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Download full textEaton, Sarah Elaine – Online Submission, 2020
Purpose: This report highlights ways in which race-based data can be used to combat systemic racism in matters relating to academic and non-academic and student misconduct. Methods: Information synthesis of available information relating to race-based data and student conduct. Results: A summary and synthesis of how and why race-based data can be…
Descriptors: Data Collection, Minority Group Students, Racial Bias, Student Behavior
Peer reviewed
Direct linkWitmer, Hope; Johansson, Jonas – International Journal for Educational Integrity, 2015
The purpose of this study is to identify if gender differences exist with respect to conviction of university students for academic dishonesty. To investigate this phenomenon, data from the Swedish National Agency for Higher Education (SNAHE) and from disciplinary boards of several Swedish universities were evaluated from a gender perspective. To…
Descriptors: Ethics, Cheating, Gender Differences, Data Analysis
Diego, Leo Andrew B. – IAFOR Journal of Education, 2017
Cheating during examinations is triggered by peer influence. It makes every learner know and do what should not be done. Cheating during examinations defeats the purpose of understanding, applying and creating ideas as stipulated in the revised Bloom's taxonomy by Anderson. The study reported here was designed to delve into the reasons and…
Descriptors: Foreign Countries, Cheating, Student Evaluation, Junior High School Students
Igbineweka, V. O.; Iguodala, W. A.; Anukaenyi, Blessing Osuigwe – Journal of Education and Learning, 2016
Nigeria, situated in the West African sub-region of the African continent has an estimated population of over 170 million people with 146 universities. The demand for these universities in the recent past has been unprecedented with an average of 1.5 million applicants for placement annually, the highest anywhere in the world. Regrettably, public…
Descriptors: Foreign Countries, Undergraduate Students, Student Behavior, Behavior Problems
Feng, 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
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
Del Carlo, Dawn; Bodner, George – Biochemistry and Molecular Biology Education, 2006
Although reports of academic cheating are abundant, there are relatively few papers in the literature that focus on cheating in the context of science courses and even fewer that address dishonest practices, such as "cooking" or fudging data, within the classroom laboratory. This paper briefly reviews the existing literature on academic dishonesty…
Descriptors: Laboratories, Biochemistry, Molecular Biology, Cheating
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
