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Liandi van den Berg – International Journal for Educational Integrity, 2025
Due to the coronavirus disease 2019 (COVID-2019) and the sudden shift to online learning, higher education institutions adopted various approaches to reduce cheating in online assessments, mainly involving online live proctoring (OLP). The international assessment integrity regulation trend also applied to a university in South Africa, where…
Descriptors: Foreign Countries, College Faculty, College Students, Computer Assisted Testing
Ebru Balta; Arzu Uçar – International Journal of Assessment Tools in Education, 2025
Unproctored Computerized Adaptive Testing (CAT) is gaining traction due to its convenience, flexibility, and scalability, particularly in high-stakes assessments. However, the lack of proctor can give rise to aberrant testing behavior. These behaviors can impair the validity of test scores. This paper explores the use of a verification test to…
Descriptors: Adaptive Testing, Computer Assisted Testing, Paper and Pencil Tests, Test Validity
Yang Zhen; Xiaoyan Zhu – Educational and Psychological Measurement, 2024
The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…
Descriptors: Artificial Intelligence, Models, Cheating, Identification
Brittland K. DeKorver; Mitchell Krahulik; Deborah G. Herrington – Journal of Chemical Education, 2023
The COVID-19 pandemic confronted chemistry instructors worldwide with many new challenges as they were quickly required to make the shift to remote instruction. One of these challenges was administering assessments in an online format. This was the topic of many discussions in the online support group Strategies for Teaching Chemistry (SFTC). This…
Descriptors: Chemistry, Science Teachers, Teacher Attitudes, COVID-19
Reddy, Leelakrishna; Letswalo, Machaba Leanyatsa; Sefage, Amanda Percy; Kheswa, Bonginkosi Vincent; Balakrishna, Avula; Changundega, Jesman Moreblessing; Mvelase, Mashinga Johannes; Kheswa, Khayelihle Allen; Majola, Siyabonga Ntokozo Thandoluhle; Mathe, Themba; Seakamela, Teffo; Nemakhavhani, Thendo Emmanuel – Pedagogical Research, 2022
Integrity and quality of assessments on the online platform should be upheld to ensure that it supports student learning as well as the efficacy of teaching because in the end it measures the reputation of an institution. How institutions have traversed such domains remains a grey area. This paper provides anecdotal insights into how staff from a…
Descriptors: Computer Assisted Testing, Cheating, Foreign Countries, College Faculty
Tobias Haug; Franz Holzknecht; Wolfgang Mann – Language Education & Assessment, 2024
This study investigated through an online survey how sign language practitioners changed their sign language assessment practices during the COVID-19 pandemic. The survey consisted of five sections and 29 questions overall. It was provided in written English and German as well as in International Sign and was administered online between October…
Descriptors: Sign Language, COVID-19, Pandemics, Evaluation
Kaiwen Man – Educational and Psychological Measurement, 2024
In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such…
Descriptors: Prior Learning, Testing, Behavior, Artificial Intelligence
Jinshui Wang; Shuguang Chen; Zhengyi Tang; Pengchen Lin; Yupeng Wang – Education and Information Technologies, 2025
Mastering SQL programming skills is fundamental in computer science education, and Online Judging Systems (OJS) play a critical role in automatically assessing SQL codes, improving the accuracy and efficiency of evaluations. However, these systems are vulnerable to manipulation by students who can submit "cheating codes" that pass the…
Descriptors: Programming, Computer Science Education, Cheating, Computer Assisted Testing
Meng, Huijuan; Ma, Ye – Educational Measurement: Issues and Practice, 2023
In recent years, machine learning (ML) techniques have received more attention in detecting aberrant test-taking behaviors due to advantages when compared to traditional data forensics methods. However, defining "True Test Cheaters" is challenging--different than other fraud detection tasks such as flagging forged bank checks or credit…
Descriptors: Artificial Intelligence, Cheating, Testing, Information Technology
Ebru Balta; Celal Deha Dogan – SAGE Open, 2024
As computer-based testing becomes more prevalent, the attention paid to response time (RT) in assessment practice and psychometric research correspondingly increases. This study explores the rate of Type I error in detecting preknowledge cheating behaviors, the power of the Kullback-Leibler (KL) divergence measure, and the L person fit statistic…
Descriptors: Cheating, Accuracy, Reaction Time, Computer Assisted Testing
Li Zhao; Junjie Peng; Shiqi Ke; Kang Lee – Educational Psychology Review, 2024
Unproctored and teacher-proctored exams have been widely used to prevent cheating at many universities worldwide. However, no empirical studies have directly compared their effectiveness in promoting academic integrity in actual exams. To address this significant gap, in four preregistered field studies, we examined the effectiveness of…
Descriptors: Supervision, Tests, Testing, Integrity
LaFlair, Geoffrey T.; Langenfeld, Thomas; Baig, Basim; Horie, André Kenji; Attali, Yigal; von Davier, Alina A. – Journal of Computer Assisted Learning, 2022
Background: Digital-first assessments leverage the affordances of technology in all elements of the assessment process--from design and development to score reporting and evaluation to create test taker-centric assessments. Objectives: The goal of this paper is to describe the engineering, machine learning, and psychometric processes and…
Descriptors: Computer Assisted Testing, Affordances, Scoring, Engineering
Henderson, Michael; Chung, Jennifer; Awdry, Rebecca; Ashford, Cliff; Bryant, Mike; Mundy, Matthew; Ryan, Kris – International Journal for Educational Integrity, 2023
Discussions around assessment integrity often focus on the exam conditions and the motivations and values of those who cheated in comparison with those who did not. We argue that discourse needs to move away from a binary representation of cheating. Instead, we propose that the conversation may be more productive and more impactful by focusing on…
Descriptors: College Students, Computer Assisted Testing, Cheating, Ambiguity (Semantics)
Mohamed Kara-Mohamed – Journal of Educational Technology Systems, 2025
(1) Context: The growing accessibility of Artificial Intelligence (AI) technology, such as ChatGPT, poses a challenge to the integrity of online assessments in higher education. As AI becomes more integrated into academic contexts, educators face the complex task of maintaining assessment standards particularly within modern Virtual Learning…
Descriptors: Artificial Intelligence, Virtual Classrooms, Computer Assisted Testing, Universities
Tiana P. Johnson-Clements; Guy J. Curtis; Joseph Clare – Journal of Academic Ethics, 2025
Concerns over students engaging in various forms of academic misconduct persist, especially with the post-COVID-19 rise in online learning and assessment. Research has demonstrated a clear role of the personality trait psychopathy in cheating, yet little is known about why this relationship exists. Building on the research by Curtis et al.…
Descriptors: Pandemics, COVID-19, Cheating, Electronic Learning

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