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Seeland, Josh; Cliplef, Lynn; Munn, Caitlin; Dedrick, Craig – International Journal of Mathematical Education in Science and Technology, 2022
Academic integrity at our small Canadian college is informed by several key frameworks and centred around teaching, learning, and proactive education. Mathematics assessments were quickly moved through various learning environments over the past year, showing that some assessment design strategies were no longer feasible if they instead centred on…
Descriptors: Mathematics Education, Integrity, Cheating, Foreign Countries
Mathematics Assessment Integrity during Lockdown: Experiences in Running Online Un-Invigilated Exams
Richardson, Steven – International Journal of Mathematical Education in Science and Technology, 2022
During the 2020 COVID-19 lockdowns, one of the primary challenges faced by mathematics educators was maintaining assessment integrity when replacing invigilated assessments with online assessments. These assessments presented students with the opportunity to engage in misconduct in a manner that ordinarily would not exist. This was particularly…
Descriptors: Mathematics Tests, Integrity, Cheating, COVID-19
The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level
Peng, Luyao; Sinharay, Sandip – Educational and Psychological Measurement, 2022
Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of…
Descriptors: Cheating, Identification, Statistical Analysis, Testing
Khan, Zeenath Reza – International Journal for Educational Integrity, 2022
When considering a paradigm shift in higher education, it is imperative to focus on removing obstacles against maintaining integrity in academia. One such obstacle is contract cheating sites that have mushroomed disproportionately during the 18 months of emergency distance learning threatening graduate quality and university reputations (McKie,…
Descriptors: Foreign Countries, Cheating, Web Sites, Identification
Conijn, Rianne; Kleingeld, Ad; Matzat, Uwe; Snijders, Chris – Journal of Computer Assisted Learning, 2022
Background: Online and blended learning need an appropriate assessment strategy which ensures academic integrity. During the pandemic, many universities have chosen for online proctoring. Although some earlier examples suggest that online proctoring may reduce cheating, the potential side-effects of proctoring are largely unknown. Objectives:…
Descriptors: Supervision, Computer Assisted Testing, Integrity, Cheating
Sinharay, Sandip; Johnson, Matthew S. – Journal of Educational and Behavioral Statistics, 2021
Score differencing is one of the six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to…
Descriptors: Probability, Bayesian Statistics, Cheating, Statistical Analysis
Ucar, Arzu; Dogan, Celal Deha – International Journal of Assessment Tools in Education, 2021
Distance learning has become a popular phenomenon across the world during the COVID-19 pandemic. This led to answer copying behavior among individuals. The cut point of the Kullback-Leibler Divergence (KL) method, one of the copy detecting methods, was calculated using the Youden Index, Cost-Benefit, and Min Score p-value approaches. Using the cut…
Descriptors: Cheating, Identification, Cutting Scores, Statistical Analysis
Elkhatat, Ahmed M.; Elsaid, Khaled; Almeer, Saeed – International Journal for Educational Integrity, 2021
One of the main goals of assignments in the academic environment is to assess the students' knowledge and mastery of a specific topic, and it is crucial to ensure that the work is original and has been solely made by the students to assess their competence acquisition. Therefore, Text-Matching Software Products (TMSPs) are used by academic…
Descriptors: Plagiarism, Identification, Assignments, Computer Software
Sinharay, Sandip; Johnson, Matthew S. – Grantee Submission, 2021
Score differencing is one of six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to…
Descriptors: Probability, Bayesian Statistics, Cheating, Statistical Analysis
Renuka Sharma; Kiran Mehta; Vishal Vyas – Journal of Education for Business, 2024
The propensity to cheat is intrinsic to every kind of education or training that requires effort and commitment. Academic dishonesty is a significant issue among secondary and postsecondary students worldwide. The majority of students have been involved in at least one kind of academic dishonesty in the preceding academic year. The fraud triangle…
Descriptors: Ethics, Cheating, Business Administration Education, Integrity
Yinxia Zhang – Higher Education: The International Journal of Higher Education Research, 2024
To inform interventions against academic cheating among college students, the study tests the moderating role of the construct of perceived behavioral control as originally proposed yet seldom tested in the Theory of Planned Behavior, and further tests the cultural boundary conditions for this moderating role with a focus on the four…
Descriptors: Cheating, Correlation, Individualism, Collectivism
Muammer Maral – Journal of Academic Ethics, 2024
This research aimed to identify patterns, intellectual structure, contributions, social interactions, gaps, and future research directions in the field of academic integrity (AI). A bibliometric analysis was conducted with 1406 publications covering the period 1966-2023. The results indicate that there has been significant growth in AI literature…
Descriptors: Integrity, Educational History, Cheating, Plagiarism
Alireza Maleki – Journal of Academic Ethics, 2024
The evaluation of students in online education poses a notable challenge, primarily due to the potential violation of academic integrity caused by various forms of cheating during online examinations. This study aims to explore the perspectives of English as a Foreign Language (EFL) learners on the reasons for online exam cheating. The study was…
Descriptors: English (Second Language), Second Language Learning, Distance Education, Online Courses
Mike Perkins; Jasper Roe; Binh H. Vu; Darius Postma; Don Hickerson; James McGaughran; Huy Q. Khuat – International Journal of Educational Technology in Higher Education, 2024
This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity…
Descriptors: Artificial Intelligence, Inclusion, Computer Software, Word Processing
Anna Filighera; Sebastian Ochs; Tim Steuer; Thomas Tregel – International Journal of Artificial Intelligence in Education, 2024
Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However,…
Descriptors: Cheating, Grading, Form Classes (Languages), Computer Software