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Jiban Khadka; Dirgha Raj Joshi; Krishna Prasad Adhikari; Bishnu Khanal – Journal of Educators Online, 2025
This study aims to explore the impact of the fairness of semester-end e-assessment in terms of policy provision, monitoring, and authenticity. The cross-sectional online survey design was employed among 346 students at Nepal Open University (NOU). The results were analyzed by using t-test, analysis of variance, and structural equation modeling.…
Descriptors: Foreign Countries, College Students, Open Universities, Computer Assisted Testing
Yu Liu; Jing Zhang; Miranda May McIntyre; Gölge Seferoglu; Montgomery Van Wart – International Journal of Adult Education and Technology, 2025
This study investigates students' perceptions of rehearsal (test preparation) and testing after the pandemic forced increased online teaching use and experimentation. Data was gathered from information and decision sciences (IDS) students in an underrepresented minority (URM) serving university. Responses from 136 participants were analyzed and…
Descriptors: Test Preparation, Electronic Learning, Testing, Student Attitudes
K. Talman; J. Vierula; T. Karihtala; E. Laakkonen; J. Engblom; E. Haavisto – Higher Education Quarterly, 2025
Higher education institutions need to develop valid, fair, and objective selection methods. Current literature reporting the development and validation of new national large-scale selection tests is scarce. This two-phased study aimed to (1) develop and (2) evaluate the validity of the Finnish digital Universities of Applied Sciences Entrance…
Descriptors: Admission Criteria, Test Construction, Test Validity, Computer Assisted Testing
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)
Tay McEdwards; Greta R. Underhill – Online Journal of Distance Learning Administration, 2025
Online learning has steadily increased since well before the COVID-19 pandemic (Seaman et al., 2018), but research has yet to explore online students' perceptions of online exam proctoring methods. The purpose of this exploratory study was to understand the perceptions of fully online students regarding types of proctoring at a large state…
Descriptors: Supervision, Computer Assisted Testing, Electronic Learning, Student Attitudes
Gurvinder Kaur; Stephanie Stroever; Megh Gore; Bridget Vories; Vaughan H. Lee; Keith N. Bishop; Brandt L. Schneider – Discover Education, 2025
Background: Formative assessments build a positive learning environment and provide feedback to enhance learning. This study examined the impact of online formative and low-stake summative assessments on medical students' learning outcomes in the Clinically Oriented Anatomy course from 2016 to 2020. We aimed to demonstrate that formative…
Descriptors: At Risk Students, Identification, Prediction, Anatomy
Cleophas, Catherine; Hönnige, Christoph; Meisel, Frank; Meyer, Philipp – INFORMS Transactions on Education, 2023
As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams'…
Descriptors: Computer Assisted Testing, Cheating, Identification, Essay Tests
Meljun Barnayha; Gamaliel Gonzales; Rachel Lavador; Jessamae Martel; Ma. Kathleen Urot; Roselyn Gonzales – Psychology in the Schools, 2025
This study examines the determinants of online academic dishonesty using the theory of planned behavior. We surveyed 1087 college students in Central Philippines and utilized a partial least squares-structural equation modeling analysis to evaluate a proposed model. Results demonstrate that 10 of the 11 hypothesized relationships are statistically…
Descriptors: Self Control, Cheating, Intervention, Ethics
Jonas Flodén – British Educational Research Journal, 2025
This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three…
Descriptors: College Faculty, Artificial Intelligence, Comparative Testing, Scoring
Yusuf Oc; Hela Hassen – Marketing Education Review, 2025
Driven by technological innovations, continuous digital expansion has transformed fundamentally the landscape of modern higher education, leading to discussions about evaluation techniques. The emergence of generative artificial intelligence raises questions about reliability and academic honesty regarding multiple-choice assessments in online…
Descriptors: Higher Education, Multiple Choice Tests, Computer Assisted Testing, Electronic Learning
Maria Townsend; Emma Champion; Gwendoline Berndt – Open Learning, 2025
Standard online tutorials at The Open University, based in the UK, are generally tutor-led with the tutor setting the agenda and content. This study investigated the inclusion of online student-led drop-in tutorials to support assessment on an OU level one module. The aims were to gauge the value to students of this style of tutorial, to…
Descriptors: Electronic Learning, Open Education, Tutorial Programs, Computer Assisted Testing
Daniel Domínguez-Figaredo; Inés Gil-Jaurena – Distance Education, 2025
This study analyses changes in students' perceptions of online examinations during the transition from a face-to-face to a fully online assessment system. We compare data from a survey administered to two samples of students at a distance learning university at the end of two consecutive academic years in which a new online examination system was…
Descriptors: Computer Assisted Testing, Student Attitudes, Attitude Change, College Students
Shengnan Han; Shahrokh Nikou; Workneh Yilma Ayele – International Journal of Educational Management, 2024
Purpose: To improve the academic integrity of online examinations, digital proctoring systems have recently been implemented in higher education institutions (HEIs). The paper aims to understand how digital proctoring has been practised in higher education (HE) and proposes future research directions for studying digital proctoring in HE.…
Descriptors: Computer Assisted Testing, Supervision, Higher Education, Cheating
Yazid Meftah Ali Wahas; Akbar Joseph A. Syed – Education and Information Technologies, 2024
Technology has become a fundamental means to encourage reliable and more effective assessments. Rapid technological developments have led to the widespread use of digital platforms and devices in all aspects of life. Educational institutions worldwide had to take advantage of this technological leap during pandemics such as COVID-19, which changed…
Descriptors: Educational Technology, Technology Uses in Education, Computer Assisted Testing, Barriers
David Eubanks; Scott A. Moore – Assessment Update, 2025
Assessment and institutional research offices have too much data and too little time. Standard reporting often crowds out opportunities for innovative research. Fortunately, advancements in data science now offer a clear solution. It is equal parts technique and philosophy. The first and easiest step is to modernize data work. This column…
Descriptors: Higher Education, Educational Assessment, Data Science, Research Methodology