TY - GEN
T1 - Crowd-Sourced Supervisors for the Automatic Invigilation of Online Assessments
AU - Visentin, Nicholas Angelo
AU - Mhlongo, Siyabonga
AU - Ade-Ibijola, Abejide
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - An increase in digitalisation and the compounded effects of the COVID-19 pandemic have forced educational institutions to adopt digital solutions for supervising online assessments. Misconduct in online assessments is increasing as institutions compromise academic integrity to remain operational. Implementing the right tools to mitigate the risk of academic dishonesty has become the priority in ensuring academic integrity. Existing proctoring tools are intrusive, less privacy-conscious and operate in a space that has limited to no standards. Due to the state of current proctoring tools, there is a lack of adequate supervision solutions, novel enough to deal with the issues of academic misconduct. This paper proposes an algorithm called Crowd-Vision, encapsulated in a web-based tool and powered by crowd-sourced supervisors, to decrease levels of academic dishonesty in online assessments. Crowd-Vision uses various configurable assessment parameters to simulate an assessment environment balanced with both real and generated invigilators. The evaluation of the web-based tool revealed that the tool has the potential to mitigate academic dishonesty.
AB - An increase in digitalisation and the compounded effects of the COVID-19 pandemic have forced educational institutions to adopt digital solutions for supervising online assessments. Misconduct in online assessments is increasing as institutions compromise academic integrity to remain operational. Implementing the right tools to mitigate the risk of academic dishonesty has become the priority in ensuring academic integrity. Existing proctoring tools are intrusive, less privacy-conscious and operate in a space that has limited to no standards. Due to the state of current proctoring tools, there is a lack of adequate supervision solutions, novel enough to deal with the issues of academic misconduct. This paper proposes an algorithm called Crowd-Vision, encapsulated in a web-based tool and powered by crowd-sourced supervisors, to decrease levels of academic dishonesty in online assessments. Crowd-Vision uses various configurable assessment parameters to simulate an assessment environment balanced with both real and generated invigilators. The evaluation of the web-based tool revealed that the tool has the potential to mitigate academic dishonesty.
KW - Automated supervision
KW - Crowd-sourced invigilation
KW - Online assessment
KW - Online proctoring
KW - Proctoring algorithm
UR - http://www.scopus.com/inward/record.url?scp=85202290194&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-68617-7_26
DO - 10.1007/978-3-031-68617-7_26
M3 - Conference contribution
AN - SCOPUS:85202290194
SN - 9783031686160
T3 - Communications in Computer and Information Science
SP - 359
EP - 377
BT - Artificial Intelligence and Knowledge Processing - 3rd International Conference, AIKP 2023, Revised Selected Papers
A2 - K, Hemachandran
A2 - Rodriguez, Raul Villamarin
A2 - Rege, Manjeet
A2 - Piuri, Vincenzo
A2 - Xu, Guandong
A2 - Ong, Kok-Leong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Artificial Intelligence and Knowledge Processing, AIKP 2023
Y2 - 6 October 2023 through 8 October 2023
ER -