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Direct linkSelbach-Allen, Megan E.; Greenwald, Sarah J.; Ksir, Amy E.; Thomley, Jill E. – PRIMUS, 2020
In this paper, we compare and contrast our experiences in using standards-based grading in different courses and across two separate institutions to explore the related tradeoffs and subtleties in designing and implementing such grading systems, guided by innovation diffusion theory. We summarize our individual courses, use Linda Nilson's criteria…
Descriptors: Grading, Standards, Evaluation Methods, College Faculty
Petress, Ken – Education, 2008
Recently, the author had cause to discuss with a group of his freshman college students the issue of reporting observed or known academic dishonesty. He was very surprised and deeply saddened by the responses most students shared with him on this issue. In short, most--80%--stated that "others' cheating was none of their business;" "It is the…
Descriptors: Student Attitudes, Cheating, Longitudinal Studies, Student Responsibility
Lin, Chun-Hua Susan; Wen, Ling-Yu Melody – Higher Education: The International Journal of Higher Education and Educational Planning, 2007
Academic dishonesty has been an important issue. However, only few researches had been done in Asian countries, especially a nationwide study. A sample of 2,068 college students throughout Taiwan was selected and surveyed on four domains of academic dishonesty, including: cheating on test, cheating on assignment, plagiarism, and falsifying…
Descriptors: Cheating, College Students, Foreign Countries, Ethics
Download full textHu, 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
