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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
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
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
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
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
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
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
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
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
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
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
Debarati Mukherjee; Supriya Bhavnani; Georgia Lockwood Estrin; Vaisnavi Rao; Jayashree Dasgupta; Hiba Irfan; Bhismadev Chakrabarti; Vikram Patel; Matthew K. Belmonte – Autism: The International Journal of Research and Practice, 2024
Current challenges in early identification of autism spectrum disorder lead to significant delays in starting interventions, thereby compromising outcomes. Digital tools can potentially address this barrier as they are accessible, can measure autism-relevant phenotypes and can be administered in children's natural environments by non-specialists.…
Descriptors: Autism Spectrum Disorders, Young Children, Evaluation, Technology
Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes

Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2024
Assessing students' answers and in particular natural language answers is a crucial challenge in the field of education. Advances in transformer-based models such as Large Language Models (LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks,…
Descriptors: Student Evaluation, Computer Assisted Testing, Artificial Intelligence, Comprehension
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