Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan
The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/11/6/120 |
| _version_ | 1797595396663410688 |
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| author | Ardak Nurpeisova Anargul Shaushenova Zhazira Mutalova Maral Ongarbayeva Shakizada Niyazbekova Anargul Bekenova Lyazzat Zhumaliyeva Samal Zhumasseitova |
| author_facet | Ardak Nurpeisova Anargul Shaushenova Zhazira Mutalova Maral Ongarbayeva Shakizada Niyazbekova Anargul Bekenova Lyazzat Zhumaliyeva Samal Zhumasseitova |
| author_sort | Ardak Nurpeisova |
| collection | DOAJ |
| description | The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use of outside help. Online proctoring based on artificial intelligence technologies in distance education is an effective technological solution to prevent academic dishonesty. This article explores the development and implementation of an online control proctoring system using artificial intelligence technology for conducting online exams. The article discusses the proctoring systems used in Kazakhstan, compares the functional features of the selected proctoring systems, and describes the architecture of Proctor SU. A prototype of the Proctor SU proctoring system has been developed. As a pilot program, the authors used this system during an online university exam and examined the results of the test. According to the author’s examination, students have a positive attitude towards the use of Proctor SU online proctoring. The proposed proctor system includes features of face detection, face tracking, audio capture, and the active capture of system windows. Models CNN, R-CNN, and YOLOv3 were used in the development process. The YOLOv3 model processed images in real time at 45 frames per second, and CNN and R-CNN processed images in real time at 30 and 38 frames per second. The YOLOv3 model showed better results in terms of real-time face recognition. Therefore, the YOLOv3 model was implemented into the Proctor SU proctoring system. |
| first_indexed | 2024-03-11T02:36:53Z |
| format | Article |
| id | doaj.art-751bdc7405e149d082509cdc5fd49351 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-11T02:36:53Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-751bdc7405e149d082509cdc5fd493512023-11-18T09:54:09ZengMDPI AGComputation2079-31972023-06-0111612010.3390/computation11060120Research on the Development of a Proctoring System for Conducting Online Exams in KazakhstanArdak Nurpeisova0Anargul Shaushenova1Zhazira Mutalova2Maral Ongarbayeva3Shakizada Niyazbekova4Anargul Bekenova5Lyazzat Zhumaliyeva6Samal Zhumasseitova7Department of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical University, Nur-Sultan 010000, KazakhstanDepartment of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical University, Nur-Sultan 010000, KazakhstanInstitute of Economics, Information Technologies and Professional Education, Higher School of Information Technologies, Zhangir Khan West Kazakhstan Agrarian Technical University, Uralsk 090000, KazakhstanDepartment of Information-Communication Technology, Faculty of Natural Sciences, International Taraz Innovative Institute, Taraz 080000, KazakhstanDepartment of Banking and Monetary Regulation, Financial University under the Government of the Russian Federation, Moscow 125993, RussiaInstitute of Economics, Information Technologies and Professional Education, Higher School of Information Technologies, Zhangir Khan West Kazakhstan Agrarian Technical University, Uralsk 090000, KazakhstanDepartment of Mathematics in Education, M.Kh. Dulaty Regional University, Taraz 080000, KazakhstanDepartment of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical University, Nur-Sultan 010000, KazakhstanThe demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use of outside help. Online proctoring based on artificial intelligence technologies in distance education is an effective technological solution to prevent academic dishonesty. This article explores the development and implementation of an online control proctoring system using artificial intelligence technology for conducting online exams. The article discusses the proctoring systems used in Kazakhstan, compares the functional features of the selected proctoring systems, and describes the architecture of Proctor SU. A prototype of the Proctor SU proctoring system has been developed. As a pilot program, the authors used this system during an online university exam and examined the results of the test. According to the author’s examination, students have a positive attitude towards the use of Proctor SU online proctoring. The proposed proctor system includes features of face detection, face tracking, audio capture, and the active capture of system windows. Models CNN, R-CNN, and YOLOv3 were used in the development process. The YOLOv3 model processed images in real time at 45 frames per second, and CNN and R-CNN processed images in real time at 30 and 38 frames per second. The YOLOv3 model showed better results in terms of real-time face recognition. Therefore, the YOLOv3 model was implemented into the Proctor SU proctoring system.https://www.mdpi.com/2079-3197/11/6/120online examonline proctoringremote teachinginformation technologyartificial intelligence based automated exam proctoring systemsdistance learning |
| spellingShingle | Ardak Nurpeisova Anargul Shaushenova Zhazira Mutalova Maral Ongarbayeva Shakizada Niyazbekova Anargul Bekenova Lyazzat Zhumaliyeva Samal Zhumasseitova Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan Computation online exam online proctoring remote teaching information technology artificial intelligence based automated exam proctoring systems distance learning |
| title | Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan |
| title_full | Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan |
| title_fullStr | Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan |
| title_full_unstemmed | Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan |
| title_short | Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan |
| title_sort | research on the development of a proctoring system for conducting online exams in kazakhstan |
| topic | online exam online proctoring remote teaching information technology artificial intelligence based automated exam proctoring systems distance learning |
| url | https://www.mdpi.com/2079-3197/11/6/120 |
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