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Direct linkRobert Louis DeFranco – ProQuest LLC, 2023
Academic dishonesty poses a challenge for the online and campus-based learning environment where technology and assessment at a distance may encourage easy and innovative ways of cheating. The purpose of this quantitative study was to assess campus-based and online students' attitudes and perceptions toward academic dishonesty. Data were collected…
Descriptors: Undergraduate Students, Student Attitudes, Ethics, Integrity
Peer reviewedBeck, Victoria – Active Learning in Higher Education, 2014
Much has been written about student and faculty opinions on academic integrity in testing. Currently, concerns appear to focus more narrowly on online testing, generally based on anecdotal assumptions that online students are more likely to engage in academic dishonesty in testing than students in traditional on-campus courses. To address such…
Descriptors: College Students, College Faculty, Student Attitudes, Teacher Attitudes
Download full textFeng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
Harmon, Oskar R.; Lambrinos, James; Kennedy, Peter, Ed. – Journal of Economic Education, 2008
In this study, the authors use data from two online courses in principles of economics to estimate a model that predicts exam scores from independent variables of student characteristics. In one course, the final exam was proctored, and in the other course, the final exam was not proctored. In both courses, the first three exams were unproctored.…
Descriptors: Cheating, Online Courses, Student Characteristics, Supervision
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
