TY - GEN
T1 - Developing an Open-Book Online Exam for Final Year Students
AU - Quille, Keith
AU - Nolan, Keith
AU - Becker, Brett A.
AU - McHugh, Seán
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/6/26
Y1 - 2021/6/26
N2 - Like many others, our institution had to adapt our traditional proctored, written examinations to open-book online variants due to theCOVID-19 pandemic. This paper describes the process applied to develop open-book online exams for final year (undergraduate)students studying Applied Machine Learning and Applied Artificial Intelligence and Deep Learning courses as part of a four-year BSc in Computer Science. We also present processes used to validate the examinations as well as plagiarism detection methods implemented. Findings from this study highlight positive effects of using open-book online exams, with ∼85% of students reporting that they either prefer online open-book examinations or have no preference between traditional and open-book exams. There were no statistically significant differences reported comparing the exam results of student cohorts who took the open-book online examination, compared to previous cohorts who sat traditional exams. These results are of value to the CSEd community for three reasons. First, it outlines a methodology for developing online open-book exams(including publishing the open-book online exam papers as samples). Second, it provides approaches for deterring plagiarism and implementing plagiarism detection for open-book exams. Finally, we present feedback from students which may be used to guidefuture online open-book exam development.
AB - Like many others, our institution had to adapt our traditional proctored, written examinations to open-book online variants due to theCOVID-19 pandemic. This paper describes the process applied to develop open-book online exams for final year (undergraduate)students studying Applied Machine Learning and Applied Artificial Intelligence and Deep Learning courses as part of a four-year BSc in Computer Science. We also present processes used to validate the examinations as well as plagiarism detection methods implemented. Findings from this study highlight positive effects of using open-book online exams, with ∼85% of students reporting that they either prefer online open-book examinations or have no preference between traditional and open-book exams. There were no statistically significant differences reported comparing the exam results of student cohorts who took the open-book online examination, compared to previous cohorts who sat traditional exams. These results are of value to the CSEd community for three reasons. First, it outlines a methodology for developing online open-book exams(including publishing the open-book online exam papers as samples). Second, it provides approaches for deterring plagiarism and implementing plagiarism detection for open-book exams. Finally, we present feedback from students which may be used to guidefuture online open-book exam development.
KW - artificial intelligence
KW - machine learning
KW - online exam
KW - open-book
UR - http://www.scopus.com/inward/record.url?scp=85109021411&partnerID=8YFLogxK
U2 - 10.1145/3430665.3456373
DO - 10.1145/3430665.3456373
M3 - Conference contribution
AN - SCOPUS:85109021411
T3 - Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE
SP - 338
EP - 344
BT - ITiCSE 2021 - Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education
PB - Association for Computing Machinery
T2 - 26th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2021
Y2 - 26 June 2021 through 1 July 2021
ER -