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Peter Rowlett; Chris Graham; Christian Lawson-Perfect – International Journal of Mathematical Education in Science and Technology, 2025
Partially automated assessment is implemented via the 'Printable worksheet' mode in the Numbas e-assessment system to create a mathematical modelling worksheet which is individualised with random parameters but completed and marked as if it were a non-automated piece of coursework, preserving validity while reducing the risk of academic misconduct…
Descriptors: Automation, Worksheets, Mathematical Models, Computer Assisted Testing
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Sedigheh Karimpour; Ehsan Namaziandost; Hossein Kargar Behbahani – Journal of Educational Computing Research, 2025
As an integral part of dynamic assessment, computerized dynamic assessment (CDA) offers learners computer-assisted automated mediation. Accordingly, the possible efficacy of corrective feedback seems to be enhanced with new technologies, such as artificial intelligence tools, that offer automatic corrective feedback. Using technology-enhanced…
Descriptors: Computer Assisted Testing, Feedback (Response), Language Acquisition, Electronic Learning
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Beyza Aksu Dunya; Stefanie Wind – International Journal of Testing, 2025
We explored the practicality of relatively small item pools in the context of low-stakes Computer-Adaptive Testing (CAT), such as CAT procedures that might be used for quick diagnostic or screening exams. We used a basic CAT algorithm without content balancing and exposure control restrictions to reflect low stakes testing scenarios. We examined…
Descriptors: Item Banks, Adaptive Testing, Computer Assisted Testing, Achievement
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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
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Tyler M. Moore; Katherine C. Lopez; J. Cobb Scott; Jack C. Lennon; Akira Di Sandro; Eirini Zoupou; Alesandra Gorgone; Monica E. Calkins; Daniel H. Wolf; Joseph W. Kable; Kosha Ruparel; Raquel E. Gur; Ruben C. Gur – Journal of Psychoeducational Assessment, 2025
The Penn Computerized Neurocognitive Battery (CNB) is a collection of tests validated using neuroimaging, genetics, and other criteria. An updated version of the CNB was constructed in which all tests were converted to either computerized adaptive (CAT) or abbreviated forms. In a mixed community/clinical sample (N = 307; mean age = 25.9 years;…
Descriptors: Computer Assisted Testing, Cognitive Ability, Genetics, Adaptive Testing
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Zebo Xu; Prerit S. Mittal; Mohd. Mohsin Ahmed; Chandranath Adak; Zhenguang G. Cai – Reading and Writing: An Interdisciplinary Journal, 2025
The rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically…
Descriptors: Handwriting, Writing Skills, Chinese, Ideography
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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
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Francesca Patterson; Melina A. Kunar – Cognitive Research: Principles and Implications, 2024
Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems…
Descriptors: Cancer, Computer Use, Identification, Screening Tests
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Po-Chun Huang; Ying-Hong Chan; Ching-Yu Yang; Hung-Yuan Chen; Yao-Chung Fan – IEEE Transactions on Learning Technologies, 2024
Question generation (QG) task plays a crucial role in adaptive learning. While significant QG performance advancements are reported, the existing QG studies are still far from practical usage. One point that needs strengthening is to consider the generation of question group, which remains untouched. For forming a question group, intrafactors…
Descriptors: Automation, Test Items, Computer Assisted Testing, Test Construction
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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
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Ye Ma; Deborah J. Harris – Educational Measurement: Issues and Practice, 2025
Item position effect (IPE) refers to situations where an item performs differently when it is administered in different positions on a test. The majority of previous research studies have focused on investigating IPE under linear testing. There is a lack of IPE research under adaptive testing. In addition, the existence of IPE might violate Item…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Test Items
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Grochowalski, Joseph H.; Hendrickson, Amy – Journal of Educational Measurement, 2023
Test takers wishing to gain an unfair advantage often share answers with other test takers, either sharing all answers (a full key) or some (a partial key). Detecting key sharing during a tight testing window requires an efficient, easily interpretable, and rich form of analysis that is descriptive and inferential. We introduce a detection method…
Descriptors: Identification, Cooperative Learning, Cheating, Statistical Analysis
Leon Katcharian – ProQuest LLC, 2023
Remotely proctored online examinations proliferate in academic and corporate learning environments (Grajek, 2020). Remote (virtual) proctoring allows organizations to efficiently offer tests globally while reducing the costs of proctored testing generally associated with traditional paper-and-pencil and computer-based testing center examinations.…
Descriptors: Computer Assisted Testing, Supervision, Distance Education, Information Security
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Chen, Jennifer J.; Perez, ChareMone' – Childhood Education, 2023
Assessment holds the key to unlocking for the teacher a child's past (what he already knows), present (what he is learning), and future (what he still needs to learn) to inform teaching. Despite the benefits of assessment for informing teaching practice and enhancing student learning, it remains one of the most challenging and time-consuming tasks…
Descriptors: Evaluation Methods, Individualized Instruction, Artificial Intelligence, Computer Assisted Testing
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He, Yinhong; Qi, Yuanyuan – Journal of Educational Measurement, 2023
In multidimensional computerized adaptive testing (MCAT), item selection strategies are generally constructed based on responses, and they do not consider the response times required by items. This study constructed two new criteria (referred to as DT-inc and DT) for MCAT item selection by utilizing information from response times. The new designs…
Descriptors: Reaction Time, Adaptive Testing, Computer Assisted Testing, Test Items
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