2020, pp. 2922–2926.

K. Krishna, Y. Song, M. Karpinska, J. Wieting, and M. Iyyer, “Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense,†arXiv Prepr. arXiv2303.13408, 2023.

J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein, “A watermark for large language models,†arXiv Prepr. arXiv2301.10226, 2023.

S. K. Hodges, “Academic Dishonesty in Higher Education: Perceptions and Opinions of Undergraduates,†2017.

P. Kiekkas et al., “Reasons for academic dishonesty during examinations among nursing students: Cross-sectional survey,†Nurse Educ. Today, vol. 86, p. 104314, 2020.

G. Cebrián, R. Palau, and J. Mogas, “The Smart Classroom as a means to the development of ESD methodologies,†Sustainability, vol. 12, no. 7, p. 3010, 2020.

A. Gadekar, S. Oak, A. Revadekar, and A. V Nimkar, “MMAP: A Multi-Modal Automated Online Proctor,†in International Conference on Machine Learning and Big Data Analytics, 2021, pp. 314–325.

T. Nishikawa and H. Miwa, “Algorithm Based on Local Search Method for Examination Proctors Assignment Problem Considering Various Constraints,†in International Conference on Intelligent Networking and Collaborative Systems, 2021, pp. 23–31.

X.-L. Zou and L. Ou, “EFL reading test on mobile versus on paper: a study from metacognitive strategy use to test-media impacts,†Educ. Assessment, Eval. Account., vol. 32, no. 3, pp. 373–394, 2020.

J. Muangprathub, V. Boonjing, and K. Chamnongthai, “Learning recommendation with formal concept analysis for intelligent tutoring system,†Heliyon, vol. 6, no. 10, p. e05227, 2020.

A. Fayyoumi and K. Suwais, “Online exam questions distribution technique based on terminals locations: The case of Arab Open University,†J. Comput. Model., vol. 5, no. 1, pp. 13–25, 2015.

B. Fierro, K. Detweiler, and S. Detweiler, “Evaluating mental illness among college students: Implications for online students,†J. Online High. Educ., vol. 4, no. 1, pp. 35–46, 2020.

L. K. Muthén and B. O. Muthén, “How to use a Monte Carlo study to decide on sample size and determine power,†Struct. Equ. Model., vol. 9, no. 4, pp. 599–620, 2002.

J. A. Gliem and R. R. Gliem, “Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales,†2003.

O. Awosoga, C. M. Nord, S. Varsanyi, R. Barley, and J. Meadows, “Student and faculty perceptions of, and experiences with, academic dishonesty at a medium-sized Canadian university,†Int. J. Educ. Integr., vol. 17, no. 1, pp. 1–26, 2021.

H. Apostolidis and T. Tsiatsos, “Exploring anxiety awareness during academic science examinations,†PLoS One, vol. 16, no. 12, p. e0261167, 2021.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development