УДК 004.9

Д. МУРАТУЛИ,
Західноказахстанський Технічний університет імені Д. Серикбаєва, Усть-Каменогрськ, Казахстан,
 dmuratuly@edu.ektu.kz

Н.Ф. ДЕНІСОВА,
Західноказахстанський Технічний університет імені Д. Серикбаєва, Усть-Каменогрськ, Казахстан,
 ndenisova@edu.ektu.kz

Ю.В. КРАК,
Київський національний університет імені Тараса Шевченка, Київ, Україна,
Інститут кібернетики ім. В.М. Глушкова НАН України, Київ, Україна
 Iurii.krak@knu.ua

ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ ДЛЯ ВИЯВЛЕННЯ
ПРОКТОРОМ ПОРУШЕНЬ ПІД ЧАС ІСПИТУ

СПИСОК ЛІТЕРАТУРИ

1. Pokhrel S., Chhetri R. A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future. 2021. Vol. 8, Iss. 1. P. 133–141. doi.org/10.1177/2347631120983481.


2. UNESCO Global Education Monitoring Report. Policy Brief: Education during COVID-19 and beyond. 2020. URL: https://www.un.org .


3. Hmlinen R., Nissinen K., Mannonen J., Lms J., Leino K., Taajamo M. Understanding teaching professionals’ digital competence: What do PIAAC and TALIS reveal about technology-related skills, attitudes, and knowledge? Computers in Human Behavior. 2021. Vol. 117. 106672. doi.org/10.1016/j.chb.2020.106672.


4. Raghu R., Sairam B., Veena G., Hardik V., Prema N., Adoption of online proctored examinations by university students during COVID-19: Innovation diffusion study. Public Health and Emergency. 2021. Vol. 26. P. 7339–7358. doi.org/10.1007/ s10639-021-10581-5.


5. Cavanaugh J.K., Jacquemin S.J. A large sample comparison of grade based student learning outcomes in online vs face-to-face courses. Online Learning. 2015. Vol. 19, N 2. P. 25–32. doi.org/10.24059/OLJ.V19I2.454.


6. Dendir S. Performance differences between face-to-face and online students in economics. Journal of Education for Business. 2019. Vol. 94, Iss. 3. P. 175–184. doi.org/10.1080/08832323.2018.1503586.


7. Paul J., Jefferson F. A comparative analysis of student performance in an online vs. face-to-face environmental science course from 2009 to 2016. Frontiers in Computer Science. 2019. Vol. 1. 7. P. 1–7. doi.org/10.3389/fcomp.2019.00007 .


8. Rawashdeh A.Z.A., Mohammed E.Y., Al-Arab A.R., Alara M., Al-Rawashdeh B., Al-Rawashdeh B. Advantages and disadvantages of using E-learning in university education: Analyzing students’ perspectives. Academic Conferences and Publishing International Limited. 2021. Vol. 19, N 3. P. 107–117. doi.org/10.34190/ejel.19.3.2168 .


9. Young J.R. Online classes see cheating go high-tech. The Chronicle of Higher Education. 2012. Vol. 58, N 6. P. 24–26. URL: www.chronicle.com .


10. Indi C.S., Pritham K. V., Acharya V., Prakasha K. Detection of malpractice in E-exams by head pose and gaze estimation. International Journal of Emerging Technologies in Learning. 2021. Vol. 16, N 8. P. 47–60. doi.org/10.3991/ijet.v16i08.15995.


11. Prokes C., Lowenthal P.R., Snelson C., Rice K. Faculty views of CBE, self-efficacy, and institutional support: An exploratory study. The Journal of Competency-Based Education. 2021. Vol. 6, Iss. 4. P. 233–244. doi.org/10.1002/cbe2.1263.


12. Alessio H.M., Malay N.J., Maurer K., Bailer A.J., Rubin B. Examining the effect of proctoring on online test scores. Online Learning. 2017. Vol. 21, Iss. 1. P. 146–161. doi.org/10.24059/olj.v21i1.885.


13. Norris M. University online cheating — how to mitigate the damage. Research in Higher Education Journal. 2019. Vol. 37. P. 1–20. URL: https://files.eric.ed.gov .


14. Golden J., Kohlbeck M. Addressing cheating when using test bank questions in online classes. Journal of Accounting Education. 2020. Vol. 52. 100671. doi.org/10.1016/j.jaccedu.2020.100671.


15. Moini A., Madni A.M. Leveraging biometrics for user authentication in online learning: a systems perspective. IEEE Systems Journal. 2009. Vol. 3, Iss. 4. P. 469–476. doi.org/10.1109/JSYST.2009.2038957 .


16. Anju A., Preeti G. Clustering in Big Data: A review. International Journal of Computer Applications. 2016. Vol. 153, N 3. P. 44–47. doi.org/10.5120/ijca2016911994.


17. Krak Yu.V., Barmak O.V., Mazurets O.V. The practice implementation of the information technology for automated definition of semantic terms sets in the content of educational materials. Problems in Programming. 2018. Vol. 2–3. P. 245–254. doi.org/10.15407/pp2018.02.245.


18. Han E-H., Karypis G. Centroid-based document classification: analysis and experimental results. Proc. 4th European Conference on Principles of Data Mining and Knowledge Discovery (13–16 September 2000, Lyon, France). Lyon, 2000. Lecture Notes in Computer Science. 2000. Vol. 1910. P. 424–431. doi.org/10.1007/3-540-45372-5_46 .


19. Krak Iu.V., Kudin G.I., Kulias A.I. Multidimensional scaling by means of pseudoinverse operations. Cybernetics and Systems Analysis. 2019. Vol. 55, N 1. P. 22–29. .


20. Krak Iu., Barmak O., Manziuk E. Using visual analytics to develop human and machine-centric models: A review of approaches and proposed information technology. Computitional Intelligence. 2020. Vol. 36, Iss. 3. P. 1–26. doi.org/10.1111/coin.12289.


21. 21. Kennan S., Bigatel P., Stockdale S., Hoewe J. The (lack of) influence of age and class standing on preferred teaching behaviors for online students. Online Learn. 2018. Vol. 22, Iss. 1. P. 163–181. doi.org/10.24059/olj.v22i1.1086 .